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Paper Abstract and Keywords
Presentation 2020-05-29 13:30
Characterizing Quality of In-home Physical Activities Using Bone-based Human Sensing
Sinan Chen, Sachio Saiki, Masahide Nakamura (Kobe Univ.) SC2020-1
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, with the conversion to home care in the aging society, it is significant for extending healthy life span on how to continue physical activities at home. Especially, to comprehend signs of disuse atrophy and dementia of the elderly, the physical activity amount at home is an important index.The goal of this study is to measure the physical activity amount of residents in a simple, non-invasive manner, and to connect it with the qualitative evaluation of physical activities at home. More specifically, we capture the in-home real-time video with a fixed-point camera, extract the bone data of a resident using the bone sensing techniques, and accumulate solely the coordinate of feature points as the time-series data. We examine a method to characterize the physical activity amount, which calculates the amount of change from the coordinate data on time series, such as the posture and position of residents. It does not require the wearable devices in the proposed method. The non-invasive physical activity sensing can be achieved since the original in-home video is not accumulated. We first used the machine learning model PoseNet for the pose estimation, which detected the coordinates of the body boundary area and 17 feature points in real-time. We then analyzed the accumulated data, to checked the possible with the quantitation of the basic bone movements, posture changes, and position movements. Such as sitting, standing, and walking.Furthermore, we also discussed the association with standard activity scales (ex., METs), and looked ahead to the specific application of the proposed method.
Keyword (in Japanese) (See Japanese page) 
(in English) Home care / Physical activities / Video / PoseNet / Bone sensing / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 49, SC2020-1, pp. 1-6, May 2020.
Paper # SC2020-1 
Date of Issue 2020-05-22 (SC) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee SC  
Conference Date 2020-05-29 - 2020-05-29 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) AI Application for Service Computing Environment and Other Issues 
Paper Information
Registration To SC 
Conference Code 2020-05-SC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Characterizing Quality of In-home Physical Activities Using Bone-based Human Sensing 
Sub Title (in English)  
Keyword(1) Home care  
Keyword(2) Physical activities