Seamless Tracing of Human Behavior Using Complementary Wearable and House-Embedded Sensors

Sensors

Piotr Augustyniak¹  Magdalena Smoleń²  Zbigniew Mikrut² 

[1]AGH-University of Science and Technology, 30, Mickiewicz Ave., 30-059 Kraków,Poland

关键词: ambient assisted living;    surveillance;    home care;    aging society;   

DOI  :  10.3390/s140507831

来源: mdpi

【 摘 要 】

This paper presents a multimodal system for seamless surveillance of elderly people in their living environment. The system uses simultaneously a wearable sensor network for each individual and premise-embedded sensors specific for each environment. The paper demonstrates the benefits of using complementary information from two types of mobility sensors: visual flow-based image analysis and an accelerometer-based wearable network. The paper provides results for indoor recognition of several elementary poses and outdoor recognition of complex movements. Instead of complete system description, particular attention was drawn to a polar histogram-based method of visual pose recognition, complementary use and synchronization of the data from wearable and premise-embedded networks and an automatic danger detection algorithm driven by two premise- and subject-related databases. The novelty of our approach also consists in feeding the databases with real-life recordings from the subject, and in using the dynamic time-warping algorithm for measurements of distance between actions represented as elementary poses in behavioral records. The main results of testing our method include: 95.5% accuracy of elementary pose recognition by the video system, 96.7% accuracy of elementary pose recognition by the accelerometer-based system, 98.9% accuracy of elementary pose recognition by the combined accelerometer and video-based system, and 80% accuracy of complex outdoor activity recognition by the accelerometer-based wearable system.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.