F2D: A fall detection system tested with real data from daily life of elderly people

Falls among older people remain a very important public healthcare issue. Every year over 11 million falls are registered in the U.S. alone. This paper presents a practical real time fall detection system running on a smartwatch (F2D). A decision module takes into account the rebound after the fall and the residual movement of the user, matching a detected fall pattern to an actual fall. The final decision of a fall event is taken based on the location of the user. To the best of our knowledge, this is the first fall detection system which works on an independent smartwatch, being less stigmatizing for the end user. The fall detection algorithm has been tested by Fondation Suisse pour les Téléthèses (FST), the project partner who is responsible for the commercialization of our system. By analyzing real data of activities of daily life of elderly people, we are confident that F2D meets the demands of a reliable and easily extensible system. This paper highlights the innovative algorithm which takes into account the residual movement and the location of the user to increase the fall detection accuracy. By testing with real data we have a fall detection system ready to be deployed on the market.

[1]  M. Alwan,et al.  A Smart and Passive Floor-Vibration Based Fall Detector for Elderly , 2006, 2006 2nd International Conference on Information & Communication Technologies.

[2]  Ye Li,et al.  Fall detection by built-in tri-accelerometer of smartphone , 2012, Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics.

[3]  Israel Gannot,et al.  Fall detection of elderly through floor vibrations and sound , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  P. Laippala,et al.  Falls and lying helpless in the elderly. , 1992, Zeitschrift fur Gerontologie.

[5]  S. Cummings,et al.  Risk factors for recurrent nonsyncopal falls. A prospective study. , 1989, JAMA.

[6]  Michel Deriaz,et al.  Increased Fall Detection Accuracy in an Accelerometer-based Algorithm Considering Residual Movement , 2015, ICPRAM.

[7]  Sever Pasca,et al.  Fall detection algorithm based on triaxial accelerometer data , 2013, 2013 E-Health and Bioengineering Conference (EHB).

[8]  Gregory D. Abowd,et al.  The Conference Assistant: combining context-awareness with wearable computing , 1999, Digest of Papers. Third International Symposium on Wearable Computers.

[9]  HoonJae Lee,et al.  Multiple object tracking for fall detection in real-time surveillance system , 2009, 2009 11th International Conference on Advanced Communication Technology.

[10]  Dong Xuan,et al.  PerFallD: A pervasive fall detection system using mobile phones , 2010, 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[11]  L. Levy.  Falls in Older People: Risk Factors and Strategies for Prevention , 2002 .

[12]  Stephen J. McKenna,et al.  Activity summarisation and fall detection in a supportive home environment , 2004, ICPR 2004.

[13]  Bin Huang,et al.  A method for fast fall detection , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[14]  Yibin Hou,et al.  Triaxial accelerometer-based real time fall event detection , 2012, International Conference on Information Society (i-Society 2012).

[15]  Garrett R. Brown,et al.  An Accelerometer Based Fall Detector : Development , Experimentation , and Analysis , 2005 .

[16]  Frank Sposaro,et al.  iFall: An android application for fall monitoring and response , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  A McIntosh,et al.  The design of a practical and reliable fall detector for community and institutional telecare , 2000, Journal of telemedicine and telecare.

[18]  Inês Sousa,et al.  Accelerometer-based fall detection for smartphones , 2014, 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

[19]  H.C. Kim,et al.  Development of novel algorithm and real-time monitoring ambulatory system using Bluetooth module for fall detection in the elderly , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[20]  Fatimah Ibrahim,et al.  Smartphone-Based Solutions for Fall Detection and Prevention: Challenges and Open Issues , 2014, Sensors.

[21]  Gan Chen,et al.  Accelerometer-based fall detection sensor system for the elderly , 2012, 2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems.