ArticlePDF Available

Abstract and Figures

In general, the applications of robots have shifted rapidly from industrial uses to social uses. This provides robots with the ability to naturally interact with human beings and socially fit into the human environment. The deployment of social robots in the healthcare system is becoming extensive as a result of the shortage of healthcare professionals, rising costs of healthcare and the exponential growth in the number of vulnerable populations such as the sick, the aged and children with developmental disabilities. Consequently, social robots are used in healthcare for providing health education and entertainment for patients in the hospital and for providing aids for the sick and aged. They are also used for dispensing drugs and providing rehabilitation as well as emotional and aging care. Hence, social robots improve the efficiency and quality of healthcare services. The interaction between social robots and human beings is known as human-robot interaction. Human-robot interaction in healthcare is faced with numerous challenges such as the fear of displacement of caregivers by robots, safety, usefulness, acceptability as well as appropriateness. These challenges ultimately lead to a low rate of acceptance of the robotic technology. Consequently, this paper extensively appraises human-robot interaction in healthcare, their applications and challenges. Design, ethical and usability issues such as privacy, trust, safety, users' attitude, culture, robot morphology as well as emotions and deception arising from the interaction between humans and robots in healthcare are also reviewed in this paper.
Content may be subject to copyright.
I.J. Information Engineering and Electronic Business, 2017, 3, 43-55
Published Online May 2017 in MECS (http://www.mecs-press.org/)
DOI: 10.5815/ijieeb.2017.03.06
Copyright © 2017 MECS I.J. Information Engineering and Electronic Business, 2017, 3, 43-55
State Of The Art: A Study of Human-Robot
Interaction in Healthcare
Iroju Olaronke
Department of Computer Science, Adeyemi College of Education, Ondo, Nigeria
Email: irojuolaronke@gmail.com
Ojerinde Oluwaseun and Ikono Rhoda
Department of Computer Science, Federal University of Technology, Minna, Nigeria
Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
Email: {o.ojerinde@futminna.edu.ng, rhoda_u@yahoo.com}
AbstractIn general, the applications of robots have
shifted rapidly from industrial uses to social uses. This
provides robots with the ability to naturally interact with
human beings and socially fit into the human
environment. The deployment of social robots in the
healthcare system is becoming extensive as a result of the
shortage of healthcare professionals, rising costs of
healthcare and the exponential growth in the number of
vulnerable populations such as the sick, the aged and
children with developmental disabilities. Consequently,
social robots are used in healthcare for providing health
education and entertainment for patients in the hospital
and for providing aids for the sick and aged. They are
also used for dispensing drugs and providing
rehabilitation as well as emotional and aging care. Hence,
social robots improve the efficiency and quality of
healthcare services. The interaction between social robots
and human beings is known as human-robot interaction.
Human-robot interaction in healthcare is faced with
numerous challenges such as the fear of displacement of
caregivers by robots, safety, usefulness, acceptability as
well as appropriateness. These challenges ultimately lead
to a low rate of acceptance of the robotic technology.
Consequently, this paper extensively appraises human-
robot interaction in healthcare, their applications and
challenges. Design, ethical and usability issues such as
privacy, trust, safety, users‘ attitude, culture, robot
morphology as well as emotions and deception arising
from the interaction between humans and robots in
healthcare are also reviewed in this paper.
Index TermsArtificial Intelligence, Healthcare,
Human-robot interaction, Robots, Social Robots.
I. INTRODUCTION
The rising cost of healthcare, the exponential growth of
vulnerable population such as the sick and the aged and
the shortage of qualified healthcare professionals in
recent times has led to logical alternatives of providing
healthcare services to patients [1]. One of the major
means of providing alternative care is the use of social
robots which is fast becoming prevalent as a result of the
advancement in robotic technology and Information and
Communication Technology (ICT). Social robots are
used in healthcare to provide assistive health technology
such as aids for the blind, robot wheelchairs and walkers.
They are used to rehabilitate the aged and the infirm; they
provide remote surgical operations and also dispense oral
drugs in pharmaceutical settings [2]. In addition, social
robotic systems are used to imitate the cognition of
human beings and animals in order to provide companion
for the aged. This mitigates boredom, isolation and
depression and facilitates the quick recovery of the sick
[3]. Social robots possess the ability to stimulate the
development of new treatments for different diseases,
improve the accessibility to care and also increase
independent living. Consequently, social robots improve
the quality of healthcare services and patient health
outcomes.
One of the major characteristics of a social robot is its
ability to naturally interact with human beings and
socially fit into the human environment. Furthermore,
social robots are autonomous in nature and they have the
ability to establish and maintain social relationships with
their users [4]. Therefore a social robot according to
Weiss and Evers [5] is an embodied intelligent agent
which is specifically designed for social interaction with
human beings. The interdisciplinary study of the dynamic
interaction between human beings and social robots is
referred to as human-robot interaction (HRI) [6]. Human-
robot interaction is an emerging field which is
multidisciplinary in nature. It is a branch of Computer
Science which draws its research links from the field of
Artificial Intelligence majorly in Human-Computer
Interaction (HCI), Robotic Engineering, Natural
Language Processing, and Computer Vision. It is also
related to Electrical, Mechanical, Industrial and Design
Engineering. Human-robot interaction is as well rooted in
Social Sciences majorly in the fields of Human Factors,
Psychology, Cognitive Science, Communications,
Sociology and Anthropology. It is also associated with
Ethology, Ethics, Linguistics and Philosophy in
Humanities.
One of the major challenges confronting human-robot
interaction in healthcare is the loss of privacy. This is
44 State Of The Art: A Study of Human-Robot Interaction in Healthcare
Copyright © 2017 MECS I.J. Information Engineering and Electronic Business, 2017, 3, 43-55
because social robots are mobile, they act as social actors
and they also have the ability to gather data [7]. Other
issues affecting human-robot interaction in healthcare
include lack of trust, low user acceptance, emotions and
deception. The interaction between humans and social
robots within the healthcare system is also faced with the
challenge of patients safety. For instance, during human-
robot interaction, the human is considered as an integral
part of a closed-loop feedback system which exchange
information and energy with the robot system
simultaneously [8]. At this point, too much energy may
be transferred by the robot to the human/patient and this
might result in severe injury [9]. In addition, the presence
of sharp edges in the mechanical design of a social robot
increases lacerations which can also cause severe
damages to humans [9]. Thus, the social and ethical
implications of a social robot must be considered during
the design of social robots in healthcare. Hence, this
study examines the general overview of robots and the
types of robots depending on the type of tasks that they
perform. Consequently, industrial robots, service robots
and social robots are critically examined in this paper.
This study also examines human-robot interaction in
healthcare, its applications as well as its challenges.
Ethical, design and usability issues associated with the
interaction between humans and robots in healthcare are
also considered in this study.
This paper is organized into seven sections. Section
two deals with the general overview of robots, section
three examines human-robot interaction, section four
appraises human-robot interaction in healthcare, section
five examines the challenges of human-robot interaction
in healthcare, section six suggests ways of enhancing
effective human-robot interaction in healthcare while
section seven concludes the study.
II. GENERAL OVERVIEW OF ROBOTS
The term robot originated from the Czech word
―robota‖ which denotes forced labor. The word robot was
coined by a Czech novelist named Karel Capek [10].
Capek used the word robot in a 1921 play titled Rossum‘s
Universal Robots (R.U.R.). In R.U.R, robots were man-
made beings that were created to work for people. Thus,
human beings have used robots to perform different tasks
since ancient times. However, there is no standard
definition for robots. Nevertheless, the Robot Institute of
America [11] defines a robot as a reprogrammable,
multifunctional manipulator designed to move material
parts, tools or specialized devices through variable
programmed motions for the performance of a variety of
tasks. Re-programmability in this definition distinguishes
robots from other automatic machines [12]. Davison [13]
also views a robot as a physically-embodied, artificially
intelligent device that has the ability to sense and actuate.
Furthermore, Hegel et al. [14] defines a robot as a
programmed physical entity that perceives and acts
autonomously within a physical environment which has
an influence on its behavior. In contrast to Hegel et al.
[14] definition, a robot can be fully controlled by a
human being, that is, teleoperated. From the definitions
above, a system or device is considered a robot if it
possesses the following features:
Sensing: A robot has the ability to sense its
environment. A robot should be able to react and
adapt to changing conditions in its environment. A
robot should also be able to detect objects or
features in its environment.
Movement: A robot must possess the ability to
move in its environment. This could be done by
rolling on wheels, walking with legs or propelling
by thrusters.
Energy: A robot must have a source of power
such as electrical or solar power.
Intelligence: A robot must be cognitive in nature
by possessing the capability to reason, plan,
navigate and manipulate in its environment. A
robot also possesses the ability to be easily
programmable so that it can perform its tasks.
Shape: A robot must have a shape, frame or form
that is required to carry out a specific task.
A robot can therefore explicitly be defined as a
reprogrammable, physically embodied, intelligent and
mobile system that is energy driven and has the ability to
act autonomously or be teleoperated in an environment
which it has the capability to sense. A robot consists of
seven basic components irrespective of its shape and size.
These components work together to perform a specific
task. The components of a robot include the following:
Controller: The controller coordinates the
movement of the robot. The region of space a
robot can reach is called the working envelope.
The controller is also responsible for receiving
input from the environment through its sensors.
Power conversion unit: The power source
provides energy to drive the robot‘s controller.
The most common sources of power in robotic
systems include electric power, compressed gases,
solar power and hydraulics. The power supplied is
usually converted from alternating current (ac) to
direct current (dc) in the power conversion unit.
Manipulator: Robots have the capability to
manipulate objects by picking up objects,
modifying objects as well as destroying objects.
The manipulator is the mechanical handling device
of the robot which emulates the arm of a human
being. The manipulator consists of a set of rigid
links connected by joints which are usually
referred to as the shoulder, elbow and wrist. The
joints are usually rotary or sliding in nature. The
arrangement of the joints which determines the
possible motion of the robot is referred to as
kinematics.
End Effector: The last link of the manipulator is
called the end effector. The effector is defined as a
link that is used to grip a tool. The end effector
emulates the human hand.
