monitoring people that need assistance: the backhome experience
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Eloisa VargiuBarcelona Digital
Technology CenterEURECAT
June 18, 2015
Monitoring People thatNeed Assistance:
The BackHome Experience
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About me
Manager of Integrated Continuous Care research line at EURECAT (Barcelona, Spain)
Technical coordinator of the BackHome project
Ph.D. in Electronic and Computer Engineering (Univ. of Cagliari, Italy)
Contact: eloisa.vargiu@eurecat.org More: https://sites.google.com/site/eloisavargiu/
eloisa.vargiu@eurecat.org
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1. Why: Assisting elderly and disabled people
2. What: Remote monitoring
3. How: A tele-assistance platform
4. Where: The BackHome project
5. Who: People with severe disabilities
6. Closing Remarks
7. References & Credits
Outline
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Assisting elderly and disabled people
WHY
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The Ageing Problem…
Why
By 2020, around 25% of the EU population will be over 65
People aged from 65-80 will rise by nearly 40% between 2010-2030
From 2012, the over-60 population will increase by about 2 million people a year
The median age of the EU population increased from 35.2 years in 1990, to 40.9 years by 2010
IT IS URGENT TO PROMOTE ACTIVE AGEING THROUGH ICT SOLUTIONS
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TBI as cause of mortality and disability…
Why
Every year 10 million people worldwide are affected
An average in-hospital fatality rate of 3% has been measured
Over 200 per 100000 individuals are admitted to European hospitals each year
Annually 1.7 million TBI’s occur in the US either in isolation or alongside other injuries
IT IS URGENT TO SUPPORT PEOPLE WIHT COGNITIVE IMPAIRMENT THROUHG ICT SOLUTIONS
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How to better live alone at home…
Why
People need to be independent to live better
Elderly people feel more safe living at their home
People need to return to their previous life roles
The long term rehabilitation goal for individuals with an TBI is resettlement back in thecommunity away from institutional care
IT IS URGENT TO ASSIST PEOPLE LIVING ALONE THROUGH ICT SOLUTIONS
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Our Mission
Why
To help and support people that need assistance –elderly or disabled – at home
To give a feedback to therapists, caregivers, and relatives about the evolution of the status, behaviour and habits of each monitored user
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Remote monitoring
WHAT
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What
eKauri
eKauri
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What
Safety
• Motion• Door• Temperature• Luminosity• Gas• Smoke• Panic Button
Health
• Blood pressure• Weight• Glucose• Activity
Social
• Calendar• Alerts• Messages• Videoconference
User perspective
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What
Therapist/Caregiver perspective
Notifications/Triggers
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Main functionalities
What
Remote support Event notifications
Activity recognition Quality of life
assessment
Remote interaction with therapists and caregivers
Alerts in case of emergency situations
Event notifications Triggers in case of
anomaly detections
Summary of activities Summary of quality of
life
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A tele-assistance platform
HOW
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How
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How
Home
4 in 1:DoorMotionTemperatureLuminosity
3 in 1:MotionTemperatureLuminosity
z-wave
smartphone
Raspberry pi
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How
Healthcare centre (Therapist Station)
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How
Healthcare centre (Therapist Station)
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How
Healthcare centre (Therapist Station)
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How
Healthcare centre (Therapist Station)
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How
Healthcare centre (Therapist Station)
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How
Healthcare centre (Therapist Station)
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How
Intelligent Monitoring
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How
Intelligent Monitoring
PP Its goal is to preprocess the data iteratively sending
a chunk c to both ED and RE according to a sliding window approach
Starting from the overall data streaming, the system sequentially considers a range of time |ti - ti+1| between a sensor measure si at time ti and the subsequent measure si+1 at time ti+1
The output of PP is a window c from ts to ta, where ts is the starting time of a given period and ta is the actual time
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How
Intelligent Monitoring
ED It aims to detect and inform about emergency
situations for the end-users and about sensor-based system critical failures
Regarding the critical situations for the end-users, simple rules are defined and implemented to raise an emergency, when specific values appear on c
Regarding the system failures, ED is able to detect whenever user’s home is disconnected from the middleware as well as when a malfunctioning of a sensor occurs
Each emergency is a pair <si; lei> composed of the sensor measure si and the corresponding label lei that indicates the corresponding emergency
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How
Intelligent Monitoring
AR Its goal is to recognize
activities performed by the user
To recognize if the user is at home or away and if s/he is alone, we implemented a solution based on machine learning techniques
The output is a triple <ts; te; l>
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How
Intelligent Monitoring
EN It is able to detect events to be notified Each event is defined by a pair <ti; l> corresponding
to the time ti in which the event happens together with a label l that indicates the kind of event
Currently, this module is able to detect the following events: o leaving the homeo going back to homeo receiving a visito remaining alone after a visito going to the bathroomo going out of the bathroomo going to sleepo awaking from sleep
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How
Intelligent Monitoring
SC Once all the activities and events have been
classified, measures aimed at representing the summary of the user’s monitoring during a given period are performed
Two kinds of summary are providedo Historicalo Actual
A QoL assessment system is also provided to assess a specific QoL itemso Mobilityo Sleepingo Mood
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How
Intelligent Monitoring
RE It is aimed at executing one or more rules at runtime
according to the sequence of sensor measures coming from the PP as well as the summary provided by the SC
A rule is a quadruple <i; v; o; ar>
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How
Results
Data from a 40 years-old abled-body user
have been used!
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How
Results
AR a window of 4 months for training and evaluation
(training dataset) a window of 1 month for the test (testing dataset) experiments have been performed at each level of
the hierarchy
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How
Results
AR: Is the user at home?
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How
Results
AR: Is the user at alone?
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How
Results
AR: Overall hierarchical approach
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How
Results
AR: Overall hierarchical approach
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How
Results
AR: Activity
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How
Results
AR: Location
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How
Results
AR: Indoor position
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How
Results
AR: Sleeping
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How
Results
SC: Summary of a day
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How
Results
SC: QoL The habits of SU have been monitored in the period
from 01/11/2014 to 28/04/2015 A total of about 80 days have been considered to
build the dataset that has been labeled by using the answers given by SU to the following questionnaireo How was your ability to move about?o How did you sleep last night?o How was your mood?
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How
Results
SC: QoL - Mobility
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How
Results
SC: QoL - Mobility
Features: number of times the user left home total time performing outdoor activities total time performing activities (both indoors and
outdoors) total time of inactivity covered distance number of performed steps number of visited places number of burned calories
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How
Results
SC: QoL – Mobility: Classification
ClassifierOutdoor activitiesparams F1
Indoor and outdoor activitiesparams F1
SVMC = 1000γ = 0.008
0.699 C = 1γ = 0.04
0.765
Logistics Regression
C = 1.693 0.662 C = 3.0 0.764
kNN k = 7 0.675 k = 3 0.684Naïve Bayes -- 0.616 -- 0.736Decision tree -- 0.567 -- 0.618Random forest estimators = 5 0.666 estimators = 100 0.700AdaBoost estimators = 50 0.620 estimators = 10 0.485
The best classifiers have been then used with the test-set obtaining a F1 of 0.569 considering only outdoor activities and a F1 of 0.654 in case of considering both indoor and outdoor activities.
