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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.
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) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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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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