State Of The Art: A Study of Human-Robot Interaction in Healthcare 45
Copyright © 2017 MECS I.J. Information Engineering and Electronic Business, 2017, 3, 43-55
Sensors: The sensors allow robots to receive
information about a certain measurement of the
environment. This is usually done to ensure the
safety of the robot. Sensors allow a robot to act on
changes in the environment.
Actuator: The actuator is usually referred to as
the muscle of the robot. It converts the power
supplied into the robot‘s movement.
Control and Task Program: The control
program is a set of instructions provided by the
manufacturer of the robot to control the robot‘s
manipulator while the task program is a set of
instructions usually provided by the user. The task
program specifies the motion that the manipulator
needs to complete a specific task.
Hence, Qureshi and Syed [15] view a robot as a system
that contains sensors, control systems, manipulators,
power supplies, and software that work together to
perform a specific task. The components of a robot are as
illustrated in Fig. 1.
Fig.1. Components of a robot [11]
Robots can be classified into different categories
depending on the task that they perform. Based on this,
the International Federation of Robots classified robots
into two basic classes. These include the industrial robots
and the service robot [16].
A. Industrial Robots
An industrial robot according to the International
Federation of Robots is an automatically controlled,
reprogrammable, multipurpose manipulator that is
programmable in three or more axes which may be either
fixed or mobile, and designed for use in industrial
automation applications [17]. Industrial robots are usually
used in manufacturing industries to perform tasks that are
too cumbersome or too dangerous for human beings.
They are mostly found in automobile industries where
they are used to perform repetitive and predictable tasks.
The applications of industrial robots typically include
painting, welding, assembling, picking and placing,
palletizing, product inspection and testing. Most
industrial robots are robot arms or manipulators whose
function is to position an end-effector through which it
interacts with its environment. However, industrial robots
are designed for environments where the presence of
human beings is limited [18].
B. Service Robots
Engelhardt [19] views a service robot as a system that
functions as a smart, programmable tool that can sense,
think, and act to benefit or enable humans to
extend/enhance human productivity. In addition, the
International Federation of Robots defines a service robot
as a robot which operates semi autonomously or fully
autonomously to perform tasks that are useful to the well
being of human beings and equipment, excluding
manufacturing operations [17]. It is clear from the above
definitions that service robots are not used for
manufacturing purposes and they do not interact with
human beings. Conversely, a third class of robots was
established. This special type of robot known as social
robot was specifically designed to interact socially with
human beings and other robots in order to support a
human-like interaction.
C. Social Robots
A social robot according to Bartneck and Forlizzi [20]
is an autonomous or semi-autonomous robot that interacts
with human beings by following the behavioral norms
expected by the people with whom the robot is intended
to interact. However, Breazeal [21] argues that social
robots interact with both human beings and other robots.
Fong et al. [22] also corroborated Breazeal [21] by
defining social robots as embodied agents that are part of
a heterogeneous group of a society of robots or humans.
However, for a robot to be considered social it must
possess the following characteristics:
Social Interaction: A social robot engages in
social interactions by explicitly communicating
with human beings within the social rules attached
to its roles. Hence, Fong et al. [22] emphasized
that social robots have the ability to establish and
maintain social relationships with human beings
using natural cues such as gaze and gestures.
Breazeal [21] stressed that human beings should
be able to understand a social robot, relate with it
and also empathize with it. Social robots also have
the ability to express and perceive emotions.
Hence, social robots exhibit personality, traits and
character.
Anthropomorphism: Anthropomorphism is a
phenomenon that describes the tendency of
humans to see human-like shapes in an
environment [23]. It is a process of attributing
human like qualities and personal characteristics to
entities in an environment. According to Graaf et
al. [4], social robots can be designed to possess
life-like qualities in order to enhance their
interactions with human beings. Robots that take
the shape and possess the qualities of humans are
referred as humanoid robots.
Social learning and imitation: A social robot can
easily adapt, perceive, recognize and learn new
behaviors or skills by imitation through natural or
intuitive means [18]. Behaviors can be verbal or
non-verbal. Examples of non-verbal behaviors
include gestures and gazing.
Power Conversion
Unit
Controller
Manipulator
Sensor
Actuator
Control and
Task program
46 State Of The Art: A Study of Human-Robot Interaction in Healthcare
Copyright © 2017 MECS I.J. Information Engineering and Electronic Business, 2017, 3, 43-55
One of the basic requirements that manage the
behavior of social robots is the Isaac Asimov three laws
of robots [24]. These laws are highlighted below.
A robot may not injure a human being or through
inaction allow a human being to come to harm.
A robot must obey orders given to it by human
beings except where such orders would conflict
with the first law.
A robot must protect its own existence as long as
such protection does not conflict with the first or
second law.
These three laws prove that the closer a human gets
with a robot, the more complicated their relationship
becomes and the risk of the human getting injured is
heightened. Hence, safe zones must be defined during
human-robot interaction. The basic consequence of these
laws however is that robots that interact with other robots
are not considered social robots. This is however against
the definitions of Breazeal [21] and Fong et al. [22]
which argued that social robots interact with both humans
and other robots. However, Vincent et al. [25] is of the
view that a robot will only be considered social if it
interacts with humans within the social values, norms and
standards of a society. Consequently, Vincent et al. [25]
view robots as culturally dependent since social values,
norms and standards differ amongst cultures.
III. HUMAN-ROBOT INTERACTION
There are diverse definitions for human-robot
interaction. For instance, Goodrich and Schultz [26]
define human-robot interaction (HRI) as a field of study
that is dedicated to understanding, designing, and
evaluating robotic systems for use by or with humans.
Interaction in this definition according to Goodrich and
Schultz [26] requires communication between robots and
humans. In addition, Feil-Seifer and Mataric [6] view
HRI as an interdisciplinary study of the dynamic
interaction between human beings and robots. Interaction
in this definition refers to the process of working together
to achieve a common goal. HRI focuses on the study of
the functionality and the usability of robots when
performing tasks that involve human beings [27]. There
are diverse means in which a human and a robot interact.
These include the use of visual displays such as graphical
user interfaces or augmented reality interfaces, gaze and
gestures such as hand and facial movements, speech,
natural languages, physical interaction and haptics [26].
HRI is therefore focused on making the interactions
between robots and human beings as natural as possible.
However, it is important to note that the social interaction
between humans and robots is not limited to one human
and one robot. The interactions between humans and
robots in HRI can also take the form of one human-robot
team, one human-multiple robots, human team-one robot,
multiple humans-one robot, human team-robot team,
human team-multiple robots, multiple humans-robot team
[28]. A team in this regard refers to a group of humans or
robots working together to achieve a common goal. The
robot team could however contain different types of
robots or the same type of robot. The form of interactions
between humans and robots is as illustrated in Fig. 2.
According to Goodrich and Schultz [26], the
interaction between a human and robot are categorized
into two. These include remote interaction and proximate
interaction.
Remote interaction: As the name implies, in
remote interaction the human and the robot are not
located in the same geographical location. The
humans and the robots may be separated spatially
or temporally.
Proximate interaction: In proximate interaction,
the humans and the robots are in the same location
or environment. This implies that the humans and
robots are collocated.
Fig.2. Forms of interactions in HRI [28]
R
R
R
One human
One robot
One human
Robot team
One human
Multiple robots
Human team
One robot
H
R
Multiple humans
One robot
Human Team
Robot Team
Human Team
Multiple Robots
Human Team
Robot Team
State Of The Art: A Study of Human-Robot Interaction in Healthcare 47
Copyright © 2017 MECS I.J. Information Engineering and Electronic Business, 2017, 3, 43-55
According to Yanco and Drury [28], there are different
types of interaction roles in HRI. These include:
Supervisor: A supervisor role involves
monitoring, controlling and evaluating a task that
is required to be performed by a robot. Hence, the
supervisor controls the behavior of the robot. The
supervisor can manage more than one robot. The
human usually performs the role of a supervisor in
HRI.
Operator: The operator is responsible for
knowing where a robot is and what the robot is
doing at any given time. The operator also has the
knowledge of the robot‘s health and environment.
The operator is also tasked with the responsibility
of modifying the robot‘s behavior to a suitable one.
The person who is responsible for remotely
operating and controlling a robot is known as the
Wizard-of-Oz (WoZ).
Mechanic: The mechanic plays the role of the
programmer. The mechanic is responsible for
changing the robots‘ hardware or software.
Peer/Team: The word peer refers to teammates.
Hence, a robot as well as a human can be a
member of a team. Both the human and the robot
in the team work together to achieve a common
objective. Interactions between the robot and the
human at this point could be through gestures,
gaze and voice.
Bystander: The role of the bystander is to coexist
in the same environment with a robot without
necessarily interacting with the robot. However,
the bystander is required to have a little knowledge
of the robot‘s behavior so as to understand the
consequences of the robot‘s action.
Hence, both humans and robots must have the
knowledge of one another. This process is referred to as
Human-Robot Interaction Awareness. Formally, Drury et
al. [29] defines HRI awareness as the understanding that
the human has of a robots location, activities, status, and
surroundings; the knowledge that the robot has of the
human‘s commands or instructions that are necessary to
direct its activities and the knowledge of the conditions
and constraints under which the robot must operate.
However, the lack of awareness significantly reduces the
level of interaction between a human and a robot and thus
the performance of the overall task required to be carried
by both the human and the robot is greatly reduced [30].
According to Drury et al. [29], there are five types of HRI
awareness. These include the following:
Human-robot Awareness: In human-robot
awareness, the human have the knowledge of the
robots‘ locations, surroundings, identities,
activities and status.
Human-human Awareness: In human-human
awareness, the humans have the knowledge of the
locations, surroundings, identities, activities and
status of their fellow human collaborators.
Robot-human Awareness: In robot-human
awareness, the robots have the knowledge of the
humans‘ instructions needed to perform a specific
task.
Robot-robot Awareness: In robot-robot
awareness, the robots‘ have the knowledge of the
instructions given to them by other robots.
Humans’ overall mission Awareness: In
humans‘ overall mission awareness, the human
beings have the knowledge of the overall goal of
the mutual activities carried out by the humans and
the robots.
There are five basic taxonomies of interactions
between humans and robots [26]. These taxonomies
include autonomy, the nature of information exchange,
the structure of the team, adaptation, learning, and
training of people and the robot as well as the shape of
the task.
Autonomy: Autonomy can simply be described as
the ability of a robot to carry out a task
independently. Four levels of autonomy have been
identified by the Department of Defense, United
States of America [17]. These include human
operated, human delegated, human supervised and
fully autonomous. In human operated autonomy,
the robot has no autonomous control of its
environment while the human operator makes all
the decisions. In human delegated autonomy, the
robot performs diverse tasks which are delegated
to it, independent of human control. In human
supervised autonomy, the robot performs diverse
tasks when directed by humans while in fully
autonomous autonomy, the robot receives goals
from the human and translates them into tasks to
be performed without requiring human interaction,
although the human can still change the goal in
this process.
Nature of Information Exchange: The nature of
information exchange in this context refers to the
way or manner in which humans and robots
exchange information. Information is usually
exchanged between humans and robots using
different types of communication media which are
characterized by the senses of hearing, touch and
seeing [26]. Typical media of communication used
for information exchange between humans and
robots include gestures, such as hand and facial
movements, speech, natural languages and visual
displays such as graphical user interfaces or
augmented reality interfaces.
The structure of the team: This can simply be
described as the number, the composition and the
organization of humans and robots that are
working together to perform a particular task
Adaptation, Learning and Training: This simply
means that a robot has the ability to be trained or
taught a particular behavior and it also has the
ability to learn new behaviors by natural or
48 State Of The Art: A Study of Human-Robot Interaction in Healthcare
Copyright © 2017 MECS I.J. Information Engineering and Electronic Business, 2017, 3, 43-55
intuitive means. A robot also possesses the ability
to adapt to changes in its environment. In HRI,
humans who are relatively inexperienced in a
particular task can also trained.
Shape of the Task: The shape of the task is a term
that refers to how a particular task is carried out.
HRI is a wide research area which has been applied in
several areas such as entertainment, military, search and
rescue missions, education, communication and
healthcare.
IV. HUMAN-ROBOT INTERACTION IN HEALTHCARE
The use of robots is swiftly shifting from industrial
uses where they are basically deployed for manufacturing
purposes and tasks that are too dangerous for human
beings to the use of social robots which have the
capability to interact with human beings in a particular
environment. Social robots have been widely deployed in
healthcare in recent times as a result of low accessibility
to healthcare services. For instance, in the United States
of America, 28.6 million Americans (about 9.1% of her
population) were uninsured in 2015 [31]. One of the
major factors responsible for this is cost. Hence, these set
of people were hindered from receiving healthcare. This
may however lead to loss of lives. Nonetheless, social
robots are now being designed to provide affordable
home based, personalized and telemedicine technologies
for preventive and curative care. In addition, the upsurge
in population as well as the shortage in the number of
qualified health workers has also necessitated the need
for social robots in healthcare. Hence, researches in HRI
in healthcare has resulted in the design of social robots
that served as companions to patients, provide support for
the aged and the sick and serve as assistive aids or
mobility assistance to the visually impaired or people
with mobility challenges [32]. Studies in HRI in
healthcare have also brought about the design of social
robots that provide therapy for autistic children in order
to improve their social interactions [33]. Consequently,
HRI has the capability to improve the quality and
accessibility to healthcare services which in turn
increases patients‘ health outcome.
HRI in healthcare is primarily concerned with helping
patients improve or monitor their health. Social robots in
healthcare have been classified as surgical robots,
rehabilitation robots, behavioral therapy robots,
companion robots, assistive and supportive robots,
physician surrogate, telepresence robots, biorobots, and
vital signs monitoring robot.
A. Surgical Assistance Robots
Surgical assistance robots are robots that allow
physicians to perform surgical operations with greater
precision. Surgical robots support both face-to-face and
remote surgical operations. In face-to-face surgical
operations, the physicians and patients are physically
present while the human surgeon is not physically present
with the patients in remote surgical operations. The use of
surgical assistance robots results in minimally invasive
surgeries. Surgical assistive robots have been used in
urology for prostate cancer [17]. Advantages of surgical
robots according to Kefee [17] include increased
precision of surgical manipulation, improved vision due
to magnification, a more controlled, comfortable and
safer environment as well as better ergonometric for the
operator. A typical example of a surgical robot is the Da
Vinci surgical system. The Da Vinci surgical system is a
teleoperated and telepresence system which consists of an
end-effector with surgical instruments. Da Vinci surgical
system was designed to emulate a human like wrist with
greater flexibility in order to assist surgeons to perform
delicate and complicated operations. It is however worthy
to note that the Da Vinci surgical system has performed
more than 20,000 surgeries [15]. Fig. 3 shows the picture
of Da Vinci system. Another type of a surgical robot is
the Magnetic Microbots which have been used for
removing plaques from a patient‘s arteries.
Fig.3. Da Vinci system [17]
B. Rehabilitation Robots
Rehabilitation robots according to Van der Loos and
Reinkensmeyer [2] are robots that assist people with
disability and provide therapy for people seeking to
improve physical or cognitive functions. According to
Van der Loos and Reinkensmeyer [2], disability means a
physical or mental impairment that substantially limits
one or more of major life activities. Van der Loos and
Reinkensmeyer [2] categorized rehabilitation robots as
assistive robots for mobility, assistive robots for
manipulation and therapy robots.
Assistive robots for mobility: These are robots
that assist people with motor impairments such as
difficulty in vision and walking. Examples of
assistive robots for mobility include robotic
wheelchairs, intelligent wheel chairs, robotic
walkers and robotic aids for the blind. Assistive
robots improve mobility and navigation and also
prevent collision and falls. A typical example of
an assistive robot used in healthcare is Pearl. Pearl
is a mobile robot system developed as a part of the
Nursebot project at Carnegie Mellon University. It
is designed to assist elderly people in navigating
their daily activities in their environment and also
to remind them to take their medications [33].
Pearl is equipped with sonar sensors, microphones
State Of The Art: A Study of Human-Robot Interaction in Healthcare 49
Copyright © 2017 MECS I.J. Information Engineering and Electronic Business, 2017, 3, 43-55
for speech recognition, speakers for speech
synthesis, touch-sensitive graphical displays,
actuated head units, and stereo camera systems
[33]. Pearl supports telepresence which allows
physicians to interact with remote patients.
Another example of an assistive robot in
healthcare is Robear. Robear is a giant gentle bear
with a cartoonish head. Robear has the capability
of lifting and transferring patients with mobility
problems. Robear can also help patients to stand
and turn in bed so as to prevent bed sores. Fig. 4
shows Robear assisting medical practitioners to lift
a patient unto a bed.
Fig.4. Robear lifting a patient unto a bed [17]
Assistive robots for manipulation: Assistive
robots for manipulation are used for handling
physical objects. They are usually used by people
with impairments of the arm, hand and fingers.
Examples of assistive robots for manipulation
include the MANUS robot arm, ISAC robot and
Bestic Arm. Bestic arm is designed for lifting food
from the plate to the mouth [34]. Handy 1 robot is
also a manipulation robot which is used for
assisting disabled people with eating and drinking
[35]. Fig. 5 shows the picture of Bestic arm.
Fig.5. Bestic arm lifting food from a plate [17]
Therapy Robots: Therapy robots are robots that
provide treatment for people with physical and
mental challenges. For instance, researches have
shown that people suffering from Autism
Spectrum Disorders (ASD) responded to
treatments involving robotic technology than
treatments from human therapists [36]. A typical
example of a therapy robot is Paro. Paro was
developed by the Intelligent Systems Research
Institute (ISRI) of the National Institute of
Advanced Industrial Science and Technology
(AIST) in Japan. Paro resembles a baby harp seal.
Paro is covered with soft artificial fur and it has
been used as a substitute for animal therapy [37].
Paro is not mobile; but it has been used for the
treatment of patients with dementia [3]. According
to Riek [37], therapy using Paro include patients‘
holding, hugging, stroking, or talking to Paro as
they would an actual animal or baby. Thus, Paro
improved patients‘ social interaction and moods
which improves patient‘s health outcome. Autom,
the weight loss coach is also a therapy robot that
helps people to reduce their weight by
encouraging diet adherence and exercise [38].
Pepper, a four feet humanoid robot is also a
therapy robot developed by Softbank to improve
the mental engagement of humans by responding
to their emotions.
C. Companion Robots
Companion robots are typically designed to enhance
the health and psychological well-being of the aged and
the sick by providing companionship, alleviating stress
and increasing their immune system. Companion robots
have been used to increase the quality of life and the life
span of the aged who usually lack human care and
support in the society. Hence, companion robots mitigate
helplessness, boredom, isolation, depression and
loneliness [3]. Paro has also been used as a companion
robot. In addition, AIBO, a metallic doglike robot has
been used as a companion robot in nursing homes. A
study conducted by Banks et al. [39] showed that AIBO
reduces stress hormones and also improve the brain
functioning of patients.
D. Entertainment Robots
These are robots that improve the health and well-
being of patients by entertaining them. Examples of
entertainment provided by these types of robots include
games, music and video. A typical example of an
entertainment robot used in healthcare is the Guide robot
manufactured by ED Robotics Company in Seoul, Korea
[3]. The Guide robot also takes the vital signs of its users.
Guide robot interacts with its users by speaking,
displaying messages, images, videos and texts on a touch
screen. It also accepts users‘ input on the touch screen [3].
E. Telepresent Robots
A telepresent robot is a form of telemedical robot that
allows healthcare professionals that are offsite or in
remote locations to participate in the care of a patient.
Telepresent robots are however non-autonomous in
nature and they are basically designed to facilitate remote
communication as well as the timely treatment of patients.
Telepresent robots are typically used for guiding therapy
from remote locations. A typical example of a telepresent
robot is Dr. Robot. Dr. Robot according to Kefee [17]
allows remote neurologists to provide special care for
patients with acute stroke in an emergency room. Another
example of a telepresent robot is the RP-VITA robot
developed by In touch health and iRobot. RP-VITA robot
is designed to collect patients‘ data and disseminates it to
50 State Of The Art: A Study of Human-Robot Interaction in Healthcare
Copyright © 2017 MECS I.J. Information Engineering and Electronic Business, 2017, 3, 43-55
designated healthcare professionals. However, whenever
there is an abnormality in the data collected, RP-VITA
robot notifies the healthcare professionals through alerts
[17].
Table 1 shows a comparative analysis of social robots
in healthcare.
Table 1. A Comparative Analysis of Some Social Robots in Healthcare
Healthcare
Robot
Types of
Interaction
Level of
Autonomy
Interaction
Roles
Da Vinci
Surgical
System
Proximate
and Remote
Non-
Autonomous
Human is
Supervisor
and Operator
Humans and
Robot are
Team
Robotic
Wheelchair
Proximate
Non-
Autonomous
Humans and
Robots are
Team
Pearl
Proximate
and Remote
Non-
Autonomous
Human is
Supervisor
Humans and
Robot are
Team
Paro
Proximate
Non-
Autonomous
Humans and
Robots are
Team
Guide
Proximate
Non-
Autonomous
Humans and
Robots are
Team
Autom
Proximate
Non-
Autonomous
Humans and
Robots are
Team
V. CHALLENGES OF HUMAN-ROBOT INTERACTION IN
HEALTHCARE
Social robots are now widely used in healthcare. Their
applications range from surgery, emotional and aging
care, companionship to telemedicine and rehabilitation.
However, there are numerous challenges associated with
the interaction between humans and social robots in
healthcare. These challenges range from ethical
challenges, design issues to safety, usefulness,
acceptability and appropriateness.
A. Ethical Challenges
Ethics is a philosophical discipline which is concerned
with the morality of human behavior, with right and
wrong [40]. There are four basic principles of healthcare
ethics. These principles were developed by Tom
Beauchamp and James Childress [41]. These four
principles include autonomy, beneficence, non-
malfeasance and justice. The ethical challenges
confronting HRI in healthcare are discussed in line with
these four principles.
Autonomy: Autonomy in this regard refers to the
right of a patient to have control over his or her
body. Hence, patients are allowed to make
decisions concerning their health. Autonomy also
refers to the right of a patient to have the
knowledge of their health information and a right
to their healthcare [42]. Thus, patients must be
fully aware before their health information is
exchanged amongst diverse healthcare providers
[43]. For instance, some robots are designed to
assist in the monitoring of patients health during
human-robot interaction. Such robots may have
cameras installed on them which enable healthcare
providers to monitor their patients‘ health
remotely. Some of these robots may have the
ability to record and transmit data in human-
readable format [7]. This usually causes privacy
concerns especially when the patient is not aware
of whom the data is transmitted to. This is because
patient‘s information must not be divulged or
revealed to anyone who is not involved in the care
of the patient. This may lead to lack of confidence
and trust in healthcare providers, and this can
prevent patients from disclosing relevant
information at the point of care. Nevertheless, trust
is essential in HRI in healthcare as it affects the
willingness of the healthcare providers and
patients to accept the information and suggestions
generated by the robots which invariably affects
the decision making process of the healthcare
system.
Beneficence: Beneficence simply means to do
good. The principle of beneficence ensures that all
procedures and therapy are done to ensure the well
being of patients. To ensure beneficence in
healthcare, healthcare professionals must maintain
a high level of skills and knowledge in the use of
current and best medical practices. However, one
of the major challenges facing HRI in healthcare is
the high cost involved in training healthcare
professionals in the use of robots for therapy and
assistive care. Furthermore, the cost of
maintaining social robots in healthcare is high.
Hence, social robots are not widely deployed in
healthcare despite their significant impacts on
healthcare delivery.
Non-Malfeasance: Non-malfeasance or primum
non nocere in Latin means to do no harm [44].
Harm in this definition refers to anything which
worsens the conditions of patients such as the
introduction of pain, discomfort, suffering,
disability or disfigurement and death [43].
However, if a robot is not designed with safety in
mind, it could harm the users it is designed to
interact with [6]. Hence, social robots in healthcare
such as robotic wheelchairs and walkers must be
designed to avoid obstacles, collision and fall
while maneuvering in an environment. In addition,
during HRI, humans and robots exchange
information and energy. Nevertheless, the
transmission of too much energy by the robot to
the human may result in severe injury.
Furthermore, the presence of sharp edges in the
mechanical design of a social robot can result in
lacerations which can also cause severe damages
to humans [9]. In addition, in HRI,
anthropomorphism creates emotional attachment
State Of The Art: A Study of Human-Robot Interaction in Healthcare 51
Copyright © 2017 MECS I.J. Information Engineering and Electronic Business, 2017, 3, 43-55
between humans and robots. This however can
create a degree of deception in the minds of
humans. This is because when patients are
emotionally attached to anthropomorphic robots,
they might begin to think they are truly humans.
Thus, a loss or an irreparable damage of the robot
that a patient is emotionally attached to can result
in the deterioration of the patient‘s health.
Furthermore, the emotional attachment between
humans and robots is unidirectional [45]. Hence,
social robots cannot reciprocate the emotions
accorded them by humans. In addition, some
humans may exhibit decreased trust and negative
emotional responses towards some robots that
imperfectly resemble human beings. Sparrow and
Sparrow [46] argued that the use of robots to
provide care to the vulnerable especially older
people would most likely result in the reduction of
human contact which is detrimental to their well-
being. Another challenge confronting human-
robot interaction is the ability of the robot to fail or
malfunction during healthcare delivery process.
This is however harmful to the health of the
patients.
Justice: The principle of justice ensures that there
is fairness in decisions concerning the care of a
patient. It also ensures the fair distribution of
scarce medical resources amongst patients. The
principle of justice also ensures that appropriate
laws and legislations that ensure data availability,
privacy, confidentiality, accuracy, integrity,
accountability and security are put in place during
healthcare delivery. In HRI, security and privacy
issues such as unauthorized view of patients‘
information is still a major cause of concern.
B. Usability Challenges
The International Standard Organization (ISO) 924111
defines usability as the extent to which a product can be
used by specified users to achieve specified goals with
effectiveness, efficiency and satisfaction in a specified
context of use [47]. Hence, usability can be described as
how easy it is for users to accurately and efficiently
accomplish a task while using a system. Usability is
concerned with a system‘s particular users, their tasks and
the system‘s environment of use. Mayhew [48] defines
usability based on how well a system supports the user‘s
real life tasks, how easy it is for diverse user groups to
learn the use of a system, how efficient the system is for
frequent users, how easy it is for occasional users to
remember the functionalities of the system, how satisfied
the users are with the system and how easy it is for the
system users to understand what the system does. The
following are some of the usability challenges
confronting HRI in healthcare.
User/Social Acceptance: Dillion [49] defines user
acceptance as the demonstrable willingness within
a user group to employ a technology for the tasks
it is designed to support. In HRI, social
acceptance is defined as an individual‘s
willingness to integrate a robot into an everyday
social environment based on interaction and
experiences [50]. For instance, one of the
challenges facing the Sedasys system, a social
robot that delivers anesthesia to patients without
an anesthesiologist, is low social acceptance rate.
This is due to the fear of autonomous care that the
robot provides [17]. Hence, the fear of
displacement of healthcare professionals by care
robots is heightened.
User Experience: User experience in human-
robot interaction according to Weiss [50] deals
with the way people use the interactive product,
the way it feels like in their hands, how well they
understand how it works, how they feel about it
while they are using it, how well it serves their
purposes, and how well it fits into the entire
context in which they are using it. Nonetheless,
Riek [37] reported that most upper-limb
rehabilitative robots are so difficult for therapists
to use and as a result they remain dormant in
closets after they are purchased. In addition, the
study carried out by Hayley [3] showed that the
sound made by Paro was distressing to some of its
users and hence they disliked it.
Culture: Culture in this parlance refers to the
ethnic, national or geographic location of the users
of the robots. It also encompasses the religion,
language and cultural values of the users of the
robots. For instance, the way the Japanese or
South Koreans interact with robots is quite
different from the way the Europeans interact with
them. The Japanese are usually more enthusiastic
in the deployments of robots than the Europeans.
This is because the use of automatons has a long
tradition in religious ceremonies in Japan, and also
the positive presentation of robots in Japanese
literature leads to a high acceptance of robots in
Japan [50]. Conversely, the Middle Eastern culture
is opposed to iconic technologies, such as
humanoid robots [37]. Hence, the use of social
robots in the Middle East is limited.
Attitude towards the use of the Technology:
Venkatesh et al. [51] defines attitude as an
individual‘s overall affective reaction to using a
system. May et al. [52] conducted a study on the
attitude of healthcare providers on the use of a
robotic telepresent psychiatric treatment delivery
system. The result of the study showed that some
of the healthcare providers felt that the presence of
the robotic technology during care was a threat to
the healthcare delivery process. Conversely, a
study carried out by Hayley et al. [3] showed that
users were enthusiastic and had positive attitude
towards the use of Paro because the users believed
that Paro was beautiful looking, life-like, tactile
and had lovely eyes.
Robot Morphology: This refers to the form or the
appearance of the robot. Social robots can be
52 State Of The Art: A Study of Human-Robot Interaction in Healthcare
Copyright © 2017 MECS I.J. Information Engineering and Electronic Business, 2017, 3, 43-55
mechanical or anthropomorphic in appearance.
However, when a robot‘s appearance is very close
to that of a human, the feelings of comfort and
familiarity declines. This concept is referred to as
the uncanny valley. For instance, Pino et al. [53]
emphasized that hyper-realistic representations of
robots with human appearance could lead persons
with dementia to confusion. Hence, the use of such
robots in the provision of therapy for patients is a
challenge. In addition, Riek et al. [37] conducted a
study on the attitudes of humans towards
humanoid robots in the United Arab Emirates
where the use of iconic technology is discouraged.
Riek et al. [54] used a humanoid robot that
resembles the philosopher Ibn Sina in their study.
The result of the study showed that despite the
opposition to the use of iconic technology in
United Arab Emirates, the humanoid robot was
well-accepted when used for healthcare services
because of its appearance.
VI. RECOMMENDATIONS FOR ENHANCING HUMAN-ROBOT
INTERACTION IN HEALTHCARE
This research so far has shown that social robots have
broad applications in healthcare. They are used to provide
therapeutic and assistive care to the vulnerable, they are
also used to provide companion to patients and they also
assist in surgical operations. The interaction between
humans and social robots in the healthcare system is no
doubt bedeviled with numerous challenges ranging from
emotions and deception, safety, privacy to lack of trust,
high cost of training healthcare professionals in the use of
robotic technology as well as usability issues. The major
consequence of these challenges is a decline in the
acceptance rate of the robotic technology in healthcare.
However, the following suggestions can be adopted to
enhance an effective interaction between humans and
robots in healthcare.
The care of a patient should not be totally left in
the hands of an autonomous social robot. Social
robots should be made to provide complementary
care and not replace human contacts. This will
prevent patients from being totally emotionally
attached to social robots.
Healthcare laws and security policies such as the
Health Insurance Portability and Accountability
Act (HIPAA), National Health Information
Technology and Privacy Advancement Act of
2007, Technologies for Restoring Users Security
and Trust in Health Information Act of 2008, Red
Flags Rule as well as The American Recovery and
Reinvestment Act (ARRA) should be implemented
in HRI in healthcare. This will prevent breaches of
sensitive healthcare information and also ensure
that patients have legal rights concerning their
personally identifiable healthcare information.
This will also ensure that patients have a right to
how their information are revealed and used for
other purposes apart from treatment, payment and
other medical operations. Hence, policies that
ensure patients‘ consent should be encouraged
when robots are involved in the exchange of
patients‘ data.
Human factors should be considered during the
design of social robots because humans are
considered as a major component in human-robot
interaction. For instance, when designing robots to
aid the movement of older people, the robot
designer should consider that most elderly people
are slow in movement and are weak. Hence, robots
with slower motion and soft surface should be
considered for elderly people.
The safety of both the robot, patient and healthcare
providers should be considered during human-
robot interaction. Robots with sharp edges that can
cause lacerations to patients should be avoided in
healthcare. Safety devices such as safety screens
should also be employed in the design of assistive
mobile robots in order to prevent collision and fall.
The uncanny valley should be avoided during the
design of humanoid robots in healthcare. Hence,
the appearance of humanoid robots in healthcare
should not be too close to that of human beings in
order to avoid a repulsive reaction from patients
and caregivers. This will prevent the expression of
fear by its users.
Trust is very essential in human-robot interaction.
This is because the lack of trust in the interaction
between human beings and robots could result in
the misuse and abuse of robots. Hence, trust
facilitates the reliance of human beings on the
ability of social robots to perform their tasks.
Hence, trustworthy robots should be designed for
the healthcare system. This will help social robots
used in healthcare to perform their tasks
effectively.
Social robots in healthcare should be designed to
be emotionally intelligent. They should be able to
recognize and understand human emotions as well
as respond to and manage these emotions. This is
because emotionally intelligent robots are less
frustrating to deal with [55]. For instance,
Cameron [56] is of the view that a robot that is
able to recognize a human emotion can modify its
own behavior to be more accommodating.
The integration of social robots into the healthcare
system is a challenging task. This is because users
perceive autonomous robots differently from other
computer technologies. Social robots conform to
the rules of their environment and also negotiate
their interactions with human beings [50].
Nevertheless, social robots within the context of
healthcare must be robust, effective, efficient and
flexible. This will enhance a human-robot team in
the healthcare system to accomplish a task.
Social robots in healthcare should be fault tolerant
and they should also be designed to degrade
gracefully. This is because the frequent failure or
State Of The Art: A Study of Human-Robot Interaction in Healthcare 53
Copyright © 2017 MECS I.J. Information Engineering and Electronic Business, 2017, 3, 43-55
malfunctioning of social robots during healthcare
delivery process can be harmful to the health of
patients.
Hence, it is advised that social and ethical implications
of social robots should be considered during the design of
social robots in healthcare.
VII. CONCLUSION
Human-robot interaction is an emerging and dynamic
field which deals with the interaction between humans
and social robots in a specific environment. Human-robot
interaction has been applied in several fields such as
Education, Military, Entertainment, Communication and
Healthcare. Human-robot interaction is fast becoming
popular in healthcare as a result of the increase in the
number of vulnerable populations, rising cost of
healthcare and the shortage of qualified healthcare
professionals. HRI have been used to provide
companionship, surgical operations, rehabilitative care
and entertainment to humans within the context of
healthcare. Consequently, HRI improves patient health
outcome as well as the quality of healthcare services
delivered to patients. In spite of the numerous benefits of
social robots in healthcare, the interactions between
humans and robots are bedeviled by numerous challenges.
These include privacy, safety, the form of the robot, trust,
emotions and deception as well as culture. The basic
consequence of these challenges is a decline in the
acceptance rate of social robotic technology in healthcare.
It is against this backdrop that this paper examines the
general concepts of robots and their classification based
on the task they perform. This study also appraises the
concept of human-robot interaction within the concept of
healthcare. Ethical and usability challenges affecting
human-robot interaction in healthcare are also examined
in this paper. The ethical challenges were viewed in line
with the principles of ethics designed by Beauchamp and
Childress. This study suggests that human factor, privacy,
patients‘ consents as well as the safety of the robots,
healthcare providers and patients should be considered
during human-robot interaction in healthcare.
REFERENCES
[1] K. Wada, and T. Shibata, ―Living with seal robots in a
care house devaluations of social and physiological
influences,‖ IEEE/RSJ International Conference on
Intelligent Robots and Systems, Beijing, October 2006.
[2] P. S. Lum, C. G. Burgar, P. C. Shor, M. Majmundar, and
M. Van der Loos, ―Robot-assisted movement training
compared with conventional therapy techniques for the
rehabilitation of upper-limb motor function after stroke,‖
Archives of Physical Medicine and Rehabilitation, vol. 83,
pp. 952-959, 2002.
[3] H.Robinson, A.M. Bruce, N. Kerse, E. Broadbent,
Suitability of healthcare robots for a dementia unit and
suggested improvements,‖ JAMDA, Elsevier, pp.1-7. 2012.
[4] M.M.A. de Graaf, S. B. Allouch, and J.A.G.M. van Dijk,
―What makes robots social?: A user‘s perspective on
characteristics for social human-robot interaction‖,
Springer-Verlag Berlin Heidelberg, 2015
[5] A.Weiss, and V. Evers, ―Exploring cultural factors in
Human-Robot Interaction: A matter of personality?,‖
Comparative Informatics Workshop, December, 2011.
[6] D. Feil-seifer, and M.J. Mataric, ―Ethical principles for
socially assistive robotics‖. Robotics and Automation
Magazine, vol. 18, 2011.
[7] K. S. Bankston, and A. Stepanovich,‖When robot eyes are
watching you: the law & policy of automated
communications surveillance‖, We Robot, University of
Miami, 2014.
[8] M.Matarić, A.Tapus, C. Winstein,and J. Eriksson,
Socially assistive robotics for stroke and mild TBI
rehabilitation‖, Advanced Technologies in Rehabilitation,
vol. 145,pp.249-262, 2008.
[9] A. De Santis, B. Siciliano, A. De Luca, and A. Bicchi,
―An atlas of physical human-robot interaction‖,
Mechanism and Machine Theory, Science Direct, Elsevier,
2007.
[10] K. Capek, ―Rossum‘s universal robots,‖Dover
Publications‖, 2001.
[11] V. Kumar,‖Introduction to Robotics,‖ University of
Pennsylvania, Philadelphia‖, 2014.
[12] Y. S. Choi, ―A Study of Human-Robot Interaction with an
assistive robot to help people with severe motor
impairments‖, Unpublished PhD Thesis, H. Milton
Stewart School Of Industrial And Systems Engineering,
Georgia Institute Of Technology, 2009.
[13] A. Davison, Introduction to robotics,‖ Department of
Computing, Imperial College London, 2016.
[14] F. Hegel, C. Muhl, B. Wrede, M. Hielscher-Fastabend, G.
Sagerer, Understanding social robots, Advances in
Human Computer Interaction, 2009.
[15] M. O. Qureshi, and R.S. Syed, ―The impact of robotics on
employment and motivation of employees in the service
sector, with special reference to health care,‖ Safety and
Health at Work, vol. 5, pp. 198-202, 2014.
[16] S.Kiesler, and P. Hinds,―Introduction to this special issue
on Human-Robot Interaction,‖Special Issue of Human-
Computer Interaction, vol. 19, 2004.
[17] B. Keefe, ―Robots/robotics in healthcare‖, Mayo Clinic
Center for Innovation, 2015.
[18] A. Bruce, I. Nourbakhsh, R. Simmons, ―The role of
expressiveness and attention in human robot interaction‖,
Fall Symposium on Emotional and Intelligence. II, The
Tangled Knot of Social. Cognition, 2001.
[19] K. G. Engelhardt, R. A. Edwards, ―Human robot
integration for service robotics,‖ in Human-Robot
Interaction, Mansour Rahimi, Waldemar Karwowki. Eds.
London: Taylor & Francis Ltd., 1992, pp. 315-346.
[20] C. Bartneck, J. Forlizzi, ―A design-centred framework for
social human-robot interaction,‖ Proceedings of RO-MAN,
Kurashiki. pp. 591-594, 2004.
[21] C. Breazeal, Designing Sociable Robots, Cambridge: MA,
MIT Press, 2002.
[22] T. W. Fong, I. Nourbakhsh, I, K. Dautenhahn, ‖A survey
of socially interactive robots: Concepts, design, and
applications,‖ Robotics and Autonomous Systems, vol. 42,
pp. 142-166, 2002.
[23] J. Złotowski, D. Proudfoot, K. Yogeeswaran and C.
Bartneck, ―Anthropomorphism: opportunities and
challenges in human–robot interaction,‖ International
Journal of Social Robots, vol. 7, pp. 347360, 2015.
[24] I. Asimov, ―I, Robot,‖ Doubleday, 1950.
[25] J. Vincent, S. Taipale, B. Sapio, G. Lugano, and
L.Fortunati, ―Social robots from a human perspective,‖
Springer, 2015.
