Segmentation and recognition of human motion sequences using wearable inertial sensors

The application of human motion monitoring technology based on wearable inertial sensors has achieved great success in the last ten years. But now the research is mainly focused on isolated motion recognition, and there is scarce research on recognition of human motion sequences. In this paper a novel monitoring framework of human motion sequences is proposed based on wearable inertial sensors. The monitoring framework is composed of data acquisition, segmentation, and recognition stages; the main work of this paper is the last two parts. At the segmentation stage, SVD is used to perform pre-segmentation of motion sequence and its purpose is to reduce time in the segmentation process as much as possible. Then a novel similarity measure named MSHsim is proposed to accomplish the fine segmentation. At the recognition stage an HMM is used to recognize the motion sequence. We use four inertial sensors to collect the human motion data. Experiments are implemented to evaluate the performance of the proposed monitoring framework, and from the experiment results, it can be seen that the proposed method may achieve better performance compared to other methods.