Multi-sensor fusion for human daily activity recognition in robot-assisted living - Zhu, Chun; Sheng, Weihua (Proceedings of the 4th ACM/IEEE international conference on Human robot interaction, 2009)
In this paper, we propose a human activity recognition method by fusing the data from two wearable inertial sensors attached to one foot and the waist of a human subject, respectively. Our multi-sensor fusion based method combines neural networks and hidden Markov models (HMMs), and can reduce the computation load. We conducted experiments using a prototype wearable sensor system and the obtained results prove the effectiveness and the accuracy of our algorithm.