54 State Of The Art: A Study of Human-Robot Interaction in Healthcare
Copyright © 2017 MECS I.J. Information Engineering and Electronic Business, 2017, 3, 43-55
[26] M. A. Goodrich and A. C. Schultz, ―Human–robot
interaction: A survey, Foundations and Trends in
HumanComputer Interaction, vol. 1, pp. 203-275, 2007.
[27] A. Weiss, R. Bernhaupt, M. Tscheligi, and E. Yoshida,
―Addressing user experience and societal impact in a user
study with a humanoid robot, Proceedings of the
Symposium on New Frontiers in Human-Robot
Interaction, 2009.
[28] H.A Yanco, and J. Drury, Classifying human-robot
interaction: an updated taxonomy,‖ Systems, Man and
Cybernetics, vol.3, pp.2841-2846, 2004.
[29] J.L. Drury, J. Scholtz, and H.A. Yanco, ―Awareness in
human-robot interactions,‖ Proceedings of the IEEE
Conference on Systems, Man and Cybernetics, vol. 1, pp.
111119, 2003.
[30] G. Randelli, ―Improving human-robot awareness through
semantic-driven tangible interaction,‖ Unpublished PhD
Thesis, Sapienza University of Rome, 2011.
[31] R.A.Cohen, Health insurance coverage: Early release of
estimates from the national health interview survey,
National Health Interview Survey Program,
http://www.cdc.gov/nchs/data/nhis/earlyrelease/insur2016
05.pdf, 2015.
[32] M. Montemerlo, J. Pineau, N. Roy, S. Thrun, and V.
Verma, ―Experiences with a mobile robotic guide for the
elderly,‖ National Conference on Artificial Intelligence,‖
2002.
[33] M. E. Pollack, L. Brown, D. Colbry, C. Orosz, B. Peintner,
S. Ramkrishnan, S. Engberg, J. Matthews, J. Dunbar
Jacob, C. McCarthy, S. Thrun, M. Montemerlo, J. Pineau,
and N. Roy,‖ Pearl: A mobile robotic assistant for the
elderly,‖ AAAI Workshop on Automation as Caregiver,
2002.
[34] Bestic. ―Bestic,‖ http://www.bestic.se/en/home/, 2013.
[35] M. Topping, and J. Smith, ―The development of Handy 1,
a rehabilitation robotic system to assist the severely
disabled,‖ Industrial Robot, vol. 25, pp. 316-320, 1998.
[36] J. J. Diehl, C. R. Crowell, M. Villano, K. Wier, K. Tang,
and L. D Riek, ―Clinical applications of robots in autism
spectrum disorder diagnosis and treatment,‖
Comprehensive Guide to Autism, Springer, pp. 411-422,
2014.
[37] L. D Riek, Robotics technology in mental health care,‖in
Artificial Intelligence in Behavioral Health and Mental
Health Care, D. Luxton (Ed.), Elsevier, 2015.
[38] C. D. Kidd, and C. Breazeal, ―Robots at home:
Understanding long-term human-robot interaction.
IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS), 2008.
[39] M. R. Banks, L. M. Willoughby, and W. A. Banks,
―Animal-assisted therapy and loneliness in nursing homes:
Use of robotic versus living dogs,―Journal of the
American Medical Directors Association, vol. 9, pp. 173-
177, 2008.
[40] F. Itulua-Abumere, ―Ethical issues in health and social
care profession,‖ OSR Journal of Humanities and Social
Science (JHSS), pp. 14-18 , 2012
[41] T.L. Beauchamp, and J.F. Childress, Principles of
Biomedical Ethics, New York: Oxford University Press,
1994.
[42] P.S Winkelstein, ―Ethical and social challenges of
electronic health information,‖Medical Informatics,
pp.139- 159, 2009.
[43] O.G. Iroju and J.O. Olaleke,‖ Ethical issues in
interoperability of electronic healthcare systems,
Communications on Applied Electronics, New York, USA,
pp. 12-18, 2015.
[44] M. Almacen, ―EHR interoperability: legal, ethical, and
social challenges,‖ Northwestern University Evanston,
Illinois, 2013.
[45] M. Scheutz,‖The inherent dangers of unidirectional
emotional bonds between humans and social robots,‖
Human-Robot Interaction Laboratory Cognitive Science
Program, Indiana University Bloomington, USA, 2015
[46] R. Sparrow, and Sparrow, L., ―In the hands of machines?
The future of aged care,‖ Minds Machines, vol. 16, pp.
141161, 2006.
[47] ISO 9241-11,‖ Ergonomic requirements for office work
with visual display terminals - part 11,‖ Guidance on
usability, International Organization for Standardization,
1998
[48] D. J. Mayhew, ―The usability engineering lifecycle: A
practitioner‘s handbook for user interface design,‖
Morgan Kaufman Publishers, San Francisco, California,
1999.
[49] A. Dillon, ‗User acceptance of information technology,
in Encyclopedia of Human Factors and Ergonomics, vol.
1. W. Karwowski, Taylor and Francis, Eds London, 2001.
[50] A.Weiss, R. Bernhaupt, M. Lankes and M. Tscheligi,
The USUS evaluation framework for human-robot
interaction,‖ HCI and Usability Unit, ICT&S Center,
University of Salzburg, Sigmund, Haffner-Gasse ,Salzburg,
Austria, 2015.
[51] V. Venkatesh and F. D. Davis, ―A theoretical extension of
the technology acceptance model: Four longitudinal field
studies,‖ Management Science, vol. 46, pp. 86204, 2000.
[52] C. May, L. Gask, T. Atkinson, N. Ellis, F. Mair, and A.
Esmail, ―Resisting and promoting new technologies in
clinical practice: The case of telepsychiatry‖, Social
Science and Medicine, vol. 52, pp.1889 1901, 2001.
[53] M. Pino, M. Boulay, F. Jouen and A.S. Rigaud, Are we
ready for robots that care for us?‖ Attitudes and opinions
of older adults toward socially assistive robots,Frontiers
in Aging Neuroscience, vol.7, 2015.
[54] L. D. Riek, N. Mavridis, S. Antali, N. Darmaki, Z. Ahmed
and Al-Neyadi, M.. ―Ibn Sina steps out: Exploring Arabic
attitudes toward humanoid robots,‖ Proceedings of the
Second International Symposium on New Frontiers in
Human-Robot Interaction, pp. 88-99, 2010.
[55] R. W. Picard. ―What does it mean for a computer to have
emotions,‖ Emotions in humans and artifacts, pp 87-102,
2003.
[56] H. Cameron, ―On the possibility of robots having
emotions. Unpublished PhD Thesis, Depatment of
Philosophy, Georgia State University, 2014.
Authors Profiles
Dr. Iroju Olaronke has a B.Sc. in
Computer Technology at Babcock
University, Nigeria. She also has M.Sc
and PhD in Computer Science at
Obafemi Awolowo University, Nigeria.
She bagged a Post Graduate Diploma in
Education from Obafemi Awolowo
University, Nigeria. She is a member of
the Teachers‘ Registration Council of
Nigeria. Dr. Iroju is a lecturer at the Department of Computer
Science, Adeyemi College of Education, Ondo, Nigeria. Her
research interest is on health informatics, Big Data, human-
robot interaction, interoperability and ontology matching. She
has published widely in international journals and conferences
State Of The Art: A Study of Human-Robot Interaction in Healthcare 55
Copyright © 2017 MECS I.J. Information Engineering and Electronic Business, 2017, 3, 43-55
in the field of Computer Science and she is also a reviewer of
several journals. Her professional papers discuss problems in
interoperability, usability, ontology, ontology matching,
capacity building in electronic healthcare systems and big data
in healthcare.
Dr. Oluwaseun A. Ojerinde is a lecturer
in the Department of Computer Science in
the School of Information and Computer
Technology in Federal University of
Technology, Minna. He bagged his B.Sc.
in Computer Technology at Babcock
University in 2006. He received his M.Sc.
in Mobile Communication System from
Loughborough University in 2008. He also obtained his PhD in
Mobile Communication System from Loughborough University
in 2014. His research area is in Antenna, On-body systems,
Multiple Input Multiple Output (MIMO) systems, spanning,
Telecommunications, Networking and Radiation. He has
worked on the effects of metallic objects on radiation for mobile
devices. He is a member of IEEE and IET.
Dr. Ikono R. N. is a Senior Lecturer at the
Department of Computer Science and
Engineering, Obafemi Awolowo University
Ile-Ife. Her research interests are
Information System, Health Informatics,
Human Robot Interaction, and Software
Product Usability. She has published
widely in international journals and
conferences in the field of Computer Science. She is also a
reviewer of several journals. She is a member of IEEE, Nigerian
Computer Society, Association for Information Systems
Information Development for Health in Africa (INDEHELA)
and African Health Informatics in Nigeria.
How to cite this paper: Iroju Olaronke, Ojerinde Oluwaseun,
Ikono Rhoda,"State Of The Art: A Study of Human-Robot
Interaction in Healthcare", International Journal of Information
Engineering and Electronic Business(IJIEEB), Vol.9, No.3,
pp.43-55, 2017. DOI: 10.5815/ijieeb.2017.03.06
... There is a shortage in healthcare personnel, as well as an increasing number of vulnerable populations such as the elderly and the disabled. Due to these reasons, the use of social robots in healthcare is becoming more widespread [1]. As a result, social robots are deployed in healthcare to provide health education and entertainment to hospital patients, as well as assist the sick and elderly. ...
... Although the positive effects of animal assisted therapy and activities are understood, animals are not allowed in most hospitals and nursing homes due to fear of any negative impacts such as allergic reactions, infections, bites, and scratches [3]. This is where robotics can be integrated, as the applications for robotics have been expanding towards social uses [1]. This then enables the robot to have more natural interactions with humans and become socially fit for the human environment. ...
... This then enables the robot to have more natural interactions with humans and become socially fit for the human environment. With previously stated reasons for finding such solutions necessary, there is an extensive deployment of social robots in the healthcare system [1]. For instance, researches have shown that those suffering from Autism Spectrum Disorders responded more to treatments involving robotic technology than treatments from human therapists [10]. ...