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How
Results
SC: QoL – Mobility: Regression
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The BackHome project
WHERE
FP7/2007-2013grant agreement n. 288566
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The Project BackHome is the first European research project
aimed at delivering the ambitious, but critical, step to bring BNCI systems to mainstream markets
The Objectives To study the transition from the
hospital to the home To learn how different BNCIs and
other assistive technologies work together
To reduce the cost and hassle of the transition from the hospital to the home
Where
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BackHome is aimed at… …producing applied results, developing
o new and better integrated practical electrode systems
o friendlier and more flexible BNCI softwareo better telemonitoring and home support tools
Where
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Practical electrode systems
Its design is completely different from all other devices and it sets a new standard of usability
The dry electrode version is based on the worldwide proven g.SAHARA electrodes
The tiny and lightweight device is attached to the EEG cap to avoid cable movements and to allow completely free movements
Where
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Flexible BNCI softwareSmart Home Control
Where
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Flexible BNCI softwareSmart Home Control Speller
Where
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Flexible BNCI softwareSmart Home Control Speller
Web Browsing, e-mail and social networks
Where
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Flexible BNCI softwareSmart Home Control Speller
Web Browsing, e-mail and social networksMultimedia player
Where
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Flexible BNCI softwareSmart Home Control Speller
Web Browsing, e-mail and social networksMultimedia player
Brain Painting
Where
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Flexible BNCI softwareSmart Home Control Speller
Web Browsing, e-mail and social networksMultimedia player
Brain PaintingCognitive Rehabilitation Games
Where
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People with severe disabilities
WHO
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Cedar Foundation (Belfast) Control Group: N= 5 End User Group: N=5
(1 F, M= 37 yrs ± 8.7, Post ABI M= 9.8 yrs, ±3.7) Home Users: N=3
University of Würzburg Control User Group (gel-based): N=10
(6 F, M: 24.5 yrs ±3.4) Control User Group (dry electrodes): N=10
(9 F, M: 24.4 yrs ±2.7) End User Group: N=6
(2 F, M=47.3 yrs ± 11)
BackHome end-users
Who
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The system has been installed and is currently running in 3 end-users’ homes in Belfast.
Experiments finished on Tuesday, results are coming!
Who
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Feedback
Who
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Feedback
Who
Therapist focus group: N=53 Do you feel the
BackHome platform could benefit your clients?
Yes N= 50
Do you think the BackHome platform could benefit you in your day-to-day practice?
Yes N=50
more variety of
tasks would have
been beneficial
Allows you to easily access
patient results. Easy to set up
tasks for patient to complete.
very useful starting point
when client returns home
from hospital and is very
dependant
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Closing Remarks
The End
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The proposed solution provides An no-intrusive sensor-based system installed at
user’s home An intelligent system that mines data to study
habits and quality-of-life of monitored users A web application for therapists and caregivers to
stay aware about the user status, condition, habits and quality-of-life
The overall approach has been fully integrated in the overall BackHome system
BackHome is almost finished, results from end-users are coming…stay tuned!
Closing Remarks
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References and Credits
More info
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BackHome
More Info
Web• www.Backhome-FP7.eu
LinkedIn• BackHome-FP7-Research-Innovation
Twitter• @BackHomeFP7
Youtube• BackHomeFP7
Consortium EURECAT/BDigital Team
And also…Javier BaustistaEloi CasalsJosé Alejandro CorderoJuan Manuel FernándezJoan ProtaAlexander Steblin
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eKauri partners for industrial exploitation
More Info
eKauri safety services:• Undergoing testing and
validation with 40 users throughout 2015
eKauri safety services:• Installation at 200 users
for final validation
eKauri health services:• eKauri provides the tablet
app + medical deviceseKauri social services:• eKauri provides
videoconference, calendar, messages and picture sharing to avoid social exclusion
eKauri safety services:• Integrated with the StrokePod, currently
under testing
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Miralles, F., Vargiu, E., Dauwalder, S., Solà, M., Müller-Putz, G., Wriessnegger, S.C., Pinegger, A., Kübler, A., Halder, S., Käthner, I., Martin, S., Daly, J., Armstrong, E., Guger, C., Hintermüller, C., and Lowish, H. Brain Computer Interface on Track to Home. The Scientific World Journal, Vol. 2015 (2015), Article ID 623896. http://dx.doi.org/10.1155/2015/623896
Rafael-Palou, X., Vargiu, E., Serra, G., Miralles, F. Improving Activity Monitoring through a Hierarchical Approach. The International Conference on Information and Communication Technologies for Ageing Well and e-Health, May, 20-22 2015, Lisbon. [conference]
Rafael-Palou, X., Vargiu, E., Miralles, F. Monitoring People that Need Assistance through a Sensor-based System: Evaluation and First Results. AI-AM/NETMED, 4th International Workshop on Artificial Intelligence and Assistive Medicine, June, 20 2015, Pavia.
More Info
Publications
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Miralles, F., Vargiu, E., Dauwalder, S., Solà, M., Fernández, J.M., Casals, E., and Cordero, J.A. Telemonitoring and Home Support in BackHome. DART 2014 - 8th International Workshop on Information Filtering and Retrieval - co-located with AIxIA 2014.
Miralles, F., Vargiu, E., Dauwalder, S., Casals, E., and Cordero, J.A. Providing Physical Autonomy to Disabled People through Telemonitoring and Home Support. First Italian Workshop on Artificial Intelligence for Ambient Assisted Living - AI*AAL.it 2014 - co-located with AIxIA 2014.
Miralles, F., Vargiu, E., Casals,E., Cordero, J.A., Dauwalder, S. Today, how was your ability to move about? 3rd International Workshop on Artificial Intelligence and Assistive Medicine, ECAI 2014, Prague, Czech Republic, 2014.
Vargiu, E., Fernández, J.M., and Miralles, F. Context-Aware based Quality of Life Telemonitoring. Distributed Systems and Applications of Information Filtering and Retrieval. C. Lai et al. (eds.), Distributed Systems and Applications of Information Filtering and Retrieval, Studies in Computational Intelligence 515, DOI: 10.1007/978-3-642-40621-8_1, © Springer-Verlag Berlin Heidelberg 2014.
More Info
Publications
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Fernández, J.M., Torrellas, S., Dauwalder, S., Solà, M., Vargiu, E. and Miralles, F. Ambient-Intelligence Trigger Markup Language: A new approach to Ambient Intelligence rule definition. DART@AI*IA 2013 - Information Filtering and Retrieval. Proceedings of the 7th International Workshop on Information Filtering and Retrieval co-located with the 13th Conference of the Italian Association for Artificial Intelligence (AI*IA 2013). CEUR Workshop Proceedings, Vol. 1109, December 2013.
Vargiu, E., Fernández, J.M., Torrellas, S. Dauwalder, S., Solà, M., and Miralles, F. A Sensor-based Telemonitoring and Home Support System to Improve Quality of Life through BNCI. In Assistive Technology: From Research to Practice, AAATE 2013. Encarnação, P., Azevedo, L., Gelderblom, G.J., Newell, A., Mathiassen, N.-E. (Eds.), September 2013.
Vargiu, E., Ceccaroni, L., Subirats, L., Martin, S., and Miralles, F. User Profiling of People with Disabilities - A Proposal to Pervasively Assess Quality of Life. In ICAART 2013 - Proceedings of the 5th International Conference on Agents and Artificial Intelligence, Volume 2, J. Filipe, A. L. N. Fred (Eds.) Barcelona, Spain, 15-18 February, 2013. SciTePress 2013
More Info
Publications
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Miralles, F., Vargiu, E., Casals, E., Cordero, J.A., Dauwalder, S. Automatically Assessing Movement Capabilities through a Sensor-Based Telemonitoring System. International Journal of e-health medical communication, in press.
Miralles, F., Vargiu, E. Providing Physical and Social Autonomy to Disable People through BCI, Telemonitoring and Home Support. Intelligenza Artificiale, IOS Press, in press.
Casals, E., Cordero, J.A., Dauwalder, S., Fernández, J.M., Solà, M., Vargiu, E., Miralles, F. Ambient Intelligence by ATML - Rules in BackHome. Emerging ideas on Information Filtering and Retrieval. DART 2013: Revised and Invited Papers; C. Lai, A. Giuliani and G. Semeraro (eds.), in press.
More Info
Publications
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Rafael-Palou, X., Vargiu, E., Dauwalder, S., Miralles, F. Monitoring and Supporting People that Need Assistance: the BackHome Experience. Information Filtering and Retrieval. DART 2014: Revised and Invited Papers. C. Lai, A. Giuliani and G. Semeraro (eds.). To be published.
Fernández, J.M., Solá, M., Steblin, A., Vargiu, E., Miralles, F. The Relevance of Providing Useful Information to Therapists and Caregivers in Tele*. DART 2014: Revised and Invited Papers. C. Lai, A. Giuliani and G. Semeraro (eds.). To be published.
More Info
Publications
eloisa.vargiu@eurecat.org
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