Assessment Test Framework for Collecting and Evaluating

Fall-Related DataUsing Mobile Devices

DI Stefan AlmerJuly 11th, 2012

Graz University of TechnologyInstitute for Information Systems and Computer Media

Univ.-Doz. Dipl.-Ing. Dr.techn. Martin EbnerDipl.-Ing. Dr.techn. Josef Kolbitsch

Central European Institute of TechnologyInstitute for Rehabilitation and Ambient Assisted Living Technologies

Dipl.-Ing. Dr.techn. Johannes Oberzaucher

Stefan Almer July 11th, 2012

Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices

Agenda

• Introduction

• Mobile Devices for Fall Detection

• Assessment Test Framework

• Mobile Device Client

• Evaluation

• Summary

2

Stefan Almer July 11th, 2012

Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices

Introduction

• Motivated by the demographic trend [van den Broek et al., 2009]

• average age will increase

• impact on healthcare systems, retirement plans

• more people will need assistance or support

• Falls and fall-related injuries

• Mobile Devices

• device of the future: “the steady companion”

3

Stefan Almer July 11th, 2012

Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices

Fall Prevention[Todd and Skelton, 2004; WHO, 2007; Tremblay Jr. and Barber, 2005; LeMier et al., 2002; BRAID, 2010]

• Common methods

• assessment tests

• adjustment of environment and walking aids

• gait analysis

• education

• exercise/training

• Differ in usage based on context

4

Stefan Almer July 11th, 2012

Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices

Fall Detection

• Five phases of a fall

• Classification of fall detection methods [Yu, 2008]

• wearable device / camera-based / ambience device

• Important to differentiate between a fall andactivities of daily living

5

1) Activityof daily living

2) Hard-predictable event

3) Free-fall 4) Impact 5) Optionalrecovery

Fig. 1: Fall Phases [Abbate et al., 2010]

Stefan Almer July 11th, 2012

Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices

Mobile Devices for Fall Detection[Columbus, 2011; Noury et al., 2007; Kangas et al., 2007]

• Classic approach

• “Individual” devices and sensors

• New approach: Mobile Devices

• equipped with required hardware: accelerometer

• software capabilities to read acceleration data

• Method: measure body acceleration

• fall has higher acceleration

• acceleration threshold to determine fall

• problem: position of sensor

6

Stefan Almer July 11th, 2012

Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices

Test Framework

• Fall detection is complex

• many parameters

• no general fall detection algorithm

• Aim of the framework

• collecting fall-related data

• easily set up of tests settings

• integration with different systems and devices

7

Stefan Almer July 11th, 2012

Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices

Test Framework (cont.)

• Assessment test-based approach

• Provides Interface (API)

• well defined interface

• integrate various devices with different sensors

• stored data can be accessed later

8

TestUser

Test Type

Device

Sensors

Position

Sample Rate

Fig. 2: Test Properties and Device Relation

Motion Data

Stefan Almer July 11th, 2012

Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices

Framework Architecture[Helic, 2008]

• Based on 3-tier architecture

9

Fig. 3: Framework Architecture

Interface

Client (Browser/

iOS)

Java (Web service)

Static Content(Backend)

HTTP

HTTP

Database JDBC

Data Tier Application Tier Client Tier

Stefan Almer July 11th, 2012

Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices

Proof-of-Concept

• Integration of different devices

• Mobile Device Client

• demonstrates functionality of the framework

• shows capabilities and sensor accuracy

• Developed on iOS Platform

• uses the possibility to receive high-rate continuous motion data

10

Fig. 4: Apple iOS Client

Stefan Almer July 11th, 2012

Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices

Evaluation

• 3 clinical mobility tests were performed[Podsiadlo and Richardson, 1991; Whitney et al., 2005; Lewis and Shaw, 2005]

• “Sit-to-Stand 5”, “Timed Up and Go”, “2-Minute-Walk”

• iPhone 4, worn on hip height, 50Hz sample rate

• Tests analyzed afterwards

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Fig. 5: User wears Device while performing test

Stefan Almer July 11th, 2012

Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices

Evaluation (cont.)

• Movement analysis of recorded gait data

• sensor accurate enough to perform fall detection

• Data analysis and future extraction (peak detection, knowledge methods, statistical analysis)

12

0 100 200 300 400 500 600 700 800 900 10000

0.2

0.4

0.6

0.8

1

1.2

1.4

datapoints [n] - movement data recorded with 50 Hz

SV

M [g]

3 m straight walk

sit phasesit phase

sit down phase

3 m straight walk turn phase

Fig.6: Timed Up and Go Test (Individual Phases)

Stefan Almer July 11th, 2012

Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices

Summary

• One framework for various devices

• support for different sensors

• data collected or later analysis

• Web-Service API

• Backend for administrative tasks

• Proof-of-Concept

• Mobile Device Client (iOS platform)

• hardware well-suited for fall detection

13

Assessment Test Framework forCollecting and Evaluating Fall-Related Data

Using Mobile Devices

Stefan Almerstefan@almer.cc

@stefalmer

Slides available at: http://elearningblog.tugraz.at

Graz University of TechnologyInstitute for Information Systems and Computer Mediahttp://www.iicm.tugraz.at

Central European Institute of TechnologyInstitute for Rehabilitation and Ambient Assisted Living Technologies

http://www.ceit.at

[Abbate et al., 2010] Abbate, S., Avvenuti, M., Corsini, P., Vecchio, A., and Light, J., 2010. Monitoring of Human Movements for Fall Detection and Activities Recognition in Elderly Care using Wireless Sensor Network: A Survey. InTech. ISBN 9789533073217.