Article
Full-text available
The development of social robots has been notably increasing and gaining popularity in recent times. These are also being integrated into healthcare systems, as a means to accompany patients, provide mental health therapy, and entertainment in place of direct human intervention. This paper discusses the recent developments on imitation learning for robot therapy in the field of social robotics to gain knowledge about the importance of this approach as an alternative solution to mental health therapy. The integration of robots to the mental healthcare system is known as robot therapy, which is used as a substitute for animal assisted therapy. Therapy that makes use of animals has been proven to be effective in dealing with mental disorders. However, there are risks such as allergic reactions, bites, and scratches that come with animal assisted therapy, but not robot therapy. The goal for developing robots for this is to make them seem almost life-like--has a way of thinking and emotions. For this to happen, humanoids are being programmed to appear human-like. A solution for this is imitation learning, which is a way for machines to learn, not only tasks, but responses in certain situations, only by observing and imitating humans in an environment.
... Fig. 3 shows a robot drawing blood from a patient. This robot developed by Veebot can adequately identify the most accessible vein with an accuracy of 83%, which is as good as an experienced phlebotomist [33]. Robots have also been used to assist healthcare workers in surgeries. ...
... A typical example of this robot is Paro. Fig. 3. Veebot robot drawing blood from a patient [33] ...
Article
Full-text available
The health of every individual in the world is greatly influenced by global health issues and threats which are usually caused by international trade and voyage. These threats which have exposed the inadequacies of healthcare systems across the globe include the rapid spread of non-communicable and infectious diseases, pandemics, hunger and starvation, natural disasters, shortage of healthcare personnel and climate change. These threats have led to economic and social disruption in almost all spheres of human lives such as agriculture and education. Aim: Against this background, this study reviews global health challenges and the importance of robots in global health. This study also appraises the factors hindering the effective use of robotic technology to improve global health. Methodology: A total of 41 literatures relevant to the subject matter were obtained from diverse scientific electronic databases including CiteseerX, Science Direct, Google Scholar, IEEE explore, indexCat, PubMed and National Library of Medicine. Results: The study showed that robots can be used to improve global health by diagnosing and treating infectious diseases, reducing the dangers of human contact during pandemic and delivering food and medicines to infected individuals. The study also showed that robots can be used to reduce harmful gases released into the atmosphere and also limit the anxiety and fear of vaccination. The study also revealed that high cost, privacy-related issues, interoperability challenges and the fear of displacement of jobs by robots are some of the factors hindering the effective use of robotic technology to improve global health. Conclusion: This paper suggests that adopting a common standard for robots of different brands and education strategies are some of the strategies that will facilitate the effective use of robotic systems to improve the health of individuals across the globe.
... Social robots with autonomous decision-making capabilities are becoming real in many applications. They are used in tasks related to healthcare (Olaronke et al. 2017;Castillo et al. 2018), education (Bertel and Hannibal 2016), and entertainment (Alonso- Martín et al. 2010). Thus, social robots are intended to coexist with humans at their homes or care centres, requiring robust interaction mechanisms for working during long periods. ...
Article
Full-text available
Adapting to dynamic environments is essential for artificial agents, especially those aiming to communicate with people interactively. In this context, a social robot that adapts its behaviour to different users and proactively suggests their favourite activities may produce a more successful interaction. In this work, we describe how the autonomous decision-making system embedded in our social robot Mini can produce a personalised interactive communication experience by considering the preferences of the user the robot interacts with. We compared the performance of Top Label as Class and Ranking by Pairwise Comparison, two promising algorithms in the area, to find the one that best predicts the user preferences. Although both algorithms provide robust results in preference prediction, we decided to integrate Ranking by Pairwise Comparison since it provides better estimations. The method proposed in this contribution allows the autonomous decision-making system of the robot to work on different modes, balancing activity exploration with the selection of the favourite entertaining activities. The operation of the preference learning system is shown in three real case studies where the decision-making system works differently depending on the user the robot is facing. Then, we conducted a human–robot interaction experiment to investigate whether the robot users perceive the personalised selection of activities more appropriate than selecting the activities at random. The results show how the study participants found the personalised activity selection more appropriate, improving their likeability towards the robot and how intelligent they perceive the system. query Please check the edit made in the article title.
... Examples of intelligent assistive robots are [133,134], while the employment of robots for people rehabilitation is witnessed in [135,136,137]. An extensive assessment of human-robot interaction in healthcare, their applications and challenges is proposed in [138]. Design, ethical and usability issues such as privacy, trust, safety, users' attitude, culture, robot morphology as well as emotions and deception arising from the interaction between humans and robots in healthcare are also reviewed. ...
Thesis
The employment of personal robots or service robots has aroused much interest in recent years with an amazing growth of robotics in different domains. Design of companion robots able to assist, to share and to accompany individuals with limited autonomy in their daily life is the challenge of the future decade. However, performances of nowadays robotic bodies and prototypes remain very far from defeating such challenge. Although sophisticated humanoid robots have been developed, much more effort is needed for improving their cognitive capabilities.Actually, the above-mentioned commercially available robots or prototypes are not still able to naturally adapt themselves to the complex environment in which they are supposed to evolve with humans. In the same way, the existing prototypes are not able to interact in a versatile way with their users. In fact they are still very far from interpreting the diversity and the complexity of perceived information or to construct knowledge relating the surrounding environment. The development of bio-inspired approaches based on Artificial Cognition for perception and autonomous acquisition of knowledge in robotics is a feasible strategy to overcome these limitations. A number of advances have already conducted to the realization of an artificial-cognition-based system allowing a robot to learn and create knowledge from observation (association of sensory information and natural semantics). Within this context, the present work takes advantage from evolutionary process for semantic interpretation of sensory information to make emerge the machine-awareness about its surrounding environment. The main purpose of the Doctoral Thesis is to extend the already accomplished efforts (researches) in order to allow a robot to extract, to construct and to conceptualize the knowledge about its surrounding environment. Indeed, the goal of the doctoral research is to generalize the aforementioned concepts for an autonomous, or semi-autonomous, construction of knowledge from the perceived information (e.g. by a robot). In other words, the expected goal of the proposed doctoral research is to allow a robot progressively conceptualize the environment in which it evolves and to share the constructed knowledge with its user. To this end, a semantic-multimedia knowledge base has been created based on an ontological model and implemented through a NoSQL graph database. This knowledge base is the founding element of the thesis work on which multiple approaches have been investigated, based on semantic, multimedia and visual information. The developed approaches combine this information through classic machine learning techniques, both supervised and unsupervised, together with transfer learning techniques for the reuse of semantic features from deep neural networks models. Other techniques based on ontologies and the Semantic Web have been explored for the acquisition and integration of further knowledge in the knowledge base developed. The different areas investigated have been united in a comprehensive logical framework. The experiments conducted have shown an effective correspondence between the interpretations based on semantic and visual features, from which emerged the possibility for a robotic agent to expand its knowledge generalization skills in even unknown or partially known environments, which allowed to achieve the objectives set.
... The field of service robotics exists for more than 20 years and there have been continuous researching activities in this domain. Usually, the specific focus of most of these efforts is to optimize the collaboration possibilities between humans and robots [1]. Even though autonomous robots and mobile service platforms have been applied and investigated for many different applications in the field of home care as well as retirement and elderly homes [2,3] ...
Article
Full-text available
Robot-based service platforms are currently establishing themselves as new and affordable variants for supporting care in elderly, retirement and nursing homes. Many are open multifunctional platforms, which can potentially be integrated into such environments, if the necessary infrastructure is available. Furthermore, many services can be realized on these platforms, which can be used to foster distant interactions between inhabitants and care-providers, while simultaneously keeping up the quality of life of the inhabitants. Open mobile robotic platforms allow the extension with adequate new sensors. To detect infectious diseases of residents and healthcare-professionals, optical sensors can be used for the assessment of vital data such as heartrate and heartrate variability, respiratory rate, SpO2 or temperature. Additionally, you can consider demographic data (age, gender, constitution) of the observed person for the optical assessment, i.e. obtained by facial image analysis. As these mobile platforms are also equipped for telepresence, in case of detecting an infected person, these systems support video conferencing with their built-in cameras and microphones. Finally, the interaction with the electronic care record is necessary to upload all acquired vital data and further relevant information. All the named technologies have been under investigation in the past years and are currently moving from laboratory settings to real-world scenarios. Nevertheless, the smooth integration of all components into one system architecture in combination with (AI-based) data analysis are still open issues.
... This section is regarding some of the other research that has been done in the past to talk about HCI and healthcare. The paper is presented in [117] it gives human-robot interaction details in general. Human-robot interaction is the leading field when we talk about AI & HCI. ...
Article
Full-text available
Artificial intelligence (AI) is one of the emerging technologies. In recent decades, artificial intelligence (AI) has gained widespread acceptance in a variety of fields, including virtual support, healthcare, and security. Human-Computer Interaction (HCI) is a field that has been combining AI and human-computer engagement over the past several years in order to create an interactive intelligent system for user interaction. AI, in conjunction with HCI, is being used in a variety of fields by employing various algorithms and employing HCI to provide transparency to the user, allowing them to trust the machine. The comprehensive examination of both the areas of AI and HCI, as well as their subfields, has been explored in this work. The main goal of this article was to discover a point of intersection between the two fields. The understanding of Explainable Artificial Intelligence (XAI), which is a linking point of HCI and XAI, was gained through a literature review conducted in this research. The literature survey encompassed themes identified in the literature (such as XAI and its areas, major XAI aims, and XAI problems and challenges). The study’s other major focus was on the use of AI, HCI, and XAI in healthcare. The poll also addressed the shortcomings in XAI in healthcare, as well as the field’s future potential. As a result, the literature indicates that XAI in healthcare is still a novel subject that has to be explored more in the future.
