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Sensors 2014, 14(5), 7831-7856; doi:10.3390/s140507831

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

AGH-University of Science and Technology, 30, Mickiewicz Ave., 30-059 Kraków, Poland
Author to whom correspondence should be addressed.
Received: 14 January 2014 / Revised: 15 April 2014 / Accepted: 24 April 2014 / Published: 29 April 2014
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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. View Full-Text
Keywords: ambient assisted living; surveillance; home care; aging society ambient assisted living; surveillance; home care; aging society

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Augustyniak, P.; Smoleń, M.; Mikrut, Z.; Kańtoch, E. Seamless Tracing of Human Behavior Using Complementary Wearable and House-Embedded Sensors. Sensors 2014, 14, 7831-7856.

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