Total views : 166
Smart Phone Based Fall Detection using Auto Regression Modeling in a Non-Restrictive Setting
Fall detection is an important aspect of the field of accident prevention, ambient assisted living as well as care of the elderly. To address this issue, researchers have employed several approaches including vision based systems, setups that require deployment in a special environment and inertial sensors. Inertial sensors have the advantage of being deployable in mobile systems such as wearable devices and smart phones. An important consideration in using inertial sensors for fall detection is the need to develop techniques that would work without enforcing positional requirements of the sensor device. This paper presents a method for the detection of falls using inertial sensors readings of the smart phone, a tri-axial accelerometer, tri-axial gyroscope and orientation data. We consider inertial sensor data for two falls and three activities of daily living. Using Auto-Regressive (AR) modeling to characterize the measurements from the sensors, we compare Support Vector Machines (SVM) and Neural Networks for use in classifying between these five events. Results indicate that the Neural Network provides better classification accuracy compared to SVM for the purpose of differentiating between falls and the activities of daily living.
Fall Detection, Inertial Sensor, Machine Learning, Mobifall.
- Chaudhuri S, Thompson H, Demiris G. Fall detection devices and their use with older adults: a systematic review. Journal of Geriatric Physical Therapy. 2014; 37:178-96. https://doi.org/10.1519/JPT.0b013e3182abe779 PMid:24406708 PMCid:PMC4087103
- World Health Organization: WHO Global Report on Falls Prevention in Older Age. Community Health (Bristol). 2007; 53.
- Alwan M, Rajendran PJ, Kell S, Mack D, Dalal S, Wolfe M, Felder R. A Smart and Passive Floor-Vibration Based Fall Detector for Elderly. 2006 2nd International Conference Information Communication Technology. 2006; 1:3-7. https://doi.org/10.1109/ictta.2006.1684511
- Mager B, Patwari N and Bocca M. Fall detection using RF sensor networks. London: IEEE Personal, Indoor and Mobile Radio Communications Conference (PIMRC 2013). 2013 Sept. 9. https://doi.org/10.1109/pimrc.2013.6666749
- Avinash C Kak and Malcolm Slaney. IEEE Press: Principles of computerized tomographic imaging. 1988.
- Rougier C, Meunier J, St.-Arnaud A, Rousseau J. Monocular 3D head tracking to detect falls of elderly people. Annual International Conference IEEE Engineering Medicine Biology Proceedings. 2006; 6384-87.
- Bian Z-P, Hou J, Chau L-P, Magnenat-Thalmann N. Fall detection based on body part tracking using a depth camera. IEEE Journal Biomedical Health Informatics. 2015; 19:430-9. https://doi.org/10.1109/JBHI.2014.2319372 PMid:24771601
- Stone E, Skubic M. Fall Detection in Homes of Older Adults Using the Microsoft Kinect. IEEE Journal Biomedical Health Informatics. 2014; 19:290-301. https://doi.org/10.1109/ JBHI.2014.2312180 PMid:24733032
- Zhang Z. Microsoft kinect sensor and its effect. IEEE Multimed. 2012; 19:4-10. https://doi.org/10.1109/ MMUL.2012.24
- Hansen TR, Eklund JM, Sprinkle J, Bajcsy R, Sastry S. Using smart sensors and a camera phone to detect and verify the fall of elderly persons. Citeseer: European Medicine, Biology and Engineering Conference. 2005; p. 2486.
- Wu G, Xue S. Portable preimpact fall detector with inertial sensors. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2008; 16:178-83. https://doi.org/10.1109/TNSRE.2007.916282 PMid:18403286
- Bourke AK, O’Brien JV, Lyons GM. Evaluation of a thresholdbased tri-axial accelerometer fall detection algorithm. Gait Posture. 2007; 26;194-9. https://doi.org/10.1016/j.gaitpost.2006.09.012 PMid:17101272
- Vavoulas G, Pediaditis M, Spanakis EG. The MobiFall Dataset-An Initial Evaluation of Fall Detection Algorithms Using Smartphones. 2013 IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE). 2013; 1-4.
- Vavoulas G, Chatzaki C, Malliotakis T, Pediaditis M, Tsiknakis M. The MobiAct Dataset : Recognition of Activities of Daily Living using Smartphones. Proceedings of International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2016). 2016; 143-51.
- Hsieh C-J, Chang K-W, Lin C-J, Keerthi SS, Sundararajan S. A Dual Coordinate Descent Method for Large-scale Linear SVM. Proceedings of 25th International Conference on Machine Learning - ICML ‘08. 2008; 408-15. https://doi.org/10.1145/1390156.1390208
- Jiangpeng Dai, Xiaole Bai, Zhimin Yang, Zhaohui Shen, Dong Xuan: PerFallD: A pervasive fall detection system using mobile phones. 2010 8th IEEE, International Conference Pervasive Computing Communications Workshops, (PERCOM Work). 2010; 292-7.
- Engin M. ECG beat classification using neuro-fuzzy network. Pattern Recognition Letters. 2004; 25:1715-22. https://doi.org/10.1016/j.patrec.2004.06.014
- Patterson AHD, Patterson BYHD. Biometrika Trust: The Use of Autoregression in Fitting an Exponential Curve. The Use Of AUTOREGRESSION YX-1. 2014; 45:389-400.
- Zeng M, Nguyen LT, Yu B, Mengshoel OJ, Zhu J, Wu P, Zhang J. Convolutional Neural Networks for Human Activity Recognition using Mobile Sensors. Proceedings of 6th International Conference Mobile Computer Applications Services. 2014; 6:197-205. https://doi.org/10.4108/icst.mobicase.2014.257786
- Hanrahan Grady. CRC Press: Artificial neural networks in biological and environmental analysis. 2011.
- Bishop C, Nasrabadi N. Pattern Recognition and Machine Learning. 2007.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.