Realtime human daily activity recognition through fusion of motion and location data

Abstract

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.