Content uploaded by Heinrich C. Mayr
All content in this area was uploaded by Heinrich C. Mayr on Aug 26, 2016
Content may be subject to copyright.
International Journal of Distributed
2016, Vol. 12(8)
ÓThe Author(s) 2016
A review on applications of activity
recognition systems with regard to
performance and evaluation
Suneth Ranasinghe, Fadi Al Machot and Heinrich C Mayr
Activity recognition systems are a large field of research and development, currently with a focus on advanced machine
learning algorithms, innovations in the field of hardware architecture, and on decreasing the costs of monitoring while
increasing safety. This article concentrates on the applications of activity recognition systems and surveys their state of
the art. We categorize such applications into active and assisted living systems for smart homes, healthcare monitoring
applications, monitoring and surveillance systems for indoor and outdoor activities, and tele-immersion applications.
Within these categories, the applications are classified according to the methodology used for recognizing human beha-
vior, namely, based on visual, non-visual, and multimodal sensor technology. We provide an overview of these applica-
tions and discuss the advantages and limitations of each approach. Additionally, we illustrate public data sets that are
designed for the evaluation of such recognition systems. The article concludes with a comparison of the existing meth-
odologies which, when applied to real-world scenarios, allow to formulate research questions for future approaches.
Human activity recognition, active and assisted living, sensor networks, smart systems
Date received: 9 March 2016; accepted: 11 July 2016
Academic Editor: Jose
Human activity recognition (HAR) is a highly dynamic
and challenging research topic. It aims at determining
the activities of a person or a group of persons based
on sensor and/or video observation data, as well as on
knowledge about the context within which the observed
activities take place. In the ideal case, an activity is
recognized regardless of the environment it is per-
formed in or the performing person.
In general, the HAR process involves several steps—
from collecting information on human behavior out of
raw sensor data to the final conclusion about the cur-
rently performed activity. These steps are as follows:
(1) pre-processing of the raw data from sensor streams
for handling incompleteness, eliminating noise and
redundancy, and performing data aggregation and nor-
malization; (2) segmentation—identifying the most
significant data segments; (3) feature extraction—
extracting the main characteristics of features (e.g. tem-
poral and spatial information) from the segmented data
using, for example, statistical moments; (4) dimensional-
ity reduction—decreasing the number of features to
increase their quality and reduce the computational
effort needed for the classification; and (5) classifica-
tion, the core machine learning and reasoning—deter-
mining the given activity.
Application Engineering Research Group, Alpen-Adria-Universita
Klagenfurt, Klagenfurt, Austria
Suneth Ranasinghe, Application Engineering Research Group, Alpen-
¨t Klagenfurt, Klagenfurt 9020, Austria.
Creative Commons CC-BY: This article is distributed under the terms of the Creative Commons Attribution 3.0 License
(http://www.creativecommons.org/licenses/by/3.0/) which permits any use, reproduction and distribution of the work without
further permission provided the original work is attributed as specified on the SAGE and Open Access pages (http://www.uk.sagepub.com/aboutus/
The main goals of HAR systems are to observe and
analyze human activities and to interpret ongoing
events successfully. Using visual and non-visual sensory
data, HAR systems retrieve and process contextual
(environmental, spatial, temporal, etc.) data to under-
stand the human behavior. There are several applica-
tion domains where HAR concepts are investigated
and the systems are developed. We divide them roughly
into four categories: active and assisted living (AAL)
systems for smart homes, healthcare monitoring applica-
tions, monitoring and surveillance systems for indoor and
outdoor activities, and tele-immersion (TI) applications.
Traditionally, the task of observing and analyzing
human activities was carried out by human operators,
for example, in security and surveillance processes or
the processes of monitoring a patients’ health condi-
tion. With the increasing number of camera views and
technical monitoring devices, however, this task
becomes not only more challenging for the operators
but also increasingly cost-intensive, in particular, since
it requests around-the-clock operation. In practice, for
the case of home care, personnel deployment for such
tasks often cannot be financially feasible.
Moreover, HAR systems within these fields are able
to support or even replace human operators in order to
enhance the efficiency and effectiveness of the observa-
tion and analysis process. As an example, with the help
of sensory devices, an HAR system can keep track of
the health condition of a patient and notify the health
personnel in case of an urgent need.
On the other hand, scientific and technical progress
continuously improved the living conditions of humans.
This causes a dramatic societal change as it comes with
decreasing birth rates and increasing life expectancy,
which together turn the age pyramid upside down.
Intensive research and development in the field of
Active and Assisted Living (AAL)
focuses on master-
ing one of the consequences of this change: the increas-
ing need of care and support for older people. The goal
of AAL systems, therefore, is to provide appropriate
unobtrusive technical support enabling people to live as
independent as possible for as long as possible in their
homes. To be able to provide such support, an AAL
system needs to know about a person’s behavior; that
is, it depends on powerful HAR systems for obtaining,
collecting, compiling, and analyzing such knowledge.
Similarly, TI systems also make use of HAR systems to
track and simulate human behaviors in a virtual envi-
ronment in order to build attractive game interfaces or
to enhance the existing communication methods.
This survey article focuses on current activity recog-
nition (AR) projects applied within these fields, their
achievements, issues, and challenges. We do not focus
on the classification (machine learning) approaches of
HAR systems, as there is a rich body of previously pub-
lished work in this area.
The organization of the article is as follows: section
‘‘Notion of ‘activity’’’ shortly discusses the concept of
‘‘activity’’ as understood within this study. Section
‘‘Applications of AR systems’’ presents a broad selec-
tion of state-of-the-art systems representing the above
categories: active and assisted living systems for smart
homes, healthcare monitoring applications, monitoring
and surveillance systems for indoor and outdoor activi-
ties, and TI applications. In section ‘‘AR systems and
approaches,’’ these systems are classified based on dif-
ferent types of sensors, namely, video-based, non-
video-based, or multimodal sensors. Section ‘‘Popular
data sets’’ outlines popular AR data sets that are used
as universal data sets to evaluate such systems. Based
hereon, section ‘‘Discussion of HAR approaches’’ dis-
cusses the limitations of the existing HAR approaches
and presents challenges for future research.
Notion of ‘‘activity’’
So far, no unique ontological definition of the notion/
concept of ‘‘activity’’ has been proposed. However,
common to the usage of this notion in the various appli-
cation domains is its refinement into granularity levels.
An example of such refinement can be found in the
which assumes activities (e.g. ‘‘prepar-
ing coffee’’) to be hierarchically structured into actions
(e.g. ‘‘enter the kitchen,’’ ‘‘fill the water container’’) that
again are composed out of operations, the latter being
understood as atomic steps implementing the action
(like ‘‘push the door handle,’’ ‘‘open the water tap’’).
Activities are understood as aggregations of actions,
which again are understood as aggregations of atomic
Within the AAL domain, taxonomies have been pro-
posed in, for example, Katz
and Lawton and Brody
for the so-called (instrumental) activities of daily living
(ADLs). In general, the taxonomies depend on the liv-
ing scenarios, such as the ADLs and activities related to
medical or other domains.
Usually, an activity is supposed to be performed
within a certain time span, which is calculated based on
the durations of the composing sub-units.
the aspects of performance, duration, and order of these
sub-units, activities can be divided into composite activi-
ties, sequential activities, concurrent activities, and inter-
leaved activities (Figure 1).
Based hereon, actions and
activities can be derived from a well-defined information
hierarchy of events (based on sensor inputs) fused with
additional context information.
Applications of AR systems
During the last decade, there was a significant growth
of the number of publications in the field of AR; in
2International Journal of Distributed Sensor Networks
particular, many researchers have proposed application
domains to identify specific activity types or behaviors
to reach specific goals in these domains. This section
focuses on state-of-the-art applications that use HAR
methodologies to assist humans. This review, in partic-
ular, discusses the application of the current AR
approaches to AAL for smart homes, healthcare moni-
toring solutions, security and surveillance applications,
and TI applications; these approaches are further clas-
sified along the observation methodology used for
recognizing human behavior, namely, into approaches
based on visual, non-visual, and multimodal sensor
technology. Table 1 shows the references considered,
each related to the respective category and class.
Active and assisted living applications for smart
Advances in modern technologies have provided inno-
vative ways to enhance the quality of independent living
of elderly or disabled people. Active and assisted living
systems use AR techniques to monitor and assist resi-
dents to secure their safety and well-being. According
to Demiris et al.,
a smart home is an environment
equipped with sensors that enhance the safety of resi-
dents and monitor their health conditions. Thus, smart
homes improve the level of independency and the qual-
ity of life of the people who need support for physical
and cognitive functions. In general, inside a smart
home, the behavior of the residents and their interac-
tion with the environment are monitored by analyzing
the data collected from sensors. Table 2 shows the
state-of-the-art AAL systems using HAR components.
Recently, research has been conducted aimed at
using the combination of wearable sensors and sensors
implanted into the environment, which, together with
audio–video-based solutions, allow for a gentle assis-
tance of the elderly people. The GER’HOME project
topicsText/gerhomeProject.html) used multi-sensor
analysis for monitoring the activity of the elderly peo-
ple to improve their living conditions and to reduce the
costs of long hospitalizations. GER’HOME is equipped
with a network of cameras and contact sensors to track
people and recognize their activities.
Similarly, HERMES (http://www.fp7-hermes.eu/)
aimed at providing cognitive care for people who are
suffering from mild memory problems. This is achieved
by combining the functional skills of a particular per-
son with his or her age-related cognitive incapabilities
and assist them when necessary. HERMES used visual
and audio processing techniques, speech recognition,
speaker identification, and face detection techniques to
guide the people. In a similar manner, the universAAL
form and reference specification for AAL was intro-
duced to technically produce cheaper products that are
simple, configurable, and easily deployable at a smart
home to provide useful ‘‘AAL services.’’
Figure 1. Relations between actions and general structures of ADLs.
Table 1. References, classified along application categories and observation methodology.
Application type Visual-based systems Non-visual-based systems Multimodal systems
Active and assisted living (AAL)
applications for smart homes
13–18 11,19,20 9,10,12,21–32
Healthcare monitoring applications 33–36 37,38 39–45
Security and surveillance applications 46–60 58,61–63
Tele-immersion (TI) applications 64–75
Ranasinghe et al. 3
Moreover, smart home system proposed by the Center
for Advanced Studies in Adaptive Systems (CASAS)
used machine learning and data mining techniques to
analyze the daily activity patterns of a resident to gener-
ate automation policies based on the identified patterns
to support the residents. Automation policies were used
to assist elder individuals in their urgent needs.
Another example of an intelligent home environ-
ment is the Mobiserv (http://www.mobiserv.info/) proj-
The aim of this project is to offer health, medical,
and well-being services to older adults. The support
was provided based on the understanding of the user
context data of their indoor daily living situations.