Article
Full-text available
With the increasingly severe aging of the population, the difficult and expensive medical treatment problems are becoming more and more prominent; the salary level of domestic doctors is not high, but the cost of training doctors is high, coupled with doctors’ work pressure and mental pressure; the number of candidates for medical school is decreasing year by year; medical talent is rare; and the allocation of medical staff is scarce. Health care is the basic guarantee for people’s good life, and the shortage of medical staff will have many impacts on health care. Human-computer interaction (HCI) is the study of people, computers, and their interaction. HCI refers to the communication between the user and the computer system, which is the two-way information exchange of various symbols and actions between the human and the computer. The purpose of this paper is to study a healthcare system with human-computer interaction through the client, apply the system to the teaching of physiology and medicine, and analyze its effects and functions in combination with various evaluation indicators. This paper selects teaching content, ease of use of human-computer interaction design, technical services, and user subjective satisfaction as evaluation indicators, and constructs an evaluation model for this. And it builds the physiology and medicine teaching system framework and healthcare system, and conducts tests and statistics on the teaching system. This paper combines online questionnaires, in-app survey feedback, and field visits to collect feedback from users and administrators. The final data show that the teaching system meets the requirements in four evaluation indicators: teaching content, ease of use of human-computer interaction design, technical services, and user subjective satisfaction. User satisfaction with these four aspects reached 86.33%, 95.17%, 63.83%, and 81.87%, respectively. It shows that the system is more popular and can meet the needs of most users.
Chapter
The most recent innovation in endoscopic medical devices intersects two very diverse main disciplines—robotics and surgery, both promising extraordinary opportunities and raising some concerns for their ethical and legal consequences. In this chapter, we provide a general overview of such issues, focusing on the European Union framework. First, we provide an up-to-date overview of the academic discussion on the social impact of robotics and “roboethics,” the new discipline in applied ethics, and clarifying the fundamental principles of “engineering ethics” that should be taken into consideration in designing and implementing endorobots. Then, we describe the regulatory framework in the EU, illustrating briefly the transition between the current legislation (provided by the combination of Directive 1993/42/EEC and Directive 1990/385/EEC), the new Regulation (EU) 2017/745 (expected to enter into force in 2021), and the proposals of Regulations concerning ethical aspects and legal liability of Artificial Intelligence and robotics adopted by the European Parliament in October 2020.
Chapter
Parallel link mechanisms with flexible links (cable-driven robots) offer the following advantages: low link inertia, low noise, mobility, low material consumption and others. Cable-driven robots are finding more and more applications, examples of which are described in this article. After reviewing the examples, the possible classification criteria will be outlined and a classification for three- and four-coordinate flexible link manipulators will be developed. The S symbol indicates that the basic structural scheme is a mechanism that performs Schoen flies motion and the number of attachment points n on the output link and satisfy the following relations on the base i: 3 ≤ n ≤ 4 and 3 ≤ i ≤ 4, and the number of arrangement layouts of axes of the input links m and the combination of flexible links satisfy the following relations: 1 ≤ m ≤ 3 and 1 ≤ j ≤ 3 respectively. The article concludes with a summary of the work results and an outline of future research.
Chapter
In der theoretischen Diskussion ist mit einem Artificial Companion eine Reihe an Eigenschaften gemeint, welche fördern sollen, dass Nutzer:innen ein technologisches System als verlässlichen und treuen Gefährten wahrnehmen. Bislang gibt es allerdings keinen Konsens darüber, welche Eigenschaften dafür konkret notwendig sind. Der vorliegende Beitrag nähert sich deshalb der Thematik von einer praktischen Seite, damit Aussagen über die Eigenschaften heutiger Companion-Systeme getroffen werden können – welche in der vorliegenden Arbeit als Artificial Companions der ersten Generation bezeichnet werden. Der Beitrag stellt die Ergebnisse einer deskriptiven Datenanalyse von n = 50 Companion-Robotern vor, die hinsichtlich ihres Aussehens und ihrer kommunikativen Fähigkeit verglichen werden. Es erfolgt ein Vorschlag für eine Companion-Typologie anhand ihrer Einsatzgebiete inklusive Beschreibung der zentralen Aufgaben und Funktionen. Der letzte Teil erläutert zwei zentrale Motive, auf deren Grundlage Artificial Companionships entstehen können.
Article
Full-text available
To improve the way humans are interacting with robots various factors have to be taken into account. An evaluation framework for Human-Robot Collaboration with humanoid robots addressing usability, social acceptance, user experience, and societal impact (abb. USUS) as evaluation factors is proposed (see figure 1). The theoretical framework USUS is based on a multi-level indicator model to operationalize the evaluation factors. Evaluation factors are described and split up into several indicators, which are extracted and justified by literature review. The theoretical factor indicator framework is then combined with a methodological framework consisting of a mix of methods derived and borrowed from various disciplines (HRI, HCI, psychology, and sociology). The proposed method mix allows addressing all factors within the USUS framework and lays a basis for understanding the interrelationship of the USUS Factors.
Conference Paper
Full-text available
A common description of a social robot is for it to be capable of communicating in a humanlike manner. However, a description of what communicating in a 'humanlike manner' means often remains unspecified. This paper provides a set of social behaviors and certain specific features social robots should possess based on user's experience in a longitudinal home study, discusses whether robots can actually be social, and presents some recommendations to build better social robots.
Article
Full-text available
Socially Assistive Robots (SAR) may help improve care delivery at home for older adults with cognitive impairment and reduce the burden of informal caregivers. Examining the views of these stakeholders on SAR is fundamental in order to conceive acceptable and useful SAR for dementia care. This study investigated SAR acceptance among three groups of older adults living in the community: persons with Mild Cognitive Impairment, informal caregivers of persons with dementia, and healthy older adults. Different technology acceptance questions related to the robot and user characteristics, potential applications, feelings about technology, ethical issues, and barriers and facilitators for SAR adoption, were addressed in a mixed-method study. Participants (n = 25) completed a survey and took part in a focus group (n = 7). A functional robot prototype, a multimedia presentation, and some use-case scenarios provided a base for the discussion. Content analysis was carried out based on recorded material from focus groups. Results indicated that an accurate insight of influential factors for SAR acceptance could be gained by combining quantitative and qualitative methods. Participants acknowledged the potential benefits of SAR for supporting care at home for individuals with cognitive impairment. In all the three groups, intention to use SAR was found to be lower for the present time than that anticipated for the future. However, caregivers and persons with MCI had a higher perceived usefulness and intention to use SAR, at the present time, than healthy older adults, confirming that current needs are strongly related to technology acceptance and should influence SAR design. A key theme that emerged in this study was the importance of customizing SAR appearance, services, and social capabilities. Mismatch between needs and solutions offered by the robot, usability factors, and lack of experience with technology, were seen as the most important barriers for SAR adoption.
Article
Full-text available
Anthropomorphism is a phenomenon that describes the human tendency to see human-like shapes in the environment. It has considerable consequences for people’s choices and beliefs. With the increased presence of robots, it is important to investigate the optimal design for this technology. In this paper we discuss the potential benefits and challenges of building anthropomorphic robots, from both a philosophical perspective and from the viewpoint of empirical research in the fields of human-robot interaction and social psychology. We believe that this broad investigation of anthropomorphism will not only help us to understand the phenomenon better, but can also indicate solutions for facilitating the integration of human-like machines in the real world.
Article
Full-text available
The advent of Information and Communication Technology (ICT) in the past decades has led to the wide adoption of electronic healthcare systems in healthcare. This has in actual fact led to the improvement in the quality of healthcare by enhancing the collection, storage, retrieval and access to health information. In addition, the use of ICT in healthcare provides more efficient healthcare services by reducing medical errors and costs thereby increasing patients' safety and satisfaction. Moreover, patients' information can be located in more than one electronic healthcare system. This is because patients may have more than one healthcare provider or they may move from one location to another. Hence, it becomes pertinent for patients' information to be available in all electronic healthcare systems at all points of care. Thus, the seamless exchange of meaningful information amongst healthcare providers and patients at the point of care becomes very crucial. This is because the diagnoses and treatment of patients depends on the timely access to accurate patients' information such as the medical history, laboratory reports as well as radiology report. However, several ethical challenges such as data privacy, confidentiality, control of access to patients' information, the commercialization of de-identified patients' information and ownership of patients' information which are associated with the interoperability of electronic healthcare systems still remain unresolved. Hence, the acceptance and adoption of electronic healthcare systems for information exchange and use is discouraged and hindered. Consequently, the healthcare system is associated with high error rates and low quality healthcare services. Thus, ethical issues need to be addressed in the context of interoperability of electronic healthcare systems. Based on this background, this paper appraises ethical issues associated with the interoperability of healthcare systems and how they can be addressed.
Chapter
Advances in socially assistive robotics have the potential to promote innovation in the diagnosis and treatment of individuals with Autism Spectrum Disorder (ASD). Research has revealed that individuals with ASD (1) show strengths in understanding the physical, object-related world and weaknesses in understanding the social world, (2) are more responsive to feedback given by a computer than a human, and (3) are more interested in treatment involving technology/robots. These findings suggest that a co-robot therapist may be an important addition to clinical assessment and/or therapy if it can emulate certain human therapist functions. Still, the majority of research in this area to date has focused on the development of technology, with scant attention paid to best practice clinical approaches. Therefore, the clinical use of robots for ASD should be considered an experimental approach to diagnosis and/or treatment until rigorous clinical trials are conducted and replicated. The end of this section includes a roadmap for future research on the clinical uses for robots in the diagnosis and treatment of individuals with ASD. Crucially, clinical innovation must parallel technological innovation if this approach is to become an accepted diagnostic and/or treatment approach for ASD.
Book
Artificial Intelligence in Behavioral and Mental Health Care summarizes recent advances in artificial intelligence as it applies to mental health clinical practice. Each chapter provides a technical description of the advance, review of application in clinical practice, and empirical data on clinical efficacy. In addition, each chapter includes a discussion of practical issues in clinical settings, ethical considerations, and limitations of use. The book encompasses AI based advances in decision-making, in assessment and treatment, in providing education to clients, robot assisted task completion, and the use of AI for research and data gathering. This book will be of use to mental health practitioners interested in learning about, or incorporating AI advances into their practice and for researchers interested in a comprehensive review of these advances in one source. Summarizes AI advances for use in mental health practice; Includes advances in AI based decision-making and consultation; Describes AI applications for assessment and treatment; Details AI advances in robots for clinical settings; Provides empirical data on clinical efficacy; Explores practical issues of use in clinical settings. © 2016 Elsevier Inc. Chapter 8: