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Open AccessArticle

Nonintrusive Fine-Grained Home Care Monitoring: Characterizing Quality of In-Home Postural Changes Using Bone-Based Human Sensing

1
Graduate School of System Informatics, Kobe University, 1-1 Rokkodai-cho, Nada, Kobe 657-8501, Japan
2
RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(20), 5894; https://doi.org/10.3390/s20205894
Received: 9 September 2020 / Revised: 12 October 2020 / Accepted: 14 October 2020 / Published: 18 October 2020
(This article belongs to the Special Issue Smart IoT Systems for Pervasive Healthcare)
In contrast to the physical activities of able-bodied people at home, most people who require long-term specific care (e.g., bedridden patients and patients who have difficulty walking) usually show more low-intensity slow physical activities with postural changes. Although the existing devices can detect data such as heart rate and the number of steps, they have been increasing the physical burden relying on long-term wearing. The purpose of this paper is to realize a noninvasive fine-grained home care monitoring system that is sustainable for people requiring special care. In the proposed method, we present a novel technique that integrates inexpensive camera devices and bone-based human sensing technologies to characterize the quality of in-home postural changes. We realize a local process in feature data acquisition once per second, which extends from a computer browser to Raspberry Pi. Our key idea is to regard the changes of the bounding box output by standalone pose estimation models in the shape and distance as the quality of the pose conversion, body movement, and positional changes. Furthermore, we use multiple servers to realize distributed processing that uploads data to implement home monitoring as a web service. Based on the experimental results, we conveyed our findings and advice to the subject that include where the daily living habits and the irregularity of home care timings needed improvement. View Full-Text
Keywords: home care monitoring; physical activity; human sensing; postural changes; pose estimation; pose conversion; body movement; positional changes home care monitoring; physical activity; human sensing; postural changes; pose estimation; pose conversion; body movement; positional changes
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Chen, S.; Saiki, S.; Nakamura, M. Nonintrusive Fine-Grained Home Care Monitoring: Characterizing Quality of In-Home Postural Changes Using Bone-Based Human Sensing. Sensors 2020, 20, 5894.

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