The SWEET-HOME project (2009–2013)
posed by the French National Research Agency aimed
at establishing a smart home solution which is capable
to interact with old people or disabled individuals using
an audio-based interaction technology. Initially, a mul-
timodal sound corpus was recorded using the interac-
tions of healthy individuals. Later on, this audio data
were used to train the smart home’s speech and sound
Finally, the Human Behavior Monitoring and
Support (HBMS) project
(this work was funded by the
Klaus Tschira Stiftung gGmbH, Heidelberg) was initi-
ated to support old or disabled individuals with cognitive
impairments to live autonomously in their familiar
environments. In the first phase, a person’s activities of
daily living are observed by the HBMS observation
engine to create a Human Cognitive Model (HCM) using
the Human Cognitive Modeling Language HCM-L
view=download). Then, these HCMs are used by the
HBMS support engine that applies reasoning mechan-
isms based on Answer Set Programming (ASP) to assist
individuals in a smart and unobtrusive way.
Healthcare monitoring applications
The development of medical science and technology
considerably increased the life quality of patients. As
stated by Goldstone,
life expectancy rates will increase
dramatically in 2050, and approximately 30% of
Americans, Canadians, Chinese, and Europeans will be
over the age of 60 years. This will lead to higher
demands for medical personnel which may be impossi-
ble to be supplied in the near future. Hence, researchers
try to enhance the existing healthcare monitoring
approaches that would handle urgent medical situations
and shorten the hospital stay and regular medical visits
of a patient. Table 3 shows the state-of-the-art health-
care monitoring applications.
Table 2. State-of-the-art active and assisted living applications for smart homes.
Author Keywords Sensor information used
Ricardo Costa et al.
Ambient intelligence, assisted living Domestic smart sensors
TLM Van Kasteren et al.
Activity recognition, machine learning,
wireless sensor networks (WSNs)
Chen Wu et al.
Multi-view activity recognition, decision
fusion methods, smart home
Parisa Rashidi et al.
Activity recognition, data mining,
sequence mining, clustering, smart homes
Accelerometer, state-change sensor,
motion sensors, and so on
Can Tunca et al.
Ambient assisted living, WSNs, human
´n Blasco et al.
Ambient assisted living, ambient
intelligence, smart homes, context and
user awareness, distributed sensor
Radio-frequency identification (RFID)
technology, WSNs, and so on
Saisakul Chernbumroong et al.
Assisted living systems, activities of daily
living (ADLs), wrist-worn multi-sensors,
elderly care, feature selection, and
magnetometer, bio-sensors, and so on
Nirmalya Roy et al.
Multimodal sensing, context recognition Body-worn sensors, motion sensors, and
M Blumendorf and S Albayrak
Smart environments, multimodal
interaction, model-based user interface
development, ambient assisted living,
multi-access service platform
Smart home sensors
A Dohr et al.
eHealth, pervasive healthcare,
telemedicine, Near Field Communication
RFID and NFC technology
Jaime Lloret et al.
Ambient assisted living (AAL), WSNs,
sensors and actuators, elderly people
Microphones, accelerometer, presence
sensors, mobile phone sensors, cameras,
and so on
4International Journal of Distributed Sensor Networks
Basically, healthcare monitoring systems are
designed based on the combination of one or more AR
components such as fall detection, human tracking,
security alarm and cognitive assistance components.
Most of the healthcare systems use body-worn and
contextual sensors that are placed on patients’ bodies
and in their environment. Once help is needed, the sys-
tem notifies the relevant parties (i.e. medical personnel)
about the situation to assist the patient quickly. The E-
safe fall detection and notification system
the zigbee-based wearable sensor system to automati-
cally detect fall situations and notify the in-house corre-
spondents via zigbee technology. Then the external
correspondents are also notified via Short Message
Service (SMS) and email.
The smart assisted living (SAIL) system was intro-
duced in Zhu et al.
using human–robot interaction
(HRI) to monitor the health condition of an elder or dis-
abled individual. SAIL consists of a body sensor network,
a companion robot, a smartphone, and a remote health
provider. Based on the sensor data, the robot assists the
human or the help is provided by a remote health provi-
der contacted through a smartphone gateway.
content/6/1/9), a European Union (EU)-funded health-
care project, aims at assisting elderly people using a
wearable device that is capable of measuring vital signs
and fall detection events and of notifying care providers
automatically in an emergency situation. Most impor-
tantly, the CAALYX is able to report the current medi-
cal status of the patient together with his or her current
location that helps the emergency team to provide
Security and surveillance applications
Traditional surveillance systems are monitored by
human operators. They should be continuously aware
of the human activities that are observed via the camera
views. An increasing number of camera deployments
and views makes the operators’ work more stressful
and, as a result, leads to decreasing their productivity
levels. As a result, security firms are seeking help from
vision-based technologies to automate the human oper-
ator processes and detect anomalies in camera views.
Table 4 shows the state-of-the-art security and surveil-
lance system applications.
Most of the traditional object recognition methods
depend on the object’s shape, but it is quite challenging
to apply those approaches to a surveillance system
which consists of cluttered, wide-angle, and low-
Therefore, the proposed applications
should be capable of addressing the environmental and
contextual issues such as noise, occlusions, and sha-
dows. The facts that affect an HAR system in an
indoor environment are different from an outdoor
environment. As an example, an approach used inside
a bank may not be applicable to a crowded place such
as a metro and an airport. Most importantly, those
approaches should be robust and capable of working
under real-world application conditions.
The surveillance system introduced in Bre
is based on the Video Surveillance Interpretation
Platform (VSIP) and is able to recognize human beha-
vior such as fighting and vandalism events occurring in
a metro system using one or several camera views.
Additionally, as shown in Chang et al.,
was able to detect and predict the suspicious and
aggressive behaviors of a group of individuals in a
prison. The researchers used multiple camera views to
detect situations such as loitering, distinct groups, and
aggression scenarios in real time and in a crowded envi-
ronment. The airport surveillance system proposed by
Fusier et al.
is able to recognize 50 types of events
including complex activities such as baggage unloading,
aircraft arrival preparation, and refueling operation.
Table 3. State-of-the-art healthcare monitoring applications.
Author Keywords Sensor information used
Yaniv Zigel et al.
Acoustic signal processing, fall detector, feature
extraction, pattern recognition
Floor vibration and sound sensing
Qiang Li et al.
Fast fall detection, activities of daily living (ADL) Accelerometers and gyroscopes
Derek Anderson et al.
Activity analysis, fuzzy logic, fall detection,
Maarit Kangas et al.
Elderly, independent living, movement analysis Accelerometers
M Lustrek and B Kaluza
Activity recognition, machine learning, body part Video, infrared motion capture sensors, and so
AK Bourke and GM Lyons
Falls in the elderly, fall detection, gyroscope,
Bi-axial gyroscope sensor
Chao Wang et al.
Healthcare monitoring, near-threshold
operation, reconfigurable computing
Minh-Thanh Vo et al.
Wireless sensor network (WSN), healthcare
Light-to-Frequency (LTF), infrared (IR) sensors,
healthcare monitoring wireless sensors, and so
Ranasinghe et al. 5
TI is an approach that allows users to share their pres-
ence in a virtual environment and interact with each
other in real time such as being present in the same
physical but in different geographical environments.
These applications require a higher amount of com-
puter processing power and generate a large amount of
data that need to be transferred through a network in
Usually, compression methods are applied to reduce
the amount of data to be transferred. For example, the
multi-camera TI system
can extract the kinematic
parameters of a human body in each frame from cloud
data using motion estimation, and thus, significantly
minimize the network transfer between the remote sites.
Furthermore, three-dimensional (3D) videoconferen-
cing applications successfully address the hardware
bottlenecks emerging from complex computational
algorithms in real-time 3D video conferences.
Similarly, the i3DPost project
used TI techniques to
enhance the existing appearance of two-dimensional
(2D) layouts (http://www.i3dpost.eu/) by converting
2D computer-aided draft (CAD) planning) into full
color motion-rendered pictures.
Table 5 shows the state-of-the-art TI applications.
AR systems and approaches
Advances in visual and sensor technology enabled AR
systems to be widely used in daily life. During the last
decade, scientists have taken various approaches to recog-
nize the human behavior in many application domains. In
this review, we categorize the AR systems based on their
design methodology, mainly taking into account the data
collection process. As a result, this section is divided into
subsections dedicated to visual sensor-based systems, non-
visual sensor-based systems, and multimodal systems.
Each approach is then discussed with regard to its usage
under different categories.
Visual systems for AR
Identification of human activities using visual sensor
networks is one of the most popular approaches in the
computer vision research community. In early stages of
visual recognition, systems were categorized into groups
such as hand gesture recognition for human–computer
interfaces, facial expression recognition, and human
behavior recognition for video surveillance.
The major difference between visual sensors and
other sensor types is the way of perceiving the informa-
tion in an environment. Most sensors provide the data
as a one-dimensional data signal, whereas the visual
sensors provide the data as a 2D set of data which is
seen as images.
There are various types of visual-
based AR approaches. Therefore, we organize this sub-
section along four categories: (1) visual AR systems for
active and assisted living and smart home systems, (2)
visual AR systems for healthcare monitoring systems,
(3) visual AR systems for security and surveillance sys-
tems in public areas, and (4) visual AR systems in
sports and outdoors.
Visual systems for active and assisted living and smart home
systems. Given the visual sensor usage in indoor envir-
onments, active and assisted living systems provide resi-
dents with supervision and assistance to ensure their
well-being. AAL systems should be easily deployable,
robust systems which are capable of assisting users
Table 4. State-of-the-art security and surveillance applications.
Author Keywords Sensor information used
Jun-Wei Hsieh et al.
Behavior analysis, event detection, string matching Camera views
Umut Akdemir et al.
Activity ontologies, visual surveillance Camera views
C Fookes et al.
Supervised learning, multi-camera network, event detection Multi-camera views
Frdric Dufaux et al.
Privacy, selective encryption, surveillance, video processing Camera views
Nils Krahnstoever et al.
Multi-camera view, real time, detection, and tracking algorithms Multi-camera views
Background model, entropy, morphology, motion detection,
L Maddalena and A Petrosino
Background subtraction, motion detection, neural network, self-
organization, visual surveillance
Liyuan Li et al.
Mean-shift tracking, multi-object tracking, occlusion, video
Donato Di Paola et al.
Autonomous mobile robot, RFID tags, surveillance applications Video, RFID tags, laser scene
Zheng Xu et al.
Video-structured description, surveillance videos, public security Video
Evgeny Belyaev et al.
Vehicular communication, video surveillance, 3D discrete wavelet
RFID: radio-frequency identification; 3D: three-dimensional.
6International Journal of Distributed Sensor Networks
Fosty et al.
presented an RGB-D camera monitor-
ing system for event recognition which uses a hierarchi-
cal model-based approach. The aim of the approach is
to recognize physical tasks that are evaluated depend-
ing on the patients with dementia. The extraction of
complex events from video sequences is carried out by
combining RGB-D data stream with the corresponding
tracking information, the contextual objects (zones or
pieces of equipment), and the event models.
Experimental results indicated 95.4% accuracy rate for
the events such as balance test, walking test, repeated
transfer test, and up-and-go tests.
