Conference PaperPDF Available

Abstract and Figures

With the ever-growing prevalence of dementia, nursing costs are increasing, while the ability to live independently vanishes. Dem@Home is an ambient assisted living framework to support independent living while receiving intelligent clinical care. Dem@Home integrates a variety of ambient and wearable sensors together with sophisticated, interdisciplinary methods of image and semantic analysis. Semantic Web technologies, such as OWL 2, are extensively employed to represent sensor observations and application domain specifics as well as to implement hybrid activity recognition and problem detection. Complete with tailored user interfaces, clinicians are provided with accurate monitoring of multiple life aspects, such as physical activity, sleep, complex daily tasks and clinical problems, leading to adaptive non-pharmaceutical interventions. The method has been already validated for both recognition performance and improvement on a clinical level, in four home pilots.
Content may be subject to copyright.
Dem@Home: Ambient Intelligence for Clinical Support
of People Living with Dementia
Stelios Andreadis, Thanos G. Stavropoulos, Georgios Meditskos
and Ioannis Kompatsiaris
Information Technologies Institute, Center for Research and Technologies - Hellas, Greece
{andreadisst, athstavr, gmeditsk, ikom}@iti.gr
Abstract. With the ever-growing prevalence of dementia, nursing costs are in-
creasing, while the ability to live independently vanishes. Dem@Home is an
ambient assisted living framework to support independent living while receiv-
ing intelligent clinical care. Dem@Home integrates a variety of ambient and
wearable sensors together with sophisticated, interdisciplinary methods of im-
age and semantic analysis. Semantic Web technologies, such as OWL 2, are ex-
tensively employed to represent sensor observations and application domain
specifics as well as to implement hybrid activity recognition and problem detec-
tion. Complete with tailored user interfaces, clinicians are provided with accu-
rate monitoring of multiple life aspects, such as physical activity, sleep, com-
plex daily tasks and clinical problems, leading to adaptive non-pharmaceutical
interventions. The method has been already validated for both recognition per-
formance and improvement on a clinical level, in four home pilots.
Keywords: ambient assisted living, sensors, semantic web, ontologies, reason-
ing, context-awareness, dementia
1 Introduction
The increase of the average lifespan across the world has been accompanied by an
unprecedented upsurge in the occurrence of dementia, with high socio-economic
costs, reaching 818 billion US dollars worldwide, in 2015. Nevertheless, its preva-
lence is increasing as the number of people aged 65 and older with Alzheimer's dis-
ease may nearly triple by 2050, from 46.8 million to 131 million people around the
world, the majority of which, living in an institution [1].
Assistive technologies could enhance clinicians’ diagnosis and decision making, in
order to meet individual needs, but also to be used as an objective assessing measure
of cognitive status and disease progress of patients. Furthermore, assistive technology
is expected to play a critical role in improving patients’ quality of life, both on cogni-
tive and physical level, whereas cost is reduced. Drawbacks of current health services
are that they often aim to evaluate single needs (e.g. pharmacological treatment) or
detect problems solely via interviews, leading to generic interventions by clinicians.
However, home remote monitoring of patients is a promising “patient-centered” man-
agement approach that provides specific and reliable data, enabling the clinicians to
monitor patients’ daily function and provide adaptive and personalized interventions.
Towards this direction, we propose Dem@Home, a holistic approach for context-
aware monitoring and personalized care of dementia at homes, prolonging independ-
ent living. To begin with, the system integrates a wide range of sensor modalities and
high-level analytics to support accurate monitoring of all aspects of daily life includ-
ing physical activity, sleep and activities of daily living (ADLs), based on a service-
oriented middleware [2]. After integrating them in a uniform knowledge representa-
tion format, Dem@Home employs semantic interpretation techniques to infer com-
plex activity recognition from atomic events and highlight clinical problems. Specifi-
cally, it follows a hybrid reasoning scheme, using DL reasoning for activity detection
and SPARQL to extract clinical problems. Utterly, Dem@Home presents information
to applications tailored to clinicians and patients, endorsing technology-aided clinical
interventions to improve care. Dem@Home has been deployed and evaluated in four
home pilots showing optimistic results with respect to accurate fusion and activity
detection and clinical value in care.
The rest of the paper is structured as follows: Section 2 presents relevant work,
while Section 3 gives an overview of the framework. Section 4 elaborates on data
analytics, presenting the activity recognition and problem detection capabilities of
Dem@Home. Section 5 describes the GUIs supported by the framework to provide
feedback to clinical experts or patients, Section 6 presents the evaluation results and
Section 7 concludes the paper.
2 Related Work
Pervasive technology solutions have already been employed in several ambient envi-
ronments, either homes or clinics, but most of them focus on a single domain to moni-
tor, using only a single or a few devices. Such applications include wandering behav-
ior prevention with geolocation devices, monitoring physical activity, sleep, medica-
tion and performance in daily chores [3] [4].
In order to assess cognitive state, activity modelling and recognition appears to be
a critical task, common amongst existing assistive technology. OWL has been widely
used for modelling human activity semantics, reducing complex activity definitions to
the intersection of their constituent parts. In most cases, activity recognition involves
the segmentation of data into snapshots of atomic events, fed to the ontology reasoner
for classification. Time windows [5] and slices [6] provide background knowledge
about the order or duration [7] of activities are common approaches for segmentation.
