Body Sensor Networks (BSNs) are conveying notable attention due to their capabilities in supporting humans in their daily life. In particular, real-time and noninvasive monitoring of assisted livings is having great potential in many application domains, such as health care, sport/fitness, e-entertainment, social interaction and e-factory. And the basic as well as crucial feature characterizing such systems is the ability of detecting human actions and behaviors. In this paper, a novel approach for human posture recognition is proposed. Our BSN system relies on an information fusion method based on the D-S Evidence Theory, which is applied on the accelerometer data coming from multiple wearable sensors. Experimental results demonstrate that the developed prototype system is able to achieve a recognition accuracy between 98.5% and 100% for basic postures (standing, sitting, lying, squatting).
|Title of host publication||Proceedings of the 12th IEEE International Symposium on Cluster Computing and the Grid Computing, 13-16 May 2012, Ottawa, Canada|
|Place of Publication||Piscataway|
|Publisher||IEEE Computer Society|
|Publication status||Published - 2012|
Li, W., Bao, J., Fu, X., Fortino, G., & Galzarano, S. (2012). Human postures recognition based on D-S evidence theory and multi-sensor data fusion. In Proceedings of the 12th IEEE International Symposium on Cluster Computing and the Grid Computing, 13-16 May 2012, Ottawa, Canada (pp. 912-917). IEEE Computer Society. https://doi.org/10.1109/CCGrid.2012.144