Xia et al.
presented a human action recognition
approach for an indoor environment based on histo-
grams of 3D joint locations (HOJ3D), as a compact
representation of postures. The researchers extracted
the 3D skeletal joint locations from Kinect depth maps
using Shotton et al’s.
method. Then, they re-projected
the computed HOJ3D from the action depth sequences
using Linear Discriminant Analysis (LDA) and clus-
tered them into k posture visual words which represent
the prototypical poses of actions. The temporal evolu-
tions of those visual words are modeled by discrete hid-
den Markov models (HMMs). The special data set has
been collected which consists of 10 types of human
actions (i.e. walk, sit down, and stand up) in an indoor
setting. Experimental results show 90.92% overall accu-
racy rate for action types such as walk, sit down, stand
up, pick up, carry, throw, push, pull, wave, and clap
With the purpose of handling uncertainty,
Romdhane et al.
described a complex event recogni-
tion approach with probabilistic reasoning. The estima-
tion of a primitive state’s probability is based on the
Bayesian process model and computes the confidence
of a complex event as Markov process considering the
probability of the event at a previous time. The pro-
posed event recognition algorithm uses the tracked
mobile objects as input (extracted by vision algorithms,
segmentation, detection, and tracking), a priori knowl-
edge of the scene, and predefined event models. After
calculating the probability of an event, the system can
make a recognition decision by accepting events with a
probability above a threshold or rejecting them.
Experimental results present a higher accuracy for
recognizing indoor events: 92.59% for up-and-go event,
100% for beginning guided test, 100% for interacting
with a chair, 93.3% for staying at kitchen, and 87.5%
for preparing a meal event.
Hartmann et al.
proposed a robust and intelligent
vision system which detects a person who is staying or
lying on the floor. The system uses only one fisheye
camera which is located in the middle of the room. The
solution is based on image segmentation using
Gaussian mixture models to detecting moving residents
and then analyzing their main and ideal orientation
using image moments. It has a low latency and a detec-
tion rate of 88%.
Visual systems for healthcare monitoring systems. Although
visual sensor-based approaches are not popular in health-
care monitoring systems, these techniques are widely
used for implementing fall detection systems, particularly
to take care of patients who suffer from diseases such as
dementia and Alzheimer. Having in mind the visual-
based healthcare monitoring approaches, Foroughi et
proposed a fall detection system combined with inte-
grated time motion images (ITMI) and eigenspace tech-
niques. The proposed system was able to detect a wide
range of daily life activities including abnormal and
unusual behaviors. The researchers extracted the eigen-
motion space and classified the motion using multilayer
perceptron (MLP) neural network to decide on a fall
event. This system showed a reliable average recognition
rate of 89.99% as their final result.
An intelligent monitoring system proposed by Chen
monitors the ‘‘elopement’’ events of dementia
Table 5. State-of-the-art tele-immersion applications.
Author Keywords Used sensor information
G Kurillo and R Bajcsy
3D video, 3D tele-immersion, human–computer
interaction, remote collaboration, telepresence
16–48 VGA cameras (640 3480 pixels)
C Zhang et al.
Teleconferencing, 3D video processing, 3D video
rendering, 3D audio processing
Three infrared (IR) cameras, three color
XGA cameras, and one Kinect
B Petit et al.
Multi-camera, real time, 3D modeling, telepresence Eight 1-megapixel cameras
Z Huang et al.
3D tele-immersion, synchronization 3D video camera
Kurillo et al.
Tele-immersion, rehabilitation, tele-rehabilitation, lower
3D video stream
Chun-Han Lin et al.
Virtual collaboration, motion-sensing techniques, gesture-
3D motion sensor data
Yunpeng Liu et al.
Depth map, compression, 3D tele-immersion Depth image streams
H Kim et al.
Big data management, multimodal data registration, film
2D/3D video data
2D: two-dimensional; 3D: three-dimensional.
Ranasinghe et al. 7
units and is able to automatically detect such events
and alert the caregivers. The monitoring system uses a
camera network to collect the audio/visual records of
daily activities. Using an HMM-based algorithm, the
authors were able to detect elopement events from the
collected data. Experimental results demonstrate that
the proposed system was able to successfully detect elo-
pement events with almost 100% accuracy.
Visual systems for security and surveillance systems in public
areas. Security and surveillance systems have used
visual processing approaches extensively to track
human behaviors in public environments. Visual
sensor-based techniques are the most suitable
approaches for implementing such systems because of
the valid evidential proofs which can be provided by
videos and images due to their nature.
Zaidenberg et al.
proposed an approach to
Scenario Recognition based on knowledge (ScReK)
framework model to automatically detect the behavior
of a group of people in a video surveillance application.
It keeps track of individuals moving together by main-
taining spatial and temporal group coherence in a video
stream. The proposed framework models the semantic
knowledge of the objects of interest and scenarios to
recognize events associated with the detected group
based on spatiotemporal constraints. At the beginning,
people are individually detected and tracked. Then,
their trajectories are analyzed over a temporal window
and clustered using the mean-shift algorithm. The
obtained coherence value describes the activity per-
formed by the group. Furthermore, the researchers have
proposed a description language for formalizing events.
The group event recognition approach has been success-
fully validated using three data sets collected from four
cameras (an airport, a subway, a shopping center corri-
dor, and an entrance hall): group events such as fight-
ing, split up, joining, entering and exiting the shop,
browsing, and getting off a train have been successfully
detected with low false positive and false negative rates.
In the context of visual surveillance of metro scenes,
Cupillard et al.
proposed an approach for recognizing
isolated individuals, groups of people, or crowded
environments using multiple cameras. The functionality
of the proposed vision module is composed of three
tasks: (1) motion detection and frame-to-frame track-
ing, (2) combining multiple cameras, and (3) long-term
tracking of individuals, groups of people, and crowd
evolving in the scene. For each tracked actor, the beha-
vior recognition module performs reasoning on three
levels: states, events, and scenarios. Also, the authors
defined a general framework to combine and tune vari-
ous recognition methods (e.g. automaton, Bayesian net-
work, or AND/OR tree) that are dedicated to analyzing
specific situations (e.g. mono/multi-actor activities,
numerical/symbolic actions, or temporal scenarios).
This method was able to successfully recognize the sce-
narios like ‘‘Fraud’’ 6/6 (6 times out of 6), ‘‘Vandalism’’
4/4, ‘‘Fighting’’ 20/24, ‘‘blocking’’ 13/13, and the sce-
nario ‘‘overcrowding’’ 2/2.
Nievas et al.
proposed bag-of-words framework to
detect fight events using Space–Time Interest Points
(STIP) and Motion SIFT (MoSIFT) action descriptors.
They have introduced a new video database containing
1000 sequences that are grouped as fights and non-
fights for evaluation purposes. Experimental results
with this video database detected fight events from
action movies with an accuracy of nearly 90%.
Visual systems in sports and outdoors. Computer vision
techniques can also be used to recognize sport activities
to enhance the performance of players and analyze the
game plan. Direkoglu and O’Connor
method to analyze an entire playground of team activi-
ties of a handball game. Frame differencing and optical
flow methods have been used to extract the motion fea-
tures and recognize the sequence of position distribu-
tion of the team players. The proposed approach was
evaluated using the European handball data set and is
able to successfully identify six different team activities
in a handball game, namely, slowly going into offense,
offense against setup defense, offense fast break, fast
returning into defense to prevent fast break, slowly
returning into defense, and basic defense.
Action bank presented by Sadanand and Corso
bined a large set of action detectors that represent a high-
dimensional ‘‘action-space’’ with a linear classifier to
arrange a semantically rich representation for AR. The
action bank is inspired by the object bank method
mines a high level of human action in a video. The authors
have tested the action bank with popular data sets and
achieved improved performances for various data sets:
98.2% for the Kungliga Tekniska Ho
Royal Institute of Technology, data set (better by 3.3%),
95.0% for the University of Central Florida (UCF),
Sports data set (better by 3.7%), 57.9% for the UCF50
data set (baseline is 47.9%), and 26.9% for the HMDB51
data set (baseline is 23.2%).
Recognizing activities in noisy videos, that is, videos
consisting of blurred motions, occlusions, missing
observations, and dynamic backgrounds, is quite chal-
lenging. Using probabilistic event logic (PEL) interpre-
tation, Brendel et al.
were able to understand events
and time intervals of a basketball video which consisted
of noisy data. They have successfully identified the bas-
ketball events, namely, dribbling, jumping, shooting,
passing, catching, bouncing, and so on.
Tang et al.
proposed a method to identify complex
events in video streams by utilizing a conditional
model. The model is trained in a max-margin
8International Journal of Distributed Sensor Networks
framework to automatically detect discriminative and
interesting segments in a video. It competitively
achieved a higher accuracy for detecting and recogniz-
ing difficult tasks. The latent variables over the video
frames have been introduced to discover and assign the
most discriminative sequence of states for the event.
This model is based on a HMM which tracks the model
durations of states in addition to the transitions
between states. Accordingly, experimental results
acquired using the Multimedia Event Detection (MED)
Transparent Development (DEV-T) data set have
showed a 15.44% rate for attempting a board trick,
3.55% for feeding an animal, 14.02% for landing a
fish, 15.09% for a wedding ceremony, and 8.17% for
working on a woodworking project.
Crispim and Bremond
proposed a probabilistic
framework to handle the uncertainty of a constraint-
based ontology framework for event detection. The
uncertainty modeling constraint-based framework is
monitored by a RGB-D sensor (KinectÒ, MicrosoftÓ)
vision system. The RGB-D monitoring system is com-
posed of three main steps: people detection, people
tracking, and event detection. The people detection step
is performed by a depth-based algorithm proposed by
Nghiem et al.
The detection is evaluated by a multi-
feature tracking algorithm proposed by Chau et al.
The event detection step uses a set of tracked people
generated in the previous step and a priori knowledge
of the scene provided by a domain expert. Experimental
results showed that the uncertainty modeling improves
the detection of elementary scenarios in recall (e.g. in
zone ‘‘phone’’: 85%–100%), the precision indices (e.g.
in zone ‘‘reading’’: 54.71%–73.15%), and the recall of
Touati and Mignotte
introduced a set of prototypes
that are generated from different viewpoints of the video
sequence data cube to recognize human actions. The pro-
totypes are generated using a multidimensional scaling
(MDS) based on a nonlinear dimensionality reduction
technique. This strategy aims at modeling each human
action in a low-dimensional space as a trajectory of
points or a specific curve for each viewpoint of the video
cube. Then, a k-Nearest Neighbors (K-NN) classifier is
used to classify the prototype, for a given viewpoint,
associated with each action to be recognized. Fusion of
classification results of each viewpoint has been used to
improve the recognition rate performance. The overall
performance showed a 92.3% accuracy rate to recognize
activities such as walking, running, skipping, jacking,
jumping, siding, and bending.
AR systems using non-visual sensors
Besides visual-based analyzing, researchers have made
many other attempts such as analyzing human voice
streams and sensor-based detection to automatically detect
human behavior. There are various types of sensors that
can be used for AR, ranging from simple sensors such as
ball switches and radio-frequency identification (RFID)
tag readers to accelerometers which are used for complex
audio processing and computer vision sensing. Also, other
sensors such as fiber optical sensors for posture measur-
ing, foam pressure sensors for respiration rate measuring,
and physiological sensors such as oximetry sensors, skin
conductivity sensors, electrocardiographs, and body tem-
perature sensors can be included.
