Support Vector Machine for tri-axial accelerometer-based fall detector

The aim of this work is the development of a computationally low-cost scheme for feature extraction and the implementation of an One-class Support Vector Machine classifier for people fall detection, by using a tri-axial MEMS wearable wireless accelerometer, managed by a stand-alone PC through ZigBee connection. The proposed approach allows the generalization of the detection of fall events in several practical conditions after a short period of calibration. The approach appears invariant to age, weight, people's height and the relative positioning area (even in the upper part of the waist) This overcomes the drawbacks of well-known threshold-based approaches in which several parameters need to be manually estimated according to the specific features of the end-user. In order to limit the workload, the specific study on posture analysis has been avoided and a polynomial kernel function is used, while maintaining high performances in terms of specificity and sensitivity.

[1]  Tong Zhang,et al.  Fall Detection by Wearable Sensor and One-Class SVM Algorithm , 2006 .

[2]  Wen-Chang Cheng,et al.  Fall Detection with the Support Vector Machine during Scripted and Continuous Unscripted Activities , 2012, Sensors.

[3]  Xinguo Yu Approaches and principles of fall detection for elderly and patient , 2008, HealthCom 2008 - 10th International Conference on e-health Networking, Applications and Services.

[4]  M. Chung,et al.  Posttraumatic stress disorder in older people after a fall , 2009, International journal of geriatric psychiatry.

[5]  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.

[6]  Malik Yousef,et al.  One-Class SVMs for Document Classification , 2002, J. Mach. Learn. Res..

[7]  L. Gonzo,et al.  A hardware-software framework for high-reliability people fall detection , 2008, 2008 IEEE Sensors.

[8]  G. ÓLaighin,et al.  A proposal for the classification and evaluation of fall detectors Une proposition pour la classification et l'évaluation des détecteurs de chutes , 2008 .

[9]  Siv Sadigh,et al.  Falls and Fall-Related Injuries Among the Elderly: A Survey of Residential-Care Facilities in a Swedish Municipality , 2004, Journal of Community Health.

[10]  A. Bourke,et al.  Fall detection - Principles and Methods , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  Alessandro Leone,et al.  Detecting falls with 3D range camera in ambient assisted living applications: a preliminary study. , 2011, Medical engineering & physics.

[12]  Nigel H. Lovell,et al.  Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring , 2006, IEEE Transactions on Information Technology in Biomedicine.

[13]  Jing Huang,et al.  A multi-sensor approach for People Fall Detection in home environment , 2008 .

[14]  A K Bourke,et al.  Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities. , 2010, Journal of biomechanics.

[15]  Jeffrey M. Hausdorff,et al.  Evaluation of Accelerometer-Based Fall Detection Algorithms on Real-World Falls , 2012, PloS one.