In this paradigm, ontologies are used to model domain information, whereas rules,
widely embraced to compensate for OWL’s expressive limitations, aggregate activi-
ties, describing the conditions that drive the derivation of complex activities e.g. tem-
poral relations.
Focusing on clinical care through sensing, the work in [8] has deployed infrared
motion sensors in clinics to monitor sleep disturbances, limited, though, to a single
sensor. Similarly, the work in [9] presents a sensor network deployment in nursing
homes in Taiwan to continuously monitor vital signs of patients, using web-based
technologies, verifying the system’s accuracy, acceptance and usefulness. Neverthe-
less, it so far lacks the ability to fuse more sensor modalities such as sleep and ambi-
ent sensing, with limited interoperability.
Other solutions involve smart home deployments of environmental sensors to ob-
serve and assess elder and disabled people activities [10] [11]. The work in [12] moni-
tors the residents’ physical activity and vital signs by using wearable sensors, door
sensors to measure presence and “fully automated biomedical devices” in the bath-
room, while the system presented in [13] provides security monitoring, with actuators
to control doors, windows and curtains, but none of the above records sleep. On the
other hand, Dem@Home offers a unified view of many life aspects, including sleep
and activities, to automatically assess disturbances and their causes, aiding clinical
monitoring and interventions.
3 The Dem@Home Framework
Dem@Home proposes a multidisciplinary approach that brings into effect the synergy
of the latest advances in sensor technologies addressing a multitude of complementary
modalities, large-scale fusion and mining, knowledge representation and intelligent
decision-making support. In detail, as depicted in Fig. 1, the framework integrates
several heterogeneous sensing modalities, such as physical activity and sleep sensor
measurements, combined input from lifestyle sensors and higher-level image analyt-
ics, providing their unanimous semantic representation and interpretation.
The current selection of sensors is comprised of proprietary, low-cost, ambient or
Fig. 1. Dem@Home architecture, sensors and clinical applications
wearable devices, originally intended for lifestyle monitoring, repurposed to a medi-
cal context. Ambient depth cameras
1
are collecting both image and depth data. The
Plug sensors
2
are attached to electronic devices, e.g. to cooking appliances, to collect
power consumption data. Tags
3
are attached to objects of interest, e.g. a drug-box or a
watering can, capturing motion events and Presence sensors are modified Tags that
detect people’s presence in a room using IR motion. A wearable Wristwatch
4
measures physical activity levels in terms of steps, while a pressure-based Sleep sen-
sor
5
is placed underneath the mattress to record sleep duration and interruptions.
Each device is integrated by using dedicated modules that wrap their respective
API, retrieve data and process them accordingly to generate atomic events from sen-
sor observations e.g. through aggregation. In the case of image data, computer vision
techniques are employed to extract information about humans performing activities,
such as opening the fridge, holding a cup or drinking [14]. All atomic events and ob-
servations are mapped to a uniform semantic representation for interoperability and
stored to the system’s Knowledge Base. Dem@Home applies further semantic analy-
sis, activity recognition and detection of problems i.e. anomalies, and then all the
derived information can be used by domain-specific applications offering a tailored
view to different types of users.
4 Activity Recognition and Problem Detection
To obtain a more comprehensive image of an individual’s condition and its progres-
sion, driving clinical interventions, Dem@Home employs semantic interpretation to
perform intelligent fusion and aggregation of atomic, sensor events to complex ones
and identify problematic situations, with a hybrid combination of OWL 2 reasoning
and SPARQL queries.
Dem@Home provides a simple pattern for modelling the context of complex activ-
ities. First of all, sensor observations, including location, posture, object movement
and actions, are integrated with complex activities in a uniform model, as types of
events, extending the leo:Event class of LODE
6
(Fig. 1). The agents of the events
and the temporal context are captured using constructs from DUL
7
and OWL Time
8
,
respectively.
Each activity context is described through class equivalence axioms that link them
with lower-level observations of domain models (Fig. 1). The instantiation of this
pattern is used by the underlying reasoner to classify context instances, generated
during the execution of the protocol, as complex activities. The instantiation involves
1
Xtion Pro - http://www.asus.com/Multimedia/Xtion_PRO/
2
Plugwise sensors - https://www.plugwise.nl/
3
Wireless Sensor Tag System - http://wirelesstag.net/
4
Jawbone UP24 - https://jawbone.com
5
Withings Aura - http://www2.withings.com/us/en/products/aura
6
LODE - http://linkedevents.org/ontology/
7
DUL - http://www.loa.istc.cnr.it/ontologies/DUL.owl
8
OWL Time - http://www.w3.org/TR/owl-time/
linking ADLs with context containment relations through class equivalence axioms.
For example, given that the activity PrepareHotTea involves the observations Turn-
KettleOn, CupMoved, KettleMoved, TeaBagMoved and TurnKettleOff, its semantics
are defined as:
𝑃𝑟𝑒𝑝𝑎𝑟𝑒𝑇𝑒𝑎 ≡ 𝐶𝑜𝑛𝑡𝑒𝑥𝑡 ⊓ ∃𝑐𝑜𝑛𝑡𝑎𝑖𝑛𝑠. 𝑇𝑢𝑟𝑛𝐾𝑒𝑡𝑡𝑙𝑒𝑂𝑛 ⊓ ∃𝑐𝑜𝑛𝑡𝑎𝑖𝑛𝑠. 𝐶𝑢𝑝𝑀𝑜𝑣𝑒𝑑
⊓ ∃𝑐𝑜𝑛𝑡𝑎𝑖𝑛𝑠. 𝐾𝑒𝑡𝑡𝑙𝑒𝑀𝑜𝑣𝑒𝑑 ⊓ ∃𝑐𝑜𝑛𝑡𝑎𝑖𝑛𝑠. 𝑇𝑒𝑎𝐵𝑎𝑔𝑀𝑜𝑣𝑒𝑑
⊓ ∃𝑐𝑜𝑛𝑡𝑎𝑖𝑛𝑠. 𝑇𝑢𝑟𝑛𝐾𝑒𝑡𝑡𝑙𝑒𝑂𝑓𝑓
According to clinical experts involved in the development of Dem@Home, high-
lighting problematic situations next to the entire set of monitored activities and met-
rics would further facilitate and accelerate clinical assessment. Dem@Home uses a set
of predefined rules (expressed in SPARQL) with numerical thresholds that clinicians
can adjust and personalize to each of the individuals in their care, through a GUI.
Furthermore, each analysis is invoked for a period of time allowing different thresh-
olds for different intervals e.g. before and after a clinical intervention. Problematic
situations supported so far regard night sleep (short duration, many interruptions, too
long to fall asleep), physical activity (low daily activity totals), missed activities (e.g.
skipping daily lunch) and reoccurring problems (problems for consecutive days).
5 End-User Assessment Application
At the application level, Dem@Home provides a multitude of user interfaces to assist
both clinical staff, summarizing an individual’s performance and highlighting abnor-
mal situations, and patients, proposing simplified view of measurements and educa-
tional material.
The clinician interface offers four different approaches to monitoring a patient, i.e.
Summary, Comparison, and All Observations, as well as four options of time extent of
the data, i.e. One-Day, Per Day, Per Week and Per Month. In One-Day Summary,
sleep measurements are obtained from one single night and are categorized as Total
Time in Bed but Awake, Total Time Shallow Sleep, Total Time Deep Sleep, Total Time
Asleep, Number of Interruptions and Sleep Latency (Fig. 2). In Summary Per Day, the
Fig. 2. The clinician interface regarding sleep parameters, in One-Day Summary session.
clinician is able to select to a time interval between two dates or a single date, to ob-
serve sleep stages, physical levels and other activities of daily living, derived from
power consumption, moved objects and presence in rooms (Fig. 3). Moreover, the
clinician can set specific thresholds about sleeping problems during the night and a
problems section will be added (bottom of Fig. 3). In Comparison per Day, different
measurements of a particular time period can be combined in the same chart, allowing
the clinician to check how observations affect each other, e.g. how physical activity
affects sleep or how usage of a device affects a daily activity (Fig. 4). Finally, Corre-
lation shows a scatterplot for two types of measurements, while All Observations
shows all collected data in detail. The Per Week/Month options offer the above-
mentioned functionalities summarized per week/month.
On the other hand, patients are introduced to an alternative interface, tailored to
provide easy monitoring of their daily life and simple interaction with the clinicians.
Accessed by a tablet device, a limited view of the most important measurements is
displayed, to avoid overwhelming the users or even stressing them out. The patient
interface presents 3-day information regarding Physical Activity (daily steps and
burned calories), Sleep, Usage of Appliances and Medication. Especially in Sleep
section, patient is notified about how many sleep interruptions they had during the
Fig. 3. Sleep, daily activities and problems in Summary Per Day session.
night. In addition to sensor readings, the patient interface is enhanced with education-
al material, such as recipes or instructions to guide them step-by-step to perform rou-
tine tasks, and the ability to exchange messages between end-users and clinicians.
Overall, the application is explicitly design to help patients feel confident and secure
with the system they are using, but also to encourage social interaction between users
and clinicians.
6 Evaluation
Dem@Home was evaluated in four home installations, in the residences of individu-
als living alone, clinically diagnosed with mild cognitive impairment or mild demen-
tia, and maintained for four months. Sensors and relevant home areas or devices of
the installation (Table 1) were selected after a visit from the clinician to the partici-
pants. The majority of deployed sensors covered the areas of kitchen, bathroom and
bedroom, since these rooms are strongly linked with most daily activities.
Since the framework embodies an interdisciplinary approach, it was evaluated both
from research and clinical perspective. Firstly, we evaluate the effectiveness of activi-
ty recognition through fusion of sensor data and existing multimedia analytics. Sec-
ondly, clinical results vary and add significant value to monitoring and interventions.
For the evaluation of the ontology-based fusion and activity recognition capabili-
ties of Dem@Home, ground truth has been obtained through annotation (performed
once), based on images from ambient cameras. We use the True Positive Rate (TPR)
and Positive Predicted Value (PPV) measures, which denote recall and precision re-
spectively, to evaluate the performance with respect to ADLs recognized as per-
formed. The clinical expert suggested the monitoring of five activities, namely drug
box preparation, cooking, making tea, watching TV and bathroom visit. Table 2 de-
picts the pertinent context dependency models defined.