Non-visual AR systems in active and assisted living and smart
home systems. Smart home technologies use various
types of sensors which provide light, sound, contact,
motion, and state-change information of the residents
to determine their behaviors. Fleury et al.
a methodology regarding the classification of daily liv-
ing activities in a smart home using support vector
machines. The proposed system was able to identify
seven activities, namely, hygiene, toilet use, eating, rest-
ing, sleeping, communication, and dressing/undressing,
using location sensors, microphones, wearable sensors,
temperature, and hygrometry sensors.
Challenges of most smart home approaches are the
inconsistency and unreliability of the sensors which
misread the actual information. Hong et al.
duced a framework to deal with such uncertainty by
combining the reliability level of each sensor with the
overall decision-making process.
Fleury et al.
have presented an ADL (smart
home) classification which is based on support vector
machines. They have deployed sensors such as infrared
presence sensors, door contacts, temperature and
hygrometry sensors, and microphones in a smart home
and collected the data of the residents (young individu-
als) to identify the residents’ daily living activities. The
researchers were able to successfully identify seven
activities: hygiene, toilet use, eating, resting, sleeping,
communication, and dressing/undressing with a 75%
of classification rate for the polynomial kernel and
86% classification rate for the Gaussian kernel.
Most of the AAL and smart home approaches made
use of the annotated data labeled with the correspond-
ing activities. Szewcyzk et al.
have proposed a
mechanism to annotate sensor data with correspondent
activity labels; thus, they were able to achieve a higher
accuracy in recognizing daily activities such as sleeping,
eating, personal hygiene, preparing a meal, working at
computer, watching TV, and others. The average accu-
racy of the models was increased from 66.35% to
75.15%. In general, annotating sensor data with activi-
ties is time-consuming and may require the assistance
of the smart home residents.
Non-visual AR systems in other indoor environments. Apart
from smart home systems, other sensor-based indoor
Ranasinghe et al. 9
systems have been proposed. As an example, Viani et
presented a Learning-by-Example (LBE)
approach which is able to track and localize passive
targets of an indoor non-infrastructure environment.
This approach used the interaction between targets and
wireless links, so that the proposed strategy does not
need any additional use of radio devices or specific sen-
sors to track the objects. Furthermore, Viani et al.
introduced a system to detect theft attempts in an
indoor museum. This system was able to monitor the
artworks inside the museum and to estimate the visitor
behaviors using its wireless sensor network (WSN).
Multi-sensors available in a WSN gathered all the
information in a central control unit and processed the
data in real time to assist the museum authorities,
which enables them to give immediate feedback when-
Wang et al.
proposed a CSI-based human Activity
Recognition and Monitoring (CARM) system which
consists of CSI-speed model (to measure the correlation
between CSI value dynamics and human movement
speeds) and CSI-activity model (to measure the correla-
tion between the movement speeds of different human
body parts and a specific human activity). CARM was
able to detect human activities, namely, running, walk-
ing, sitting down, opening refrigerator, falling, boxing,
pushing one hand, brushing teeth, and empty (i.e. no
activity) with 96% average accuracy. Also, CARM has
been evaluated in different environments, namely, lab,
lobby, office, and apartment, and achieved an average
accuracy of 90%, 93%, 83%, and 80%, respectively.
Multimodal sensors for AR approaches
Multimodal AR approaches are becoming popular in
the last decade. They use visual and non-visual sensors
at the same time to recognize human activities.
Depending on the user requirements, the type of the
model and the usage of sensors may differ. For exam-
ple, one camera would be able to cover a wide area of a
particular context, but it may not be enough to analyze
sensitive data such as temperature of the environment,
humidity, and user information like the heart rate.
Clearly, systems using a single modality sensor
approach would not perform well in situations where
such different kinds of input data are needed. To over-
come these limitations,
multi-sensor modality is
introduced by exploiting various kinds of sensors in the
same recognition system using, for example, multi-sen-
sor/camera networks combined with body-worn sen-
sors and mobile devices.
Multimodal systems in the AAL and smart home
domain. Considering the privacy concerns of residents,
sensor-based technologies are more widely used in
AAL domains as compared to video-based approaches.
Vacher et al.
address the problem of learning and
recognizing ADLs in smart homes using (hierarchical)
hidden semi-Markov models. They have introduced a
model using typical duration patterns and inherently
hierarchical structures that can learn the behavior of a
resident during the day. Captured activities are classi-
fied and segmented to detect the abnormal behaviors of
Chen et al.
introduced a knowledge-driven
approach to assist smart homes using multi-sensor data
streams. This approach has been evaluated using vari-
ous ADLs and user scenarios. A 94.44% average recog-
nition rate has been achieved for recognizing activities
such as making tea, brushing teeth, making coffee, hav-
ing bath, watching TV, making chocolate, making
pasta, and washing hands. The average time for recog-
nizing an operation was 2.5 s.
Brdiczka et al.
presented an approach for a smart
home environment capable of learning and recognizing
human behaviors from multimodal observation data. A
3D video tracking system has been used for tracking
the entities (persons) of a scene and a speech activity
detector to analyze audio streams of each entity. The
system was able to identify basic individual activities
such as ‘‘walking’’ and ‘‘interacting with table.’’ Based
on this individual information, group situations such as
aperitif, presentation, and siesta were identified.
Chahuara et al.
presented an application to recog-
nize ADLs in a smart home using Markov Logic
Networks (MLN). Sensor raw data from non-visual and
non-wearable sensors are used to create the classification
model. The usage of a formal domain knowledge
description and a logic-based recognition method led to
a higher re-usability of the trained model from one home
to another. MLN achieved 85.3% overall accuracy for
the events such as eating, tidying up, hygiene, communi-
cating, dressing up, sleeping, and resting.
Vacher et al.
presented SWEET-HOME, a project
that aims at a new smart home system based on audio
The developed system detects the dis-
tress situations of a person and provides assistance via
natural man–machine interaction (voice and tactile
commands), and security reassurance anytime, any-
where in the house. The system uses the Dedicated
Markov Logic Network (DMLN) for domain knowl-
edge representation to handle the sensor data uncer-
tainty. During the experiment, the participants took
part into four different scenarios: feeding (preparing
and having a meal), sleeping, communicating (with spe-
cialized e-lio device; http://www.technosens.fr/), and
resting (listening to the radio). The experimental results
showed an accuracy of 70% for all four scenarios.
Multimodal systems in the healthcare domain. Multimodal
sensors are deployed or used as observation tools to
10 International Journal of Distributed Sensor Networks
monitor the health condition of patients. Maurer et
introduced ewatch, a multi-sensor platform that
monitors and recognizes activities of a person using sen-
sory devices deployed at different points of the body. It
identifies user activities in real time and records these
classification results during the day. Then, the multiple
time domain feature sets and sampling rates are com-
pared to analyze the trade-off between recognition
accuracy and computational complexity. Classification
accuracy is calculated for every activity collected from
six different body points (wrist, pocket, bag, necklace,
shirt, and belt). The data from all subjects are finally
combined to train the general classifier. As a result of
the performed evaluation, the collected data indicated
that all six points were good for detecting walking,
standing, sitting, and running activities. Based on these
results, the authors have implemented a decision tree
classifier that runs inside ewatch.
Similarly, Vacher et al.
proposed a multi-
processing system to monitor tasks such as signal detec-
tion and channel selection, sound/speech classification,
life sound classification, and speech recognition tasks
of a patient. Once the system detects an emergency, it
transfers the information to the remote medical moni-
toring application through the network.
Multimodal systems in outdoor environments. Multimodal
sensors are also used by researchers to detect human
activities in outdoor environments. Al Machot et
presented a Smart Resource Aware Multi-
Sensor Network (SRSnet) to automatically detect com-
plex events using ASP. The SRSnet system is based on
audio and video processing components. The system
detects complex events by aggregating simple events
from audio and video data processing. Information
about the detected events and PTZ configuration form
the input for the network subsystem. Detected complex
and simple events are stored in a multimedia data ware-
house. Experimental results of SRSnet showed over
94% detection ratio for the complex events, namely,
group running, fighting, and people running in differ-
Theekakul et al.
presented a rule-based framework
for human activity classification. It consists of two
main components: rule learning and rule-based infer-
ence. Domain-specific knowledge is used together with
sensor data to construct the classification rules. The
knowledge base includes assumptions of activity char-
acteristics, constraints of device packaging design, and
so on. This approach illustrates how the domain-
specific knowledge and feature space observation data
can be used for rule construction. The orientation and
baseline rules were applied for training and testing pro-
cesses. Test data sets successfully detected lying, sitting
and standing, walking, running, and jumping activities.
The detection was performed with an accuracy of
76.43% and 74.46% for the training and test data sets,
Mobile-based multimodal systems. Mobile devices have
recently become sophisticated, and most of the today’s
mobile devices are equipped with powerful sensors,
such as Global Positioning System (GPS), audio (i.e.
microphones), image (i.e. camera), temperature, direc-
tion (i.e. compasses), and acceleration, and provide a
significant computing power. Consequently, they open
a wide area for researchers to find innovative solutions
Lara et al.
presented Centinela, a mobile platform
that combines mobile acceleration sensor data with
vital signs (e.g. heart rate and respiration rate). This
platform consists of a single sensing device in a mobile
phone, which makes it a portable and unobtrusive real-
time data collection platform. It uses both statistical
and structural detectors while introducing two new fea-
tures, trend and magnitude, to detect the differentiation
of vital sign stabilization during periods of activities.
Centinela was able to recognize five different activities,
namely, walking, running, sitting, ascending, and des-
an HAR application on Android, was
also developed by Lara et al. based on Centinela.
Vigilante uses a library called MECLA to exploit multi-
ple sensing devices which are integrated into a body
area network (BAN). Evaluation results showed that
the application could run up to 12.5 h continuously in
a mobile phone and achieved 92.6% of overall accu-
racy to identify walking, running, and sitting activities.
Kwapisz et al.
introduced a system which uses a
phone-based accelerometer to recognize human activi-
ties. This system is reported to be able to collect labeled
accelerometer data, namely, walking, jogging, climbing
stairs, sitting, and standing events. The results are used
to train a predictive model for recognizing activities. The
experimental results showed recognition rates over 90%.
Similarly, Riboni et al.
presented COSAR, a
framework built on Android for context-aware AR.
This framework uses ontologies and ontological rea-
soning methods, combined with statistical inferencing.
The structured symbolic knowledge about the environ-
ment surrounding the user allows the system to success-
fully identify a particular user activity. Integrating
ontological reasoning with statistical methods demon-
strated an overall accuracy of 92.64% which is higher
than that of other pure statistical methods.
Popular data sets
In this section, we outline some publicly available data
sets currently used in the HAR research community for
Ranasinghe et al. 11
evaluation and comparison purposes. We divide the
section into subsections describing video-based and
non-video-based data sets.
Video-based data sets
KTH data set. KTH video database
consists of clips
showing sequences like walking, running, jogging,
hand-waving, boxing, and hand-clapping actions and
activities (Figure 2). These video sequences are per-
formed by 25 persons in different locations and settings,
for example, outdoor, outdoor with scale variation,
outdoors with different clothes, and indoors. The cur-
rent KTH database consists of 2391 sequences; all
sequences were collected in similar backgrounds with a
25 fps frame rate.
Weizmann data set. The Weizmann data set
of 10 action classes, namely, walk, run, jump, gallop
sideways, one hand wave, two hands wave, bend, skip,
jump in place, and jumping jack (Figure 3). The data
sequences were captured in similar outdoor back-
grounds that consist of irregular versions (with dog,
occluded, with bag, etc.) to be used in robustness
experiments for the ‘‘walk’’ activity.
INRIA Xmas Motion Acquisition Sequences multi-view data
set. INRIA Xmas Motion Acquisition Sequences
(IXMAS) data set was introduced by Weinland et al.
It contains 14 actions, namely, check watch, cross arms,
scratch head, sit down, get up, turn around, walk,
wave, punch, kick, point, pick up, throw overhead, and
throw from bottom up (see Figure 4). Actions were
captured using five cameras. These actions were per-
formed by 11 persons and captured three times from
each person. The camera viewpoints were fixed and set
up with static background and illumination settings.
Figure 2. Examples of KTH data set.
Figure 3. Examples of Weizmann data set.
12 International Journal of Distributed Sensor Networks
UCF sport data set. The UCF sport data set
of nearly 200 sports action videos that are collected
from broadcast television channels at a resolution of
720 3480 and show scenes like diving, golf swinging,
lifting, kicking, horseback riding, skating, running,
swinging, and walking (Figure 5).
YouTube action data set. This data set consists of the
videos divided into 11 action categories, namely, bas-
ketball shooting, biking/cycling, diving, golf swinging,
horseback riding, soccer juggling, swinging, tennis
swinging, trampoline jumping, volleyball spiking, and
walking with a dog (Figure 6). These videos include
large variations of camera motions, for example, object
scale and viewpoints, object appearance and poses,
cluttered backgrounds, and illumination conditions.
i3DPost multi-view human action data sets. This data set
was created in the framework of the i3DPost project
and contains synchronized/uncompressed-HD, 8-view
image sequences of 13 actions performed by 8 people
(104 in total). It consists of scenes of the following
activities: walking, running, jumping, hand-waving,
jumping in place, sitting–standing up, running–falling,
walking–sitting, running–jumping–walking, handshak-
ing, pulling, and performing facial expression actions
(see Figure 7). Additionally, it provides background
images for camera calibration parameters to recon-
struct 3D mesh models.
MOBISERV-AIIA database. This data set is specialized to
train machine learning models for recognizing ‘‘having
a meal’’ activities, including eating and drinking (see
Figure 8). MOBISERV video sequences were recorded
with a resolution of 640 3480 pixels using 12 persons
(6 females and 6 males) aging between 22 and 39 years
Figure 4. Examples of IXMAS multi-view data set.
Figure 5. Examples of UCF sport data set.
Ranasinghe et al. 13
with different facial characteristics, for example, related
to eyes, glasses, and beard. In this data set, eight videos
were recorded with four distinct ‘‘having a meal’’ ses-
sions with different cloths for each person. In total, the
database consists of 384 video sequences.
IMPART data sets. The IMPART multimodal/multi-view
consists of multimodal data footage and
3D reconstructions of various indoor/outdoor scenes,
for example, LiDAR scans, digital snapshots of
reconstructed 3D models, spherical camera scans, and
reconstructed 3D models. Additionally, it provides
multi-view video sequences of actions in indoor and
outdoor environments using different types of cameras,
for example, fixed multiple HD camera sequences
(360°/120°setup), free-moving principal HD camera,
Figure 6. Examples of YouTube action data set.
Figure 7. Examples of i3DPost multi-view human action data set.
14 International Journal of Distributed Sensor Networks
nodal cameras, and multi-view facial expression cap-
tures. The provided facial expressions are categorized
as, for example, Neutral—Anger—Fear—Happiness—
Sadness—Surprise (see Figure 9).
Non-video-based data sets
CASAS. The CASAS data set
has been introduced by
the Washington State University, CASAS, as a part of
the CASAS smart home project. Five sequences of
activities, namely, using telephone, washing hands, pre-
paring and eating meals, using medication, and clean-
ing were asked to be performed by participants, and
relevant sensor information was collected using motion,
temperature, water, burner, phone usage (for com-
pleted calls), and item sensor readings (see Figure 10).
Benchmark (Van Kasteren) data set. This data set
sists of four data sets which record several weeks of
human behaviors inside their homes. Reed switches,
pressure mats, mercury contacts, passive infrared, and
float sensors were used to collect the data. Activities
such as leaving, toileting, showering, brushing teeth,
sleeping, having breakfast, preparing dinner, preparing
snacks, and drinking were annotated for each data set.
Sweet-Home data sets. The SWEET-HOME multimodal
corpus data set
was recorded by observing individu-
als performing ADLs in a fully equipped smart home
using microphones and home automation sensors. This
multimodal corpus consists of three data sets, namely,
multimodal subset, home automation speech subset, and
interaction subset. Initially, the model was trained using
the data collected from 16 persons (who were healthy
and had no disabilities) to recognize the human beha-
viors automatically. Then, the automatic voice com-
mand recognition system was developed using the
home automation speech subset (audio) data. The
audio data set was recorded using microphones which
were placed at distant locations, for example, on ceil-
ings (no body-worn microphones had been used).
Finally, the interaction data subset was recorded based
on the observations of user interactions of 11 persons
(6 elder persons and 5 visually impaired persons) dur-
ing the Sweet-Home system evaluation.
Discussion of HAR approaches
In the last decades, the enthusiasm for the vision-based
technology has emerged rapidly. Recent advances in
computer vision and sensor device technologies have
assisted researchers to address various real-world HAR
problems, but there are still important issues that need
to be further investigated.
As it becomes clear from this survey, there is a high
demand for existing and upcoming AR solutions.
Scientists have investigated different types of sensors
depending on available user contexts and research
requirements. For example, most of the security and
surveillance solutions were developed using video cam-
eras because of the obvious advantage that camera
observations can be immediately used as valid eviden-
tial proofs. On the other hand, considering a patient
monitoring system, it would be more effective and
guarantee higher privacy to use body-worn sensors
over a visual-based approach, since such sensors pro-
vide more accurate physiological data than can be
obtained from visual data. However, forcing an old or
disabled person to wear such body-disturbing and
Figure 8. Examples of MOBISERV-AIIA data set.
Figure 9. Examples of multimodal/multi-view data set—multi-view facial expression capture.
Ranasinghe et al. 15
intrusive sensors is still a controversial issue. According
to the survey by Ni et al.,
the acceptance does not
only rely on people’s capabilities and limitations, it also
depends on the personal, socioeconomic, and cultural
contexts as well. To avoid such barriers, the authors
suggested technology designers to pay attention to
human factor-related functionalities before starting the
development of such devices. Introducing design pat-
terns and low-level design guidelines would be helpful
for the developers to easily customize the devices for
each specific application, person, and context. Most of
the emergency or fall detection systems are designed to
trigger a response to abnormal user behavior. For
avoiding the triggering of false alarms, Young and
developed an interface, the Personal
Emergency Response System (PERS), which aimed at
recognizing the keywords and seniors’ speech. PERS
was able to identify high-risk emergencies successfully
while ignoring false alarm situations.
A study of Rialle et al.
carried out with 270 fami-
lies, and dealing with their user perceptions of 14 inno-
vative technologies, showed that smart home
technologies allow caregivers to leave the nursing home
without compromising the patients’ safety. This fact
was highly appreciated by both, patients and care-
givers. Consequently, the design of smart home tech-
nologies has to take user aspects comprehensively into
account to become acceptable for the future.
TI systems and gaming interface applications use
multiple camera views to identify human interactions
and simulate them within a virtual environment.
Although TI systems require higher processing power
due to their nature, cameras can hardly be replaced by
other sensory devices.
A single camera can replace multiple sensor deploy-
ments in an AAL environment as it tracks a wide angle
of an environment and captures all the environmental
information at once. Nevertheless, using camera obser-
vations may not be the most suitable option for AAL
environments considering the user privacy and
acceptance issues. Also, it is quite challenging to iden-
tify complex user activities using visual-based
approaches considering the need of higher computer
processing power for the analysis. Non-visual fall
detection experiments within the AAL context showed
higher recognition rates than experiments using visual-
based approaches. In general, for AAL purposes, non-
visual-based HAR approaches are preferable.
A large set of sensor network can track the informa-
tion of simple human activities in detail. This informa-
tion can later be combined using computer fusion
algorithms to detect the complex behavior of a particu-
lar AAL resident. In order to achieve highly accurate
results, it is quite essential to deploy a sufficient amount
of sensors inside the desired environment, for example,
sensor placed in every door at a smart home and every
daily used equipment. Otherwise, final predictions may
mislead to false results. On the other hand, deploying
and maintaining such a sensor network is quite challen-
ging and expensive. Therefore, multimodal approaches
might be a good choice, when the obtrusive compo-
nents are embedded and used carefully and consciously;
for example, body-worn sensors and mobile phone sen-
sors have been used to detect such situations and
showed higher success results.
Patient monitoring systems benefit from using sensor-
based solutions; however, acceptance aspects have to be
carefully considered; moreover, wearing a sensor kit for a
long time may not be comfortable for the user.
Most of the visual sensor-based equipment is sensi-
tive to light/brightness factors of the environment;
higher brightness or lower brightness image streams
could possibly lack information sufficient for the classi-
fiers to identify human behavior. Similarly, sensor-
based approaches suffer from sensor robustness issues,
that is, some sensors may not automatically get acti-
vated by the predefined user behavior due to a mal-
function. Also, some of the sensors may automatically
be activated even without human interaction due to
weather conditions such as lightning, wind, and rain.
Considering the performance factors, visual sensor-
based approaches require a higher amount of computer
processing power to process the data compared to other
approaches due to the complexity of the vision algo-
rithms and the data volumes. In contrast to this, non-
visual sensor approaches perform comparatively faster
and consume less energy. Most of the multimodal but
non-visual algorithms can even run inside an embedded
platform, consuming a low amount of energy. Table 6
shows a comparison.
Conclusion and further research
Most of the existing AR systems are designed and
tested under laboratory conditions. Thus, using such a
system in a real-time scenario would not give a better
Figure 10. Resident ‘‘washing hands’’ (left). This activity
triggers motion sensor ON/OFF events and water flow sensor
16 International Journal of Distributed Sensor Networks
performance if the system is not adapted to the new
environment. However, deploying such an application
in a real environment will not be appropriate unless the
application is tested under real-world conditions which
consist of noise, occlusions, shadows, and other factors.
Human behavior is spontaneous; in particular,
humans tend to do several activities at the same time,
for example, cooking while talking to friends, and
watching TV while eating. Therefore, future HAR sys-
tems should recognize such concurrent activities rather
than focusing only on a single activity at a given time.
Furthermore, some of the human activities may be inter-
leaved, for example, when watching TV and phoning
with a friend in parallel, there could be moments where
you concentrate on the TV program and thus delay
phone answers to your friend. Future HAR systems
should be designed to successfully recognize such inter-
leaved situations. Additionally, HAR systems should
strengthen the handling of uncertainty, that is, avoid
ambiguous behavior interpretations; as an example, the
action opening a refrigerator can belong to several activi-
ties such as cooking and cleaning.
Most of the recently reported experiments were
based on non-visual sensor data collected from a single
user activity. But when considering real-life scenarios,
activities can be performed by multiple users concur-
rently. Recognizing multi-user activities is challenging;
it should incorporate an appropriate amount of sen-
sors, suitable methods to model multi-user interactions,
and filtering useful information from the obtained
data. Carrying out sensor data fusion for such settings
to achieve sufficient accuracy for activity monitoring is
still an open research issue.
Ziefle and Wilkowska
showed that people’s will-
ingness to use medical technology in a case of need out-
weighs negative feedback. However, they state that
most of representatives of the older generation (‘‘early
technical generation’’) expressed higher levels of
Table 6. Comparison of HAR approaches.
Advantages Disadvantages Performance
Single camera can track a wide
angle of an environment
Able to replace many sensory
devices with one camera
Easy to operate
Provide reliable data
Suitable for security and
surveillance systems and tele-
Track only specific details of the
High-sensitive video cameras are
Computer processing power is too
Sensitive for the light/brightness
Higher power consumption to operate
Existing training data sets are not
simulating realistic environments
Require more processing time
Need a higher amount of
power to perform
Processing time is
Large set of sensor-based network
can track every detail of human
behavior including human body
Secure the privacy aspects
compared to video-based solutions
Comparatively less computer
processing power is required
Comparatively low power
consumption to operate
Suitable for healthcare and AAL
Need a large set of sensors, specifically
to track each behavior
Provide unreliable data
One sensor malfunction may lead for
Able to perform faster
even with a smaller
amount of computer
Processing time is
Suitable for detecting complex
Comparatively higher accuracy rate
Comparatively low power
consumption to operate
Suitable for healthcare and AAL
Most of the time, target persons need
to carry or wear the smart kit
Require multiple sensors to capture
full body movements
Intrusiveness of wearing single or
Data fusion algorithms may lead to
Able to perform faster
even with a smaller
amount of computer
Processing time is
Ranasinghe et al. 17
aloofness and distrust against innovative medical solu-
tions. Nevertheless, it seems to be a common belief that
older and disabled people should use the upcoming
medical devices in order to be autonomous in their
homes, if there are no alternatives at affordable cost.
Several research groups have published data sets
which were produced by the observation of different
individuals, for different activity classes, and used for
different evaluation methods, as was discussed in section
‘‘Popular data set.’’ When evaluating a new approach, it
is quite essential to use such a publicly open data set and
use them as a benchmark for the evaluation.
Declaration of conflicting interests
The author(s) declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this
The author(s) disclosed receipt of the following financial sup-
port for the research, authorship, and/or publication of this
article: This work was funded by the Klaus Tschira Stiftung
1. Krishnan NC, Juillard C, Colbry D, et al. Recognition
of hand movements using wearable accelerometers. J
Ambient Intell Smart Environ 2009; 1(2): 143–155.
2. Michael J, Grießer A, Strobl T, et al. Cognitive modeling and
support for ambient assistance.In:MayrHC,KopC,Liddle
S, et al. (eds) Information systems: methods, models, and appli-
cations. Berlin and Heidelberg: Springer 2012, pp.96–107.
3. Menschner P, Prinz A, Koene P, et al. Reaching into
patients’ homes—participatory designed AAL services.
Electron Market 2012; 21(1): 63–76.
4. Turaga P, Chellappa R, Subrahmanian VS, et al.
Machine recognition of human activities: a survey. IEEE
T Circ Syst Vid 2008; 18(11): 1473–1488.
5. Aggarwal JK and Ryoo MS. Human activity analysis: a
review. ACM Comput Surv 2011; 43(3): 16.
6. Bashir F, Khokhar A and Schonfeld D. Object
trajectory-based activity classification and recognition
using hidden Markov models. IEEE T Image Process
2007; 16(7): 1912–1919.
7. Leontev AN. Activity, consciousness, and personality.
Englewood Cliffs, NJ: Prentice Hall, 1978.
8. Mayr HC, Al Machot F, Michael J, et al. HCM-L:
domain specific modeling for active and assisted living.
In: Karagiannis D, Mayr HC and Mylopoulos J (eds)
Domain-specific conceptual modeling—concepts, methods
and tools. Berlin: Springer, 2016, pp.527–552.
9. Katz S. Assessing self-maintenance: activities of daily liv-
ing, mobility, and instrumental activities of daily living. J
Am Geriatr Soc 1983; 31: 721–727.
10. Lawton MP and Brody EM. Assessment of older people:
self-maintaining and instrumental activities of daily liv-
ing. Gerontologist 1969; 9: 179–186.
11. Okeyo G, Chen L and Wang H. Combining ontological
and temporal formalisms for composite activity model-
ling and recognition in smart homes. Future Gener Comp
Sy 2014; 39: 29–43.
12. Ni Q, Garcı
´a Hernando AB and de la Cruz IP. The
elderly’s independent living in smart homes: a characteri-
zation of activities and sensing infrastructure survey to
facilitate services development. Sensors 2015; 15(5):
13. Dohr A, Modre-Opsrian R, Drobics M, et al. The Internet
of things for ambient assisted living. In: 2010 seventh inter-
national conference on information technology,LasVegas,
NV, 12–14 April 2010, pp.804–809. New York: IEEE.
14. Hartmann R, Al Machot F, Mahr P, et al. Camera-based
system for tracking and position estimation of humans.
In: 2010 conference on design and architectures for signal
and image processing (DASIP), Edinburgh, 26–28 Octo-
ber 2010, pp.62–67. New York: IEEE.
15. Chaaraoui AA, Padilla-Lo
´pez JR, Ferra
et al. A vision-based system for intelligent monitoring:
human behaviour analysis and privacy by context. Sen-
sors 2014; 14(5): 8895–8925.
16. Romdhane R, Mulin E, Derreumeaux A, et al. Auto-
matic video monitoring system for assessment of Alzhei-
mer’s disease symptoms. J Nutr Health Aging 2012; 16(3):
17. Negin F, Cosar S, Koperski M, et al. Generating unsu-
pervised models for online long-term daily living activity
recognition. In: Asian conference on pattern recognition
(ACPR 2015), November 2015, https://hal.inria.fr/hal-
18. Bilinski P, Corvee E, Bak S, et al. Relative dense tracklets
for human action recognition. In: 2013 10th IEEE inter-
national conference and workshops automatic face and ges-
ture recognition (FG), Shanghai, China, 22–26 April
2013, pp.1–7. New York: IEEE.
19. Van den Heuvel H, Huijnen C, Caleb-Solly P, et al.
Mobiserv: a service robot and intelligent home environ-
ment for the Provision of health, nutrition and safety ser-
vices to older adults. Gerontechnology 2012; 11(2): 373.
20. Goldstone JA. The new population bomb: the four mega-
trends that will change the world. Foreign Affairs 2010,
21. Demiris G, Hensel BK, Skubic M, et al. Senior residents
perceived need of and preferences for smart home sensor
technologies. Int J Technol Assess Health Care 2008;
22. Ram R, Furfari F, Girolami M, et al. UniversAAL: pro-
visioning platform for AAL services. In: Van Berlo A,
Hallenborg K, Corchado Rodrı
´guez JM, et al. (eds)
Ambient intelligence-software and applications. Berlin:
Springer International Publishing, 2013, pp.105–112.
23. Rashidi P and Cook DJ. Keeping the resident in the loop:
adapting the smart home to the user. IEEE T Syst Man
Cy A 2009; 39(5): 949–959.
24. Roy N, Misra A and Cook D. Ambient and smartphone
sensor assisted ADL recognition in multi-inhabitant
smart environments. J Ambient Intell Human Comput
2016; 7(1): 1–19.
18 International Journal of Distributed Sensor Networks
25. Blumendorf M and Albayrak S. Towards a framework
for the development of adaptive multimodal user inter-
faces for ambient assisted living environments. In: Ste-
phanidis C (ed.) Universal access in human-computer
interaction, Intelligent and ubiquitous interaction environ-
ments. Berlin and Heidelberg: Springer, 2009,
26. Lloret J, Canovas A, Sendra S, et al. A smart communi-
cation architecture for ambient assisted living. IEEE
Commun Mag 2015; 53(1): 26–33.
27. Gannapathy VR, Ibrahim AFBT, Zakaria ZB, et al. Zig-
bee based smart fall detection and notification system
with wearable sensor (e-safe). Int J Res Eng Technol 2013;
28. Zhu C and Sheng W. Wearable sensor-based hand ges-
ture and daily activity recognition for robot-assisted liv-
ing. IEEE T Syst Man Cy A 2011; 41(3): 569–573.
29. Zigel Y, Litvak D and Gannot I. A method for automatic
fall detection of elderly people using floor vibrations and
soundproof of concept on human mimicking doll falls.
IEEE T Biomed Eng 2009; 56(12): 2858–2867.
30. Li Q, Stankovic JA, Hanson MA, et al. Accurate, fast fall
detection using gyroscopes and accelerometer-derived
posture information. In: BSN 2009, sixth international
workshop wearable and implantable body sensor networks,
Berkeley, CA, 3–5 June 2009, pp.138–143. New York:
31. Anderson D, Luke RH, Keller JM, et al. Linguistic sum-
marization of video for fall detection using voxel person
and fuzzy logic. Comput Vis Image Und 2009; 113(1):
32. Kangas M, Konttila A, Lindgren P, et al. Comparison of
low-complexity fall detection algorithms for body
attached accelerometers. Gait Posture 2008; 28(2):
33. Wang C, Zhou J, Liao L, et al. Near-threshold energy-
and area-efficient reconfigurable DWPT/DWT processor
for healthcare-monitoring applications. IEEE T Circuits
II 2015; 62(1): 70–74.
34. Doulamis A, Doulamis N, Kalisperakis I, et al. A real-
time single-camera approach for automatic fall detection.
In: International archives of photogrammetry, remote sen-
sing and spatial information sciences, XXXVIII(5), com-
mission V symposium, 2010, pp.207–212, http://
35. Giroux S and Pigot H. An intelligent health monitoring
and emergency response system. In: Giroux S and Pigot
H (eds) From smart homes to smart care: ICOST 2005,
3rd international conference on smart homes and health
telematics, vol. 15, Oxford: IOS Press, 2005, p.272.
36. Poh MZ, McDuff DJ and Picard RW. Non-contact,
automated cardiac pulse measurements using video ima-
ging and blind source separation. Opt Express 2010;
´mond F, Thonnat M and Zuniga M. Video-under-
standing framework for automatic behavior recognition.
Behav Res Methods 2006; 38(3): 416–426.
38. Chang M-C, Krahnstoever N, Lim S, et al. Group level
activity recognition in crowded environments across mul-
tiple cameras. In: 2010 seventh IEEE international confer-
ence advanced video and signal based surveillance (AVSS),
Boston, MA, 29 August–1 September 2010, pp.56–63.
