Sensor Network Oriented Human Motion Segmentation With Motion Change Measurement

• Main Authors: Yang Liu, Lin Feng, Shenglan Liu, Muxin Sun
• Format: Article
• Language: English
• Published: IEEE 2018-01-01
• Series: IEEE Access
• Subjects: Sensor networks; human motion sequence segmentation; hashing learning; motion change measurement
• Online Access: https://ieeexplore.ieee.org/document/8240911/

Smart Internet of Things has greatly improved the quality of human life with increasingly intelligent sensor networks. Efficient and accurate human motion time series segmentation is the key issue in human motion analysis and understanding. To realize human motion sequence segmentation, a comprehensive human motion description and an intelligent segmentation algorithm are required. Hence, this paper proposes a sensor network-based human motion sequence segmentation framework. With the facilitation of sensor network and sensor network-based feature fusion method, human motions can be comprehensively described. Based on the comprehensive description of motion data, a new motion change variation-based segmentation method is proposed to realize human motion sequence segmentation. Moreover, to satisfy the time efficiency demand in the applications of large scale sensor networks, a hashing algorithm is introduced to compress the original captured sensor data, which can effectively represent the human motions with short binary codes and facilitate the motion change measurement. Experiments on real-world human motion data sets validate the effectiveness of our proposed sensor network-based human motion sequence segmentation framework compared with other state-of-the-art human motion segmentation methods.