Realtime Human Daily Activity Recognition Through Fusion of Motion and Location Data
As robot assisted living is gaining more attentions for elderly care recently, automated human daily activity recognition becomes more important in human-robot interaction. In this paper, we proposed an approach to indoor human daily activity recognition which combines motion data and location information. One inertial sensor is worn on the right thigh of a human subject to collect motion data, while an optical motion capture system is used to record the human location information. Such a combination has the advantage of significantly reducing the obtrusiveness to the human subject at a moderate cost of vision processing, while maintaining a high accuracy of recognition. First, a two-step algorithm is proposed to recognize the activity based on motion data using the neural networks and a hidden Markov model. Second, to fuse the motion data with the location information, Bayes? Theorem is used to update the activities recognized from the motion data. We conducted experiments in a mock apartment and the obtained results proved the effectiveness and accuracy of our algorithms.
Activity recognition assisted living wearable computing
Chun Zhu Weihua Sheng
School of Electrical and Computer Engineering Oklahoma State UniversityStillwater,OK,74078 School of Electrical and Computer Engineering Oklahoma State University Stillwater,OK,74078
国际会议
2010 IEEE信息与自动化国际会议(ICIA 2010)
哈尔滨
英文
1-6
2010-06-20（万方平台首次上网日期，不代表论文的发表时间）