New York: IEEE.
39. Vacher M, Istrate D, Portet F, et al. The sweet-home
project: audio technology in smart homes to improve
well-being and reliance. Conf Proc IEEE Eng Med Biol
Soc 2011; 2011: 5291–5294.
40. Vacher M, Caffiau S, Portet F, et al. Evaluation of a
context-aware voice interface for Ambient Assisted Liv-
ing: qualitative user study vs. quantitative system evalua-
tion. ACM T Access Comput 2015; 7(2): 1–36.
41. Lustrek M and Kaluza B. Fall detection and activity rec-
ognition with machine learning. Informatica 2009; 33(2):
42. Bourke AK and Lyons GM. A threshold-based fall-detec-
tion algorithm using a bi-axial gyroscope sensor. Med
Eng Phys 2008; 30(1): 84–90.
43. Vo MT, Nghi TT, Tran VS, et al. Wireless sensor net-
work for real time healthcare monitoring: network design
and performance evaluation simulation. In: Toi VV and
Lien Phuong TH (eds) 5th international conference on bio-
medical engineering in Vietnam. Berlin: Springer Interna-
tional Publishing, 2015, pp.87–91.
44. Peursum P, West G and Venkatesh S. Combining image
regions and human activity for indirect object recognition
in indoor wide-angle views. In: Tenth IEEE international
conference computer bision, ICCV 2005, Beijing, China,
17–21 October 2005, vol. 1, pp.82–89. New York: IEEE.
45. Fusier F, Valentin V, Bremond F, et al. Video under-
standing for complex activity recognition. Mach Vision
Appl 2007; 18(3–4): 167–188.
46. Michael J and Mayr HC. Creating a domain specific
modelling method for ambient assistance. In: 2015 fif-
teenth international conference advances in ICT for emer-
ging regions (ICTer), Colombo, Sri Lanka, 24–26 August
2015, pp.119–124. New York: IEEE.
47. Costa R, Carneiro D, Novais P, et al. Ambient assisted
living. In: Rodrı
´guez C, Manuel J, Dante T, et al. (eds)
3rd symposium of ubiquitous computing and ambient intel-
ligence 2008. Berlin and Heidelberg: Springer, 2009,
48. Van Kasteren TLM, Englebienne G and Krose BJ. An
activity monitoring system for elderly care using genera-
tive and discriminative models. Pers Ubiquit Comput
2010; 14(6): 489–498.
49. Wu C, Khalili AH and Aghajan H. Multiview activity
recognition in smart homes with spatio-temporal fea-
tures. In: Proceedings of the fourth ACM/IEEE interna-
tional conference on distributed smart cameras, Atlanta,
GA, 31 August–4 September 2010, pp.142–149. New
50. Hsieh JW, Hsu YT, Liao HY, et al. Video-based human
movement analysis and its application to surveillance sys-
tems. IEEE T Multimedia 2008; 10(3): 372–384.
51. Akdemir U, Turaga P and Chellappa R. An ontology
based approach for activity recognition from video. In:
Proceedings of the 16th ACM international conference on
multimedia, Vancouver, BC, Canada, 26–31 October
2008, pp.709–712. New York: ACM.
52. Fookes C, Denman S, Lakemond R, et al. Semi-super-
vised intelligent surveillance system for secure environ-
ments. In: 2010 IEEE international symposium industrial
Ranasinghe et al. 19
electronics (ISIE), Bari, 4–7 July 2010, pp.2815–2820.
New York: IEEE.
53. Dufaux F and Ebrahimi T. Scrambling for privacy pro-
tection in video surveillance systems. IEEE T Circ Syst
Vid 2008; 18(8): 1168–1174.
54. Krahnstoever N, Yu T, Lim SN, et al. Collaborative
real-time control of active cameras in large scale surveil-
lance systems. In: Workshop on multi-camera and multi-
modal sensor fusion algorithms and applications (M2SFA2
2008), 2008, https://hal.inria.fr/inria-00326743/document
55. Huang SC. An advanced motion detection algorithm
with video quality analysis for video surveillance systems.
IEEE T Circ Syst Vid 2011; 21(1): 1–14.
56. Maddalena L and Petrosino A. A self-organizing
approach to background subtraction for visual surveillance
applications. IEEE T Image Process 2008; 17(7):
57. Li L, Huang W, Gu IH, et al. An efficient sequential
approach to tracking multiple objects through crowds for
real-time intelligent CCTV systems. IEEE T Syst Man
Cy B 2008; 38(5): 1254–1269.
58. Di Paola D, Naso D, Milella A, et al. Multi-sensor sur-
veillance of indoor environments by an autonomous
mobile robot. Int J Intell Syst 2010; 8(1): 18–35.
59. Xu Z, Hu C and Mei L. Video structured description
technology based intelligence analysis of surveillance
videos for public security applications. Multimed Tools
Appl 2015; 1–18. DOI: 10.1007/s11042-015-3112-5.
60. Belyaev E, Vinel A, Surak A, et al. Robust vehicle-to-
infrastructure video transmission for road surveillance
applications. IEEE T Veh Technol 2015; 64(7):
61. Al Machot F, Kyamakya K, Dieber B, et al. Real time
complex event detection for resource-limited multimedia
sensor networks. In: 8th IEEE international conference on
advanced video and signal-based surveillance (AVSS),
Klagenfurt, 30 August–2 September 2011, pp.468–473.
New York: IEEE.
62. Al Machot F, Kyamakya K, Dieber B, et al. Smart
resource-aware multimedia sensor network for automatic
detection of complex events. In: 8th IEEE international
conference on advanced video and signal-based surveillance
(AVSS), Klagenfurt, 30 August–2 September 2011,
pp.402–407. New York: IEEE.
63. Theekakul P, Thiemjarus S, Nantajeewarawat E, et al. A
rule-based approach to activity recognition. In: Theera-
munkong T, Kunifuji S, Sornlertlamvanich V, et al. (eds)
Knowledge, information, and creativity support systems.
Berlin and Heidelberg: Springer, 2011, pp.204–215.
64. Rashidi P, Cook DJ, Holder LB, et al. Discovering activ-
ities to recognize and track in a smart environment. IEEE
T Knowl Data En 2011; 23(4): 527–539.
65. Tunca C, Alemdar H, Ertan H, et al. Multimodal wire-
less sensor network-based ambient assisted living in real
homes with multiple residents. Sensors 2014; 14(6):
66. Blasco R, Marco A
´, Casas R, et al. A smart kitchen for
ambient assisted living. Sensors 2014; 14(1): 1629–1653.
67. Chernbumroong S, Cang S, Atkins A, et al. Elderly activ-
ities recognition and classification for applications in
assisted living. Expert Syst Appl 2013; 40(5): 1662–1674.
68. Lien JM, Kurillo G and Bajcsy R. Multi-camera tele-
immersion system with real-time model driven data com-
pression. Visual Comput 2010; 26: 3–15.
69. Feldmann I, Waizenegger W, Atzpadin N, et al. Real-
time depth estimation for immersive 3D videoconferen-
cing. In: Proceedings of 3DTV-conference (3DTV-CON
10), Tampere, 7–9 June 2010, pp.1–4. New York: IEEE.
70. Mekuria R, et al. A 3D tele-immersion system based on
live captured mesh geometry. In: Proceedings of the ACM
multimedia systems conference (MMsys 13), Oslo, Nor-
way, 27 February–1 March 2013, pp.24–35. New York:
71. Gkalelis N, Kim H, Hilton A, et al. The i3DPost multi-
view and 3D human action/interaction. In: CVMP’09,
conference for visual media production, London, 12–13
November 2009, pp.159–168. New York: IEEE.
72. Kurillo G and Bajcsy R. 3D teleimmersion for collabora-
tion and interaction of geographically distributed users.
Virtual Real 2013; 17(1): 29–43.
73. Zhang C, Cai Q, Chou P, et al. Viewport: a fully distribu-
ted immersive teleconferencing system with infrared dot
pattern. Technical report MSR-TR-2012-60, Microsoft
Research, 1 April 2012, pp.1–11.
74. Petit B, Lesage JD, Menier C, et al. Multicamera real-
time 3D modeling for telepresence and remote collabora-
tion. Int J Digital Multimedia Broadcast 2010; 2010:
Article ID 247108 (12 pp.).
75. Huang Z, Wu W, Nahrstedt K, et al. TSync: a new syn-
chronization framework for multi-site 3D tele-immersion.
In: Proceedings of the 20th international workshop on net-
work and operating systems support for digital audio and
video, Amsterdam, 2–4 June 2010, pp.39–44. New York:
76. Vacher M, Lecouteux B, Chahuara P, et al. The Sweet-
Home speech and multimodal corpus for home automa-
tion interaction. In: The 9th edition of the language
resources and evaluation conference (LREC), Reykjavik,
Iceland, 26–31 May 2014, pp.4499–4506, http://www.lrec-
77. Kurillo G, Koritnik T, Bajd T, et al. Real-time 3D ava-
tars for tele-rehabilitation in virtual reality. In: Proceed-
ings of 18th medicine meets virtual reality (MMVR)
conference, Newport Beach, CA, February 2011,
78. Lin CH, Sun PY and Yu F. Space connection: a new 3D
tele-immersion platform for web-based gesture-collabora-
tive games and services. In: 2015 IEEE/ACM 4th interna-
tional workshop games and software engineering (GAS),
Florence, 18 May 2015, pp.22–28. New York: IEEE.
79. Liu Y, Beck S, Wang R, et al. Hybrid lossless-lossy com-
pression for real-time depth-sensor streams in 3D telepre-
sence applications. In: Ho Y-S, Sang J, Ro YM, et al.
(eds) Advances in multimedia information processing—
PCM 2015. Berlin: Springer International Publishing,
80. Kim H, Pabst S, Sneddon J, et al. Multi-modal big-data
management for film production. In: 2015 international
conference on image processing (ICIP), Quebec City, QC,
Canada, 27–30 September 2015, pp.4833–4837. New
20 International Journal of Distributed Sensor Networks
81. Weinland D, Ronfard R and Boyer E. A survey of vision-
based methods for action representation, segmentation
and recognition. Comput Vis Image Und 2011; 115(2):
82. Soro S and Heinzelman W. A survey of visual sensor net-
works. Adv Multimedia 2009; 2009: 1–22.
83. Fosty B, Crispim-Junior CF, Badie J, et al. Event recog-
nition system for older people monitoring using an RGB-
D camera. In: ASROB-workshop on assistance and service
robotics in a human environment, 2013, http://www-sop.
84. Xia L, Chen CC and Aggarwal J. View invariant human
action recognition using histograms of 3D joints. In:
2012 IEEE computer society conference on computer vision
and pattern recognition workshops (CVPRW), 2012,
pp.20–27. IEEE, http://cvrc.ece.utexas.edu/Publications/
85. Shotton J, Sharp T, Kipman A, et al. Real-time human
pose recognition in parts from single depth images. Com-
mun ACM 2013; 56(1): 116–124.
