Realtime Recognition of Complex Human Daily Activities Using Human Motion and Location Data

Daily activity recognition is very useful in robot-assisted living systems. In this paper, we proposed a method to recognize complex human daily activities which consist of simultaneous body activities and hand gestures in an indoor environment. A wireless power-aware motion sensor node is developed which consists of a commercial orientation sensor, a wireless communication module, and a power management unit. To recognize complex daily activities, three motion sensor nodes are attached to the right thigh, the waist, and the right hand of a human subject, while an optical motion capture system is used to obtain his/her location information. A three-level dynamic Bayesian network (DBN) is implemented to model the intratemporal and intertemporal constraints among the location, body activity, and hand gesture. The body activity and hand gesture are estimated using a Bayesian filter and a short-time Viterbi algorithm, which reduces the computational complexity and memory usage. We conducted experiments in a mock apartment environment and the obtained results showed the effectiveness and accuracy of our method.