Fig. 4. Comparison Per Day chart between two activities
Dem@Home’s ADL activity recognition performance has been evaluated on a da-
taset of 31 days, in July 2015. As observed on Table 3, the more atomic and continu-
ous an activity is, the more accurate the detection. BathroomVisit, most accurately
detected, is never interleaved to do something else. On the contrary, cooking is a
long-lasting activity interrupted by instances of other events (e.g. watching TV) and
influenced by uncertainty and the openness of the environment. WatchTV and Pre-
pareTea are fairly short in duration, causing less uncertainty and interleaved events in
between, yielding decent precision and recall rates.
On the other hand, the clinical evaluation of the framework regards its capabilities
and the fulfillment of clinical requirements. With Dem@Home supporting clinical
interventions, significant improvement was found in post-pilot clinical assessment in
multiple domains, such as increase in physical condition and sleep quality, utterly
bringing about positive change in mood and cognitive state, measured objectively by
neuropsychological tests. In detail, the first participant has overcome insomnia, the
lack of exercise and neglecting daily chores. The second participant has shown im-
provement in sleep and mood, while the other two users have been benefited with
respect to sleep and medication.
Table 1. Sensors in home installation
Home area or device
Kitchen, Living room, Hall
TV, Iron, Vacuum, Cooking device, Boiler, Kettle, Bathroom lights
TV remote, Iron, Fridge door, Drug cabinet, Drug box, Tea bag, Cup
Kitchen, Bathroom, Living room
User’s arm
Bed
Table 2. Context dependency models for the evaluation
Activity Concept
Context dependency set
PrepareDrugBox
DrugBoxMoved, DrugCabinetMoved, KitchenPresence
Cooking
TurnCookerOn, KitchenPresence
PrepareTea
TurnKettleOn, TeaBagMoved, CupMoved, KitchenPresence,
TurnKettleOff
WatchTV
TurnTvOn, RemoteControlMoved, LivingRoomPresence
BathroomVisit
BathroomPresence, TurnBathroomLightsOn
Table 3. Precision and recall for activity recognition
Activity
Recall (TPR)
Precision (PPV)
PrepareDrugBox
0.86
0.89
Cooking
0.61
0.68
PrepareTea
0.81
0.86
WatchTV
0.87
0.80
BathroomVisit
0.91
0.94
7 Conclusion and Future Work
Dem@Home is an ambient assisted living framework integrating a variety of sensors,
analytics and semantic interpretation with a special focus on dementia ambient care.
New, affordable sensors have been integrated seamlessly into the framework, along
with a set of processing components, ranging from sensor to image analytics. All
knowledge is semantically interpreted for further fusion and detection of problematic
behaviours, while tailored user interfaces aim to detailed monitoring and adaptive
interventions. Evaluation of the framework has yielded valuable and optimistic results
with respect to accurate fusion and activity detection and clinical value in care.
Regarding future directions, Dem@Home could be extended for increased porta-
bility and installability. Specifically, establishing an open source, IoT-enabled seman-
tic platform, following the latest advances in board computing would allow the plat-
form to be easily deployed in multiple locations. Combined with the infrastructure to
push the events on a cloud infrastructure, the framework could constitute a powerful
platform for telemedicine and mobile health, combing sensors and sophisticated am-
bient intelligence techniques such as computer vision.
8 Acknowledgement
This work has been supported by the H2020-ICT-645012 project KRISTINA: A
Knowledge-Based Information Agent with Social Competence and Human Interac-
tion Capabilities.
9 References
1. Prince, M., Wimo, A., Guerchet, M., Ali, G., Wu, Y.T., Prina, M.: World Alz-
heimer Report 2015. The global impact of dementia. An analysis of prevalence,
incidence, cost and trends. Alzheimers Dis. Int. Lond. (2015).
2. Stavropoulos, T.G., Meditskos, G., Kontopoulos, E., Kompatsiaris, I.: The
DemaWare Service-Oriented AAL Platform for People with Dementia. Artif.
Intell. Assist. Med. AI-AMNetMed 2014. 11 (2014).
3. Kerssens, C., Kumar, R., Adams, A.E., Knott, C.C., Matalenas, L., Sanford, J.A.,
Rogers, W.A.: Personalized technology to support older adults with and without
cognitive impairment living at home. Am. J. Alzheimers Dis. Other Demen.
1533317514568338 (2015).
4. Dawadi, P.N., Cook, D.J., Schmitter-Edgecombe, M., Parsey, C.: Automated
assessment of cognitive health using smart home technologies. Technol. Health
Care Off. J. Eur. Soc. Eng. Med. 21, 323 (2013).
5. Okeyo, G., Chen, L., Wang, H., Sterritt, R.: Dynamic sensor data segmentation
for real-time knowledge-driven activity recognition. Pervasive Mob. Comput. 10,
155172 (2014).
6. Riboni, D., Pareschi, L., Radaelli, L., Bettini, C.: Is ontology-based activity
recognition really effective? In: Pervasive Computing and Communications
Workshops. pp. 427431. IEEE (2011).
7. Patkos, T., Chrysakis, I., Bikakis, A., Plexousakis, D., Antoniou, G.: A reasoning
framework for ambient intelligence. In: Artificial Intelligence: Theories, Models
and Applications. pp. 213222. Springer (2010).
8. Suzuki, R., Otake, S., Izutsu, T., Yoshida, M., Iwaya, T.: Monitoring daily living
activities of elderly people in a nursing home using an infrared motion-detection
system. Telemed. J. E Health. 12, 146155 (2006).
9. Chang, Y.-J., Chen, C.-H., Lin, L.-F., Han, R.-P., Huang, W.-T., Lee, G.-C.:
Wireless sensor networks for vital signs monitoring: Application in a nursing
home. Int. J. Distrib. Sens. Netw. 2012, (2012).
10. Helal, S., Mann, W., King, J., Kaddoura, Y., Jansen, E., others: The gator tech
smart house: A programmable pervasive space. Computer. 38, 5060 (2005).
11. Demongeot, J., Virone, G., Duchêne, F., Benchetrit, G., Hervé, T., Noury, N.,
Rialle, V.: Multi-sensors acquisition, data fusion, knowledge mining and alarm
triggering in health smart homes for elderly people. C. R. Biol. 325, 673682
(2002).
12. Tamura, T., Togawa, T., Ogawa, M., Yoda, M.: Fully automated health monitor-
ing system in the home. Med. Eng. Phys. 20, 573579 (1998).
13. Bonner, S.G.: Assisted interactive dwelling house. In: Proc. 3rd TIDE Congress:
Technology for Inclusive Design and Equality Improving the Quality of Life for
the European Citizen. p. 25 (1998).
14. Avgerinakis, K., Briassouli, A., Kompatsiaris, I.: Recognition of Activities of
Daily Living for Smart Home Environments. In: Intelligent Environments (IE),
2013 9th International Conference on. pp. 173180. IEEE (2013).
... Recommendation systems are part of medical services to help making the right decision based on objective data coming from medical history, physiological and environmental sensors [5,40]. Finally, coordination of the interventions between the multiple healthcare providers constitutes a service that requires context-data [25]. ...
... Physical environmental data describe on one hand how the inhabitant feels comfortable and on the other hand how he interacts with the environment to complete activities. The first set draws together data regarding ambient parameters [37,40,60]. Among multiple parameters some papers mention at least lightness [3,28,51], noise [34,44,51], temperature [17,30], humidity [17,60], carbonic gas and smoke concentrations [32,60]. ...
... Activity recognition is then performed by a set of logic rules built on top of ontologies and non-monotonic reasoning using Answer Set Programming for stream reasoning. [40] proposes Dem@Home, an ambient intelligence system for clinical support of people living with dementia. Ambient and wearable sensors observations and application domain specifics are captured in an OWL2 ontology that is aligned on DUL and integrates OWL Time1 to capture temporal context. ...
Chapter
The current Internet of Things (IoT) development involves ambient intelligence which ensures that IoT applications provide services that are sensitive, adaptive, autonomous, and personalized to the users’ needs. A key issue of this adaptivity is context modelling and reasoning. Multiple proposals in the literature have tackled this problem according to various techniques and perspectives. This chapter provides a review of context modelling approaches, with a focus on services offered in Ambient Assisted Living (AAL) systems for persons in need of care. We present the characteristics of contextual information, services offered by AAL systems, as well as context and reasoning models that have been used to implement them. A discussion highlights the trends emerging from the scientific literature to select the most appropriate model to implement AAL systems according to the collected data and the services provided.
... The Dem@Home SHIB approach presented by Andreadis et al. (29) is in the form of a framework that integrates a variety of ambient and wearable sensors in addition to interdisciplinary methods of image and semantic analysis. There is a personalization aspect in the Dem@Home SHIB (29), as semantic analysis, activity recognition and problem detection are used to present a profile for every user. ...
... The Dem@Home SHIB approach presented by Andreadis et al. (29) is in the form of a framework that integrates a variety of ambient and wearable sensors in addition to interdisciplinary methods of image and semantic analysis. There is a personalization aspect in the Dem@Home SHIB (29), as semantic analysis, activity recognition and problem detection are used to present a profile for every user. The evaluation of the Dem@Home SHIB was carried out for 4 months in four houses where people with mild cognitive impairment or mild dementia were living alone. ...
Article
Full-text available
There is a global challenge related to the increasing number of People with Dementia (PwD) and the diminishing capacity of governments, health systems, and caregivers to provide the best care for them. Cost-effective technology solutions that enable and ensure a good quality of life for PwD via monitoring and interventions have been investigated comprehensively in the literature. The objective of this study was to investigate the challenges with the design and deployment of a Smart Home In a Box (SHIB) approach to monitoring PwD wellbeing within a care home. This could then support future SHIB implementations to have an adequate and prompt deployment allowing research to focus on the data collection and analysis aspects. An important consideration was that most care homes do not have the appropriate infrastructure for installing and using ambient sensors. The SHIB was evaluated via installation in the rooms of PwD with varying degrees of dementia at Kirk House Care Home in Belfast. Sensors from the SHIB were installed to test their capabilities for detecting Activities of Daily Living (ADLs). The sensors used were: (i) thermal sensors, (ii) contact sensors, (iii) Passive Infrared (PIR) sensors, and (iv) audio level sensors. Data from the sensors were collected, stored, and handled using a ‘SensorCentral’ data platform. The results of this study highlight challenges and opportunities that should be considered when designing and implementing a SHIB approach in a dementia care home. Lessons learned from this investigation are presented in addition to recommendations that could support monitoring the wellbeing of PwD. The main findings of this study are: (i) most care home buildings were not originally designed to appropriately install ambient sensors, and (ii) installation of SHIB sensors should be adapted depending on the specific case of the care home where they will be installed. It was acknowledged that in addition to care homes, the homes of PwD were also not designed for an appropriate integration with ambient sensors. This study provided the community with useful lessons, that will continue to be applied to improve future implementations of the SHIB approach.
... Meng et al. [35] presented a rule-based service customization strategy aimed at enhancing home environments; in this work, ontologies are used to define rules for customization of services. In the field of CA for clinical support, Andreadis et al. [36] presented Dem@Home, a CA monitoring system for dementia caring at home developed to improve independent living; ontologies here are extensively employed to represent sensor observations, activity recognition and problem detection. Tila et al. [37] used semantic modelling to provide semantic interoperability among different data and to back-up the deployment of an IoT system for indoor environment control. ...
... For modelling sensors and devices inside the cabin environment, E-Cabin relies on BOnSAI [36], an ontology developed for AmI. This model encompasses several subsets of ontologies that allow for describing sensors, actuators and the services the devices provide. ...
Article
Full-text available
The international tourism competition poses new challenges to the cruise sector, such as the achievement of the tourists’ satisfaction and the increase in on board comfort. Moreover, the growing sophistication of tourists’ needs leads to a more user-centric touristic offer. Consequently, a personalized cabin environment, which fits the users’ activities and their characteristics, could be a plus value during the cruise vacation. These topics, however, are strictly connected with the diffusion of digital technologies and dynamics, which represent the tools to achieve the goal of a customized on-cruise experience. This paper presents E-Cabin, a novel Internet of Things (IoT) framework architecture that has at its core a reasoning system tuned on data gathered from the environment and from each specific passenger and the activities he/she performs. The framework leverages on knowledge representation with ontologies and consists of a publisher–subscriber communication framework that allows all of the IoT applications to use the reasoner and the provided ontologies. The paper demonstrates the proposed system in a demo cruise cabin where, by using the E-Cabin application, it is possible to set various atmospheres based on the users and activities occurring in the cabin.
... A user profile ontology model was presented in (Skillen et al. 2014) to enable the personalization of Help-On-Demand services like assistance for automated ticket machines and assistance for personalized route guidance using a mobile phone. (Andreadis et al. 2016) described Dem@Home, a system that uses an ontology to capture ambient and wearable sensors observations and application domain specifics in order to recognize activities, highlight problematic situations and determine non-pharmaceutical interventions to monitor people living with dementia. Sweet-Home described in (Chahuara et al. 2017) used ontologies to capture sensors and actuators' raw data for situation recognition (e.g., "main door open"). ...
Article
Full-text available
Technology-driven cognitive assistance is often either excessive or insufficient, and then not necessarily helpful, because of the inadaptability to the needs and abilities of people with cognitive impairments. Cognitive assistance is usually a dialogue (an interaction) where the caregiver provides assistive cues to which the assisted person replies with behaviours. This research proposes an ontology-based cognitive assistance model for ambient assisted living (AAL) systems. It aims to characterize the situations where assistance is needed and to provide an adaptive dialogue between the AAL system and individuals to help them cope with problematic situations in a minimally assisted manner, in so far as that is possible. The theory of speech acts is extended to translate cognitive assistance into adaptive assistive messages. The model enables AAL systems to identify when to assist according to the sensor observations and to deliver progressive minimal guidance messages with stops, or adjustments and replays, depending on the behaviours of people living with cognitive impairments, such as traumatic brain injury. Such user-centered assistance considers both the abilities, and the assistance needs of individuals, to give the right level of assistance that just nudges them towards carrying out tasks “on their own”. The model builds on cognitive assistance as used within clinical practice in the field of TBI and extends it to enable AAL systems to deliver assistance acts in due course through various actuators during the person’s everyday life activities. The model is formalized in an ontology to enable reasoning capability, extension, and reuse in different AAL systems. The illustration using a real-life scenario shows its usability and applicability in the provision of cognitive assistance through graded guidance message spreading.
... A fully-instrumented smart home could facilitate the observation of a patient in near realistic environment where the patient could sleep and carry out his or her daily activities as usual. The smart home is typically equipped with a variety of sensors that can be roughly divided into three types [35], [45], [3], [22]: ...
... Many of the tools for assisted living employ means of ambient intelligence, which are digital (embedded) environments that are responsive to human activities and interactions ( Acampora et al., 2013 ). Socalled assisted smart home projects employ ambient stationary sensors, e.g., to address the needs of dementia patients ( Andreadis et al., 2016;Rashidi and Cook, 2009 ), to provide health support for seniors ( Adami et al., 2010;Costa et al., 2012;Rantz et al., 2011 ) and inform robotic carers ( Chen et al., 2011 ). Assistive robotic systems ( Smarr et al., 2011 ) may help home-based elderly ( Beer et al., 2012 ) or people with disabilities ( Chen et al., 2013 ). ...
Article
Traumatic Brain Injury (TBI) is a major cause of disability in young people in New Zealand, and has long-term effects on memory and other cognitive functions. This article introduces MyMemory, a mobile augmented memory system that aims to assist TBI survivors in coping with their memory impairments. We here present an exploration of design requirements for mobile memory aids for people with TBI, the MyMemory conceptual design and high-level details of the prototype implementation. We report on the results of our A-B-A-B study with six TBI survivors and three caregivers. The participants with TBI all reported improvements when using MyMemory with regards to their well-being, memory function and autobiographical memory. The caregivers confirmed these observations of TBI participants, however, the results regarding possible reductions of caregiver burden are mixed.
Conference Paper
BACKGROUND AND MOTIVATION: The world population is living longer than ever before, Ambient Assisted Living (AAL) research try to ensure people are ageing well by using digital and physical technologies. The development of AAL platforms presents several challenges (e.g., sensor data integration, device interoperability). Many requirements for AAL platforms can be met by using the Internet of Things (IoT) and Cloud Computing paradigm standards. METHODOLOGY: In this paper, we conducted a systematic mapping of the literature on AAL platforms to investigate the correlation between these three paradigms. We focused on AAL platforms that use IoT and Cloud Computing concepts to meet their requirements. Our primary research question is "What are the requirements of AAL platforms in the IoT Era and their relationship with IoT standards?". We defined as the scope of our systematic mapping the primary studies about AAL platforms published between 2013 to 2017, and we used seven indexed electronic databases. RESULTS: We obtained 35 papers whose AAL platforms developed using IoT or Cloud Computing standards. We then analyse and correlate the requirements, architectures, and evaluation types presented by these platforms. We found patterns by connecting specific requirements with platform design. We believe these results assist AAL developers and architectural designers in the creation of new AAL platforms and applications.
Conference Paper
For impaired people, the conduction of certain daily life activities is problematic due to motoric and cognitive handicaps. For that reason, assistive agents in ambient assisted environments provide services that aim at supporting elderly and impaired people. However, these agents act in complex stochastic and indeterministic environments where the concrete effects of a performed action are usually unknown at design time. Furthermore, they have to perform varying tasks according to the user’s context and needs, wherefore an agent has to be flexible and able to recognize required capabilities in a certain situation in order to provide adequate, unobtrusive assistance. Hence, an expressive representation framework is required that relates user-specific impairments to required agent capabilities. This work presents an approach which (a) describes and links user impairments and capabilities using the formal, model-theoretic semantics expressed in OWL2 DL ontologies, (b) computes optimal policies through Reinforcement Learning and propagates these in an agent network. The presented approach improves the collaborative, personalized and adequate assistance of assistive agents and tailors the agent-based services to the user’s missing capabilities.
Conference Paper
Full-text available
This work presents DemaWare, an Ambient Intelligence platform that targets Ambient Assisted Living for people with Dementia. DemaWare seamlessly integrates diverse hardware (wearable and ambient sensors), as well as software components (semantic interpretation, reasoning), involved in such context. It also enables both online and offline processes, including sensor analysis and storage of context semantics in a Knowledge Base. Consequently, it orchestrates semantic interpretation which incorporated defeasible logics for uncertainty handling. Overall, the underlying functionality aids clinicians and carers to timely assess and diagnose patients in the context of lab trials, homes or nursing homes.
Conference Paper
Full-text available
The recognition of Activities of Daily Living (ADL) from video can prove particularly useful in assisted living and smart home environments, as behavioral and lifestyle profiles can be constructed through the recognition of ADLs over time. Often, existing methods for recognition of ADLs have a very high computational cost, which makes them unsuitable for real time or near real time applications. In this work we present a novel method for recognizing ADLs with accuracy comparable to the state of the art, at a lowered computational cost. Comprehensive testing of the best existing descriptors, encoding methods and BoW/SVM based classification methods takes place to determine the optimal recognition solution. A statistical method for determining the temporal duration of extracted trajectories is also introduced, to streamline the recognition process and make it less ad-hoc. Experiments take place with benchmark ADL datasets and a newly introduced set of ADL recordings of elderly people with dementia as well as healthy individuals. Our algorithm leads to accurate recognition rates, comparable or better than the State of the Art, at a lower computational cost.
Article
Full-text available
This study evaluated the application of a wireless sensor network (WSN) on a web-based vital signs monitoring system to nursing homes in Taiwan. The applicability assessment focused on the timely provision of information, information accuracy, system usability, and system accessibility of healthcare systems using a wireless sensor network. Experiments were performed under Internet-based network conditions to verify the timely information provision, especially for a web-based system, including Ajax technology. The accuracy of the information was verified from statistical analyses of the residents’ daily vital sign measurements. A comparison was performed between having and not having a healthcare monitoring system in nursing homes for system usability, system accessibility, and system efficacy. The results indicate that the successful application of a WSN healthcare monitoring system is feasible for use in nursing homes in Taiwan.
