Fall detection algorithm using linear prediction model

One of the health issues in elderly people is the injury from the fall. Some of these injuries might lead to deaths. Thus, a good fall detection algorithm is needed to help reducing a rescuing time for a helper. In this paper, we develop a fall detection algorithm using the linear prediction model with a tri-axis accelerometer. We test the algorithm with the data set that have 11 activities (standing, walking, jumping, falling, running, lying, sitting, getting up (from lying to standing or from sitting to standing), going down (from standing to sitting), accelerating and decelerating) from 17 subjects. The result shows that we can detect all fall activities in both training and blind test data sets with precisions of 90.72% and 93.69%, respectively. The result also shows that we can detect 89.77% and 93.27% of other activities correctly. Although, there are some false alarms, the false alarm rate is small.

[1]  Wei Chen,et al.  A research on automatic human fall detection method based on wearable inertial force information acquisition system , 2009, 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[2]  Ding Liang,et al.  Pre-impact & impact detection of falls using wireless Body Sensor Network , 2012, Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics.

[3]  E. Alasaarela,et al.  A two-threshold fall detection algorithm for reducing false alarms , 2012, 2012 6th International Symposium on Medical Information and Communication Technology (ISMICT).

[4]  Tianmiao Wang,et al.  A wearable wireless fall detection system with accelerators , 2011, 2011 IEEE International Conference on Robotics and Biomimetics.

[5]  Paul D. Gader,et al.  A linear prediction land mine detection algorithm for hand held ground penetrating radar , 2002, IEEE Trans. Geosci. Remote. Sens..

[6]  Shih-Hau Fang,et al.  Developing a mobile phone-based fall detection system on Android platform , 2012, 2012 Computing, Communications and Applications Conference.

[7]  Yu Zhang,et al.  A wireless real-time fall detecting system based on barometer and accelerometer , 2012, 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA).

[8]  Petar Mostarac,et al.  System for monitoring and fall detection of patients using mobile 3-axis accelerometers sensors , 2011, 2011 IEEE International Symposium on Medical Measurements and Applications.

[9]  Gueesang Lee,et al.  Fall Detection Based on Movement and Smart Phone Technology , 2012, 2012 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future.

[10]  Guang-Zhong Yang,et al.  Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  R. Bajcsy,et al.  Wearable Sensors for Reliable Fall Detection , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[12]  Mark Hasegawa-Johnson,et al.  Acoustic fall detection using Gaussian mixture models and GMM supervectors , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[13]  James Brusey,et al.  Fall Detection with Wearable Sensors--Safe (Smart Fall Detection) , 2011, 2011 Seventh International Conference on Intelligent Environments.

[14]  Gerald Bieber,et al.  iFall - Case studies in unexpected falls , 2010, 2010 IEEE International Symposium on Industrial Electronics.

[15]  Gang Zhou,et al.  Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information , 2009, 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks.

[16]  P. Leroux,et al.  SFCW microwave radar for in-door fall detection , 2012, 2012 IEEE Topical Conference on Biomedical Wireless Technologies, Networks, and Sensing Systems (BioWireleSS).

[17]  Korbinian Frank,et al.  Reliable Real-Time Recognition of Motion Related Human Activities using MEMS Inertial Sensors , 2010 .