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Extracting patterns of behavior from a network of binary sensors

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Abstract

According to the World Health Organization and the US Department of Health, older adults are one of the most rapidly expanding population segments of many developed countries. As a result, significant research in the area of assistive technologies for the elderly has been undertaken to help manage this growing demographic segment. Our contribution to this effort is based on leveraging environmental sensors to track occupants for extended durations in their homes in order to extract and track various behavioral patterns in order to identify atypical activities. To aid in our detection of behavioral patterns, we extract additional intermediate indicators of mobility and behavior such as estimates of an occupant’s position as well as other parameters such as guest visitations. In our initial research, we did not associate these instances of atypical behaviors with any specific health events. Our primary goal was to see if it was possible to detect atypical behaviors using only the limited data that can be extracted using passive infrared sensors. We compared the days when atypical patterns were detected with the similar analysis of a human annotated activity log and determined that there is a strong correlation between patterns extracted from our derived behavioral indicators and those from the annotated activities. We also compared our systems result’s across multiple datasets to verify that our approach could be applied to different sensor configurations and occupants.

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Acknowledgments

We would like to thank WSU’s CASAS project for both gathering and allowing other research institutions to use their Smart Home datasets.

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Authors and Affiliations

  1. Depatment of Electrical Engineering, Stanford University, Stanford, CA, USA

    Kevin Bing-Yung Wong, Tongda Zhang & Hamid Aghajan

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  1. Kevin Bing-Yung Wong
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Correspondence to Kevin Bing-Yung Wong.

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Wong, K.BY., Zhang, T. & Aghajan, H. Extracting patterns of behavior from a network of binary sensors. J Ambient Intell Human Comput 6, 83–105 (2015). https://doi.org/10.1007/s12652-014-0246-7

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