Article
Full-text available
The goal of this work is to develop intelligent systems to monitor the wellbeing of individuals in their home environments.OBJECTIVE: This paper introduces a machine learning-based method to automatically predict activity quality in smart homes and automatically assess cognitive health based on activity quality. This paper describes an automated framework to extract set of features from smart home sensors data that reflects the activity performance or ability of an individual to complete an activity which can be input to machine learning algorithms. Output from learning algorithms including principal component analysis, support vector machine, and logistic regression algorithms are used to quantify activity quality for a complex set of smart home activities and predict cognitive health of participants. Smart home activity data was gathered from volunteer participants (n=263) who performed a complex set of activities in our smart home testbed. We compare our automated activity quality prediction and cognitive health prediction with direct observation scores and health assessment obtained from neuropsychologists. With all samples included, we obtained statistically significant correlation (r=0.54) between direct observation scores and predicted activity quality. Similarly, using a support vector machine classifier, we obtained reasonable classification accuracy (area under the ROC curve=0.80, g-mean=0.73) in classifying participants into two different cognitive classes, dementia and cognitive healthy. The results suggest that it is possible to automatically quantify the task quality of smart home activities and perform limited assessment of the cognitive health of individual if smart home activities are properly chosen and learning algorithms are appropriately trained.
Conference Paper
Full-text available
While most activity recognition systems rely on data-driven approaches, the use of knowledge-driven techniques is gaining increasing interest. Research in this field has mainly concentrated on the use of ontologies to specify the semantics of activities, and ontological reasoning to recognize them based on context information. However, at the time of writing, the experimental evaluation of these techniques is limited to computational aspects; their actual effectiveness is still unknown. As a first step to fill this gap, in this paper, we experimentally evaluate the effectiveness of the ontological approach, using an activity dataset collected in a smart-home setting. Preliminary results suggest that existing ontological techniques underperform data-driven ones, mainly because they lack support for reasoning with temporal information. Indeed, we show that, when ontological techniques are extended with even simple forms of temporal reasoning, their effectiveness is comparable to the one of a state-of-the-art technique based on Hidden Markov Models. Then, we indicate possible research directions to further improve the effectiveness of ontology-based activity recognition through temporal reasoning.
Conference Paper
Full-text available
Ambient Intelligence is an emerging discipline that requires the integration of expertise from a multitude of scientific fields. The role of Artificial Intelligence is crucial not only for bringing intelligence to everyday environments, but also for providing the means for the different disciplines to collaborate. In this paper we describe the design of a reasoning framework, applied to an operational Ambient Intelligence infrastructure, that combines rule-based reasoning with reasoning about actions and causality on top of ontology-based context models. The emphasis is on identifying the limitations of the rule-based approach and the way action theories can be employed to fill the gaps.
Article
Although persons with dementia (PWD) and their family caregivers need in-home support for common neuropsychiatric symptoms (NPS), few if any assistive technologies are available to help manage NPS. This implementation study tested the feasibility and adoption of a touch screen technology, the Companion, which delivers psychosocial, nondrug interventions to PWD in their home to address individual NPS and needs. Interventions were personalized and delivered in home for a minimum of 3 weeks. Postintervention measures indicated the technology was easy to use, significantly facilitated meaningful and positive engagement, and simplified caregivers' daily lives. Although intervention goals were met, caregivers had high expectations of their loved one's ability to regain independence. Care recipients used the system independently but were limited by cognitive and physical impairments. We conclude the Companion can help manage NPS and offer caregiver respite at home. These data provide important guidance for design and deployment of care technology for the home. © The Author(s) 2015.
Article
Approaches and algorithms for activity recognition have recently made substantial progress due to advancements in pervasive and mobile computing, smart environments and ambient assisted living. Nevertheless, it is still difficult to achieve real-time continuous activity recognition as sensor data segmentation remains a challenge. This paper presents a novel approach to real-time sensor data segmentation for continuous activity recognition. Central to the approach is a dynamic segmentation model, based on the notion of varied time windows, which can shrink and expand the segmentation window size by using temporal information of sensor data and activities as well as the state of activity recognition. The paper first analyzes the characteristics of activities of daily living from which the segmentation model that is applicable to a wide range of activity recognition scenarios is motivated and developed. It then describes the working mechanism and relevant algorithms of the model in the context of knowledge-driven activity recognition based on ontologies. The presented approach has been implemented in a prototype system and evaluated in a number of experiments. Results have shown average recognition accuracy above 83% in all experiments for real time activity recognition, which proves the approach and the underlying model.
Conference Paper
The recognition of Activities of Daily Living (ADL) from video can prove particularly useful in assisted living and smart home environments, as behavioral and lifestyle profiles can be constructed through the recognition of ADLs over time. Often, existing methods for recognition of ADLs have a very high computational cost, which makes them unsuitable for real time or near real time applications. In this work we present a novel method for recognizing ADLs with accuracy comparable to the state of the art, at a lowered computational cost. Comprehensive testing of the best existing descriptors, encoding methods and BoW/SVM based classification methods takes place to determine the optimal recognition solution. A statistical method for determining the temporal duration of extracted trajectories is also introduced, to streamline the recognition process and make it less ad-hoc. Experiments take place with benchmark ADL datasets and a newly introduced set of ADL recordings of elderly people with dementia as well as healthy individuals. Our algorithm leads to accurate recognition rates, comparable or better than the State of the Art, at a lower computational cost.