86. Romdhane R, Boulay B, Bremond F, et al. Probabilistic
recognition of complex event. In: Crowley JL, Draper BA
and Thonnat M (eds) Computer vision systems. Berlin and
Heidelberg: Springer, 2011, pp.122–131.
87. Foroughi H, Naseri A, Saberi A, et al. An eigenspace-
based approach for human fall detection using integrated
time motion image and neural network. In: 9th interna-
tional conference on signal processing, ICSP, Beijing,
China, 26–29 October 2008, pp.1499–1503. New York:
88. Chen D, Bharucha AJ and Wactlar HD. Intelligent video
monitoring to improve safety of older persons. Conf Proc
IEEE Eng Med Biol Soc 2007; 2007: 3814–3817.
89. Zaidenberg S, Boulay B and Bremond F. A generic
framework for video understanding applied to group
behavior recognition. In: 2012 IEEE ninth international
conference on advanced video and signal-based surveillance
(AVSS), 2012, pp.136–142, https://hal.inria.fr/hal-
90. Cupillard F, Bremond F and Thonnat M. Behaviour rec-
ognition for individuals, groups of people and crowd,
91. Nievas EB, Suarez OD, Garcia GB, et al. Violence detec-
tion in video using computer vision techniques. In: Real
P, Diaz-Pernil D, Molina-Abril H, et al. (eds) Computer
analysis of images and patterns. Berlin and Heidelberg:
Springer, 2011, pp.332–339.
92. Direkolu C and O’Connor NE. Team activity recognition
in sports. In: Fitzgibbon A, Lazebnik S, Perona P, et al.
(eds) Computer vision—ECCV 2012. Berlin and Heidel-
berg: Springer, 2012, pp.69–83.
93. Sadanand S and Corso JJ. Action bank: a high-level rep-
resentation of activity in video. In: 2012 IEEE conference
on computer vision and pattern recognition (CVPR),Pro-
vidence, RI, 16–21 June 2012, pp.1234–1241. New York:
94. Li LJ, Su H, Fei-Fei L, et al. Object bank: a high-level
image representation for scene classification & semantic
feature sparsification. In Advances in neural information
processing systems, 2010, pp.1378–1386, http://vision.
95. Brendel W, Fern A and Todorovic S. Probabilistic event
logic for interval-based event recognition. In: 2011 IEEE
conference on computer vision and pattern recognition
(CVPR), Providence, RI, 20–25 June 2011, pp.3329–
3336. New York: IEEE.
96. Tang K, Fei-Fei L and Koller D. Learning latent tem-
poral structure for complex event detection. In: 2012
IEEE conference on computer vision and pattern recogni-
tion (CVPR), Providence, RI, 16–21 June 2012, pp.1250–
1257. New York: IEEE.
97. Crispim C and Bremond F. Uncertainty modeling frame-
work for constraint-based elementary scenario detection in
vision system. In: Agapito L, Bronstein MM and Rother
C(eds)European conference on computer vision.Berlin:
Springer International Publishing, 2014, pp.269–282.
98. Nghiem AT, Auvinet E and Meunier J. Head detection
using kinect camera and its application to fall detection.
In: 2012 11th international conference on information sci-
ence, signal processing and their applications (ISSPA),
Montreal, QC, Canada, 2–5 July 2012, pp.164–169. New
99. Chau DP, Bremond F and Thonnat M. A multi-feature
tracking algorithm enabling adaptation to context varia-
tions. In: 4th international conference imaging for crime
detection and prevention 2011 (ICDP 2011), London,
UK, 3–4 November 2011, pp.1–6. IET.
100. Touati R and Mignotte M. MDS-based multi-axial
dimensionality reduction model for human action recog-
nition. In: 2014 Canadian conference on computer and
robot vision (CRV), Montreal, QC, Canada, 6–9 May
2014, pp.262–267. New York: IEEE.
101. Huynh DTG. Human activity recognition with wearable
sensors. PhD Thesis, TU Darmstadt, Darmstadt, 2008.
102. Fleury A, Vacher M and Noury N. SVM-based multi-
modal classification of activities of daily living in health
smart homes: sensors, algorithms, and first experimental
results. IEEE T Inf Technol B 2010; 14(2): 274–283.
103. Hong X, Nugent C, Mulvenna M, et al. Evidential
fusion of sensor data for activity recognition in smart
homes. Pervasive Mobile Comput 2009; 5(3): 236–252.
104. Szewcyzk S, Dwan K, Minor B, et al. Annotating smart
environment sensor data for activity learning. Technol
Health Care 2009; 17(3): 161–169.
105. Viani F, Martinelli M, Ioriatti L, et al. Real-time indoor
localization and tracking of passive targets by means of
wireless sensor networks. In: Antennas and propagation
society international symposium, APSURSI’09, Charles-
ton, SC, 1–5 June 2009, pp.1–4. New York: IEEE.
106. Viani F, Salucci M, Rocca P, et al. A multi-sensor WSN
backbone for museum monitoring and surveillance. In:
2012 6th European conference on antennas and propaga-
tion (EUCAP), Prague, 26–30 March 2012, pp.51–52.
New York: IEEE.
107. Wang W, Liu AX, Shahzad M, et al. Understanding
and modeling of WiFi signal based human activity
Ranasinghe et al. 21
recognition. In: Proceedings of the 21st annual interna-
tional conference on mobile computing and networking,
Paris, 7–11 September 2015, pp.65–76. New York:
108. Chen C, Jafari R and Kehtarnavaz N. A survey of depth
and inertial sensor fusion for human action recognition.
Multimed Tools Appl 2015; 1–21. DOI: 10.1007/s11042-
109. Chen L, Nugent CD and Wang H. A knowledge-driven
approach to activity recognition in smart homes. IEEE
T Knowl Data En 2012; 24(6): 961–974.
110. Brdiczka O, Langet M, Maisonnasse J, et al. Detecting
human behavior models from multimodal observation
in a smart home. IEEE T Autom Sci Eng 2009; 6(4):
111. Chahuara P, Fleury A, Portet F, et al. Using Markov
Logic Network for on-line activity recognition from
non-visual home automation sensors. In: Paterno
Ruyter B, Markopoulos P, et al. (eds) Ambient intelli-
gence. Berlin and Heidelberg: Springer, 2012,
112. Vacher M. Projet SWEET-HOME—ANR, 2016, http://
sweet-home.imag.fr/ (accessed 5 May 2016).
113. Maurer U, Smailagic A, Siewiorek DP, et al. Activity
recognition and monitoring using multiple sensors on
different body positions. In: BSN 2006, international
workshop wearable and implantable body sensor networks,
Cambridge, MA, 3–5 April 2006, p.4. New York: IEEE.
114. Vacher M, Serignat JF, Chaillol S, et al. Speech and
sound use in a remote monitoring system for health care.
In: Sojka P, Kopec
ˇek I and Pala K (eds) Text, speech
and dialogue. Berlin and Heidelberg: Springer, 2006,
115. Lara OD, Perez AJ, Labrador MA, et al. Centinela: a
human activity recognition system based on acceleration
and vital sign data. Pervasive Mobile Comput 2012; 8(5):
116. Lara OD and Labrador MA. A mobile platform for
real-time human activity recognition. In: 2012 IEEE
consumer communications and networking conference
(CCNC), Las Vegas, NV, 14–17 January 2012, pp.667–
671. New York: IEEE.
117. Kwapisz JR, Weiss GM and Moore SA. Activity recog-
nition using cell phone accelerometers. ACM SigKDD:
Explorat Newsletter 2011; 12(2): 74–82.
118. Riboni D and Bettini C. COSAR: hybrid reasoning for
context-aware activity recognition. Pers Ubiquit Comput
2011; 15(3): 271–289.
119. Schuldt C, Laptev I and Caputo B. Recognizing human
actions: a local SVM approach. In: Proceedings of the
17th international conference on pattern recognition,
ICPR 2004, 2004, vol. 3, pp.32–36, http:
120. Gorelick L, Blank M, Shechtman E, et al. Actions as
space-time shapes. IEEE Trans Pattern Anal Mach Intell
2007; 29(12): 2247–2253.
121. Weinland D, Ronfard R and Boyer E. Free viewpoint
action recognition using motion history volumes. Com-
put Vis Image Und 2006; 104(2): 249–257.
122. Mikel JA, Rodriguez D and Shah M. Action mach a
spatio-temporal maximum average correlation height
filter for action recognition. In: IEEE conference on
computer vision and pattern recognition, CVPR 2008,
Anchorage, AK, 23–28 June 2008, pp.1–8. New York:
123. Liu J, Luo J and Shah M. Recognizing realistic actions
from videos in the wild. In: IEEE conference on com-
puter vision and pattern recognition, CVPR 2009, 2009,
124. Iosifidis A, Marami E, Tefas A, et al. The MOBISERV-
AIIA eating and drinking multi-view database for
vision-based assisted living. J Inform Hiding Multimed
Sig Process 2015; 6(2): 254–273.
125. Kim H and Hilton A. Influence of colour and feature
geometry on multi-modal 3D point clouds data registra-
tion. In: 2014 2nd international conference on 3D vision,
Tokyo, Japan, 8–11 December 2014, vol. 1, pp.202–209.
New York: IEEE.
126. Cook D, Schmitter-Edgecombe M, Crandall A, et al.
Collecting and disseminating smart home sensor data in
the CASAS project. In: Proceedings of the CHI work-
shop on developing shared home behavior datasets to
advance HCI and ubiquitous computing research, 2009,
127. Van Kasteren TLM, Englebienne G and Kro
Human activity recognition from wireless sensor net-
work data: benchmark and software. In: Chen L,
Nugent CD, Biswas J, et al. (eds) Activity recognition in
pervasive intelligent environments. Paris: Atlantis Press,
128. Young V and Mihailidis A. An automated, speech-based
emergency response system for the older adult. Geron-
technology 2010; 9(2): 261.
129. Rialle V, Ollivet C, Guigui C, et al. What do family care-
givers of Alzheimer’s disease patients desire in smart
home technologies? Contrasted results of a wide survey.
Method Inform Med 2008; 47(1): 63–69.
130. Shi G, Chan CS, Li WJ, et al. Mobile human airbag sys-
tem for fall protection using MEMS sensors and
embedded SVM classifier. Sens J 2009; 9(5): 495–503.
131. Rougier C, Meunier J, St-Arnaud A, et al. Robust video
surveillance for fall detection based on human shape
deformation. IEEE T Circ Syst Vid 2011; 21(5):
132. Abbate S, Avvenuti M, Bonatesta F, et al. A
smartphone-based fall detection system. Pervasive
Mobile Comput 2012; 8(6): 883–899.
133. Madansingh S, Thrasher TA, Layne CS, et al. Smart-
phone based fall detection system. In: 2015 15th interna-
tional conference on control, automation and systems
(ICCAS), Busan, South Korea, 13–16 October 2015,
pp.370–374. New York: IEEE.
134. Ziefle M and Wilkowska W. Technology acceptability
for medical assistance. In: 2010 4th international confer-
ence on pervasive computing technologies for healthcare,
Munich, 22–25 March 2010, pp.1–9. New York: IEEE.
22 International Journal of Distributed Sensor Networks