[BRAID, 2010] BRAID, 2010. Falls Prevention. http://capsil.braidproject.eu/index.php?title=Falls_Prevention&oldid=6250. Last accessed November 2, 2011.

[Columbus, 2011] Columbus, L., 2011. Gartner Releases Hype Cycle for Networking and Communications, 2011. http://softwarestrategiesblog.com/2011/08/27/gartner-releases-hype-cycle-for-networking-and-communications-2011/. Last accessed October 12, 2011.

[Helic, 2008] Helic, D., 2008. Software Architecture VO/KU. http://coronet.iicm.tugraz.at/sa/s5/sa_styles1.html. Last accessed October 6, 2011.

[Kangas et al., 2007] Kangas, M., Konttila, A., Winblad, I., Jämsa, T., 2007. Determination of Simple Thresholds for Accelerometry-Based Parameters for Fall Detection. In Proc. of the 29th Annual International Conference of the Engineering in Medicine and Biology Society, volume 2007, pages 1367–1370. doi:10.1109/IEMBS.2007.4352552.

[LeMier et al., 2002] LeMier, M., Silver, I., Bowe, C., 2002. Falls Among Older Adults: Strategies for Prevention. Technical Report, Washington State Department of Health. http://www.doh.wa.gov/hsqa/emstrauma/injury/pubs/FallsAmongOlderAdults.pdf. Last accessed November 2, 2011.

[Lewis and Shaw, 2005] Lewis, C., Shaw, K., 2005. Benefits of the 2-Minute Walk Test. Physical Therapy & Rehab Medicine, 16(16). http://physical-therapy.advanceweb.com/Article/Benefits-of-the-2-Minute-Walk-Test.aspx. Last accessed October 1, 2011.

[Noury et al., 2007] Noury, N., Fleury, A., Rumeau, P., Bourke, A.K., Ó Laighin, G., Rialle V., Lundy, J.E., 2007. Fall Detection - Principles and Methods. In Proc. of the 29th Annual International Conference of the Engineering in Medicine and Biology Society, pages 1663–1666. IEEE. doi:10.1109/IEMBS.2007.4352627.

[Podsiadlo and Richardson, 1991] Podsiadlo, D., Richardson, S., 1991. The Timed ’Up & Go’: A Test of Basic Functional Mobility for Frail Elderly Persons. American Geriatrics Society, 39(2), pages 142–148. ISSN 00028614. http://www.ncbi.nlm.nih.gov/pubmed/1991946. Last accessed October 1, 2011.

[Todd and Skelton, 2004] Todd, C., Skelton, D., 2004. What are the main risk factors for falls among older people and what are the most effective interventions to prevent these falls? Technical Report, WHO Regional Office for Europe. http://www.euro.who.int/document/E82552.pdf. Last accessed November 2, 2011.

[Tremblay Jr. and Barber, 2005] Tremblay Jr., K.R., Barber, C.E., 2005. Preventing Falls in the Elderly. http://www.ext.colostate.edu/pubs/consumer/10242.pdf. Last accessed November 2, 2011.

[van den Broek et al., 2009] van den Broek, G., Cavallo, F., Odetti, L., Wehrmann, C., 2009. Ambient Assisted Living Roadmap. http://www.aaliance.eu/public/documents/aaliance-roadmap/aaliance-aal-roadmap.pdf. Last accessed October 20, 2011.

[WHO, 2007] WHO, 2007. WHO Global Report on Falls Prevention in Older Age. http://www.who.int/ageing/publications/Falls_prevention7March.pdf. Last accessed November 2, 2011.

[Whitney et al., 2005] Whitney, S., Wrisley, D.M., Marchetti, G.F., Gee, M.A., Redfern, S.M., Furman J.M., 2005. Clinical Measurement of Sit-To-Stand Performance in People With Balance Disorders: Validity of Data for the Five-Times Sit-To-Stand Test. Physical Therapy, 85(10), pages 1034–1045. ISSN 00319023. http://www.ncbi.nlm.nih.gov/pubmed/16180952. Last accessed October 1, 2011.

[Yu, 2008] Yu, X., 2008. Approaches and Principles of Fall Detection for Elderly and Patient. In Proc. of the 10th International Conference on e-Health Networking, Applications and Services, pages 42–47. IEEE. doi:10.1109/HEALTH.2008.4600107.