Monitoring people that need assistance: the BackHome experience

Eloisa Vargiu

EURECAT

ALIAS – Summer School

September 17, 2015

Monitoring People that

Need Assistance:

The BackHome Experience

1. Why: Assisting elderly and disabled people

2. What: Remote monitoring

3. How: A tele-assistance platform

4. Who: People with severe disabilities

5. Closing Remarks

6. References & Credits

2

Outline

3

Assisting elderly and disabled people

WHY

The Ageing Problem…

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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

TBI as cause of mortality and disability…

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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

How to better live alone at home…

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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 the community away from institutional care

IT IS URGENT TO ASSIST PEOPLE LIVING ALONE THROUGH ICT SOLUTIONS

Our Mission

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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

Main functionalities

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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: Door

Motion

Temperature

Luminosity

3 in 1: Motion

Temperature

Luminosity

z-wave

smartphone

Raspberry pi

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How

Healthcare centre (Therapist Station)

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How


18

How


19

How


20

How


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How


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How

Intelligent Monitoring

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How


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


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


AD

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


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 home

o going back to home

o receiving a visit

o remaining alone after a visit

o going to the bathroom

o going out of the bathroom

o going to sleep

o awaking from sleep

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How


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 provided o Historical

o Actual

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Mobility 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

Sleeping total sleeping time

hour the user went to sleep

hour the user woke up

number of times the user went to the

toilet during the night

time spent at the toilet during the night

number of time the user went to the

bedroom during the night

time spent at the bedroom during the

night

number of sleeping hours the day before

number of sleeping hours in the five

days before


SC

A QoL assessment system is also provided to assess a

specific QoL items

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SC

A QoL assessment system is also provided to assess a

specific QoL items

Mood number of received visits

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 burned calories

hour the user went to sleep

hour the user woke up

number of times the user went to the

toilet during the night

time spent at the toilet during the night

number of time the user went to the

bedroom during the night

time spent at the bedroom during the

night

number of sleeping hours the day before

number of sleeping hours in the five

days before

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How


RA

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


RA

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How

Results

On May, experiments started

on Belfast at HU1’s and HU3’s

homes

Both users answered to the

questionnaire

Data have been collected and

analyzed

HU1

HU3

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How

Results

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How

Results

AR (HU1)

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How

Results

QoL (HU1)

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How

Results

37

How

Results

38

How

Results

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How

Results

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How

Results

Therapist Station evaluation Overall the therapist station

was viewed in a positive

light and considered to be

an asset to daily practice

The 36.63% of the

therapists evaluated as

positive (4) the overall

platform

The 44.22% as very

positive (5)

A total of 80.86% of positive

and very positive evaluation

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People with severe disabilities

WHO

The Project

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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

Who

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)

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BackHome end-users

Who

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Who

https://youtu.be/yojVeyq6z0Q

BackHome.mov

https://youtu.be/yojVeyq6z0Q

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Closing Remarks

The End

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

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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 Baustista

Eloi Casals

José Alejandro Cordero

Juan Manuel Fernández

Joan Prota

Alexander Steblin

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Telemonitoring

o 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.). In

press.

o 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.). In press.

More Info

Selected Publications

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QoL Assessment o E. Vargiu, X. Rafael-Palou, F. Miralles. Experimenting Quality of Life

Telemonitoring in a Real Scenario. Artificial Intelligence Research,

Vol. 4, No. 2, 2015.

o 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


51

BackHome o F. Miralles, E. Vargiu, E. Casals, J.A. Cordero, S. Dauwalder

Automatically Assessing Movement Capabilities through a Sensor-

Based Telemonitoring System. International Journal of E-Health and

Medical Communications, October-December 2015, Vol. 6, No. 4,

pp. 40-49.

o C. Hintermüller, E. Vargiu, S. Halder, J. Daly, F. Miralles, H. Lowish,

N. Anderson, S. Martin, G. Edlinger. Brain Neural Computer

Interface for Everyday Home Usage. Universal Access in Human-

Computer Interaction. Access to Interaction, pp. 437-446. Springer

International Publishing.

More Info


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Monitoring people that need assistance: the BackHome experience

Technology

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