1. Assessment Test Framework for
Collecting and Evaluating
Fall-Related Data
Using Mobile Devices
DI Stefan Almer
July 11th, 2012
Graz University of Technology Central European Institute of Technology
Institute for Information Systems and Computer Media Institute for Rehabilitation and Ambient Assisted Living Technologies
Univ.-Doz. Dipl.-Ing. Dr.techn. Martin Ebner Dipl.-Ing. Dr.techn. Johannes Oberzaucher
Dipl.-Ing. Dr.techn. Josef Kolbitsch

2. Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices 2
Agenda
• Introduction
• Mobile Devices for Fall Detection
• Assessment Test Framework
• Mobile Device Client
• Evaluation
• Summary
Stefan Almer July 11th, 2012

3. Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices 3
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”
Stefan Almer July 11th, 2012

4. Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices 4
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
Stefan Almer July 11th, 2012

5. Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices 5
Fall Detection
• Five phases of a fall
1) Activity 2) Hard- 3) Free-fall 4) Impact 5) Optional
of daily living predictable event recovery
Fig. 1: Fall Phases [Abbate et al., 2010]
• Classification of fall detection methods [Yu, 2008]
• wearable device / camera-based / ambience device
• Important to differentiate between a fall and
activities of daily living
Stefan Almer July 11th, 2012

6. Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices 6
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
Stefan Almer July 11th, 2012

7. Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices 7
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
Stefan Almer July 11th, 2012

8. Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices 8
Test Framework (cont.)
• Assessment test-based approach
Motion Data
Sensors
User
Test Device Position
Test Type
Sample Rate
Fig. 2: Test Properties and Device Relation
• Provides Interface (API)
• well defined interface
• integrate various devices with different sensors
• stored data can be accessed later
Stefan Almer July 11th, 2012

9. Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices 9
Framework Architecture [Helic, 2008]
• Based on 3-tier architecture
Data Tier Application Tier Client Tier
Interface
HTTP
Database JDBC
Java (Web service)
Client
(Browser/
iOS)
Static Content HTTP
(Backend)
Fig. 3: Framework Architecture
Stefan Almer July 11th, 2012

10. Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices 10
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
Fig. 4: Apple iOS Client
Stefan Almer July 11th, 2012

11. Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices 11
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
Fig. 5: User wears Device while performing test
Stefan Almer July 11th, 2012

12. Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices 12
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)
1.4
3 m straight walk turn phase
1.2 3 m straight walk
sit phase
sit down phase sit phase
1
0.8
SVM [g]
0.6
0.4
0.2
0
0 100 200 300 400 500 600 700 800 900 1000
datapoints [n] - movement data recorded with 50 Hz
Fig.6: Timed Up and Go Test (Individual Phases)
Stefan Almer July 11th, 2012

13. Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices 13
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
Stefan Almer July 11th, 2012

14. Assessment Test Framework for
Collecting and Evaluating Fall-Related Data
Using Mobile Devices
Stefan Almer
stefan@almer.cc
@stefalmer
Slides available at: http://elearningblog.tugraz.at
Graz University of Technology Central European Institute of Technology
Institute for Information Systems and Computer Media Institute for Rehabilitation and Ambient Assisted Living Technologies
http://www.iicm.tugraz.at http://www.ceit.at

15. [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-
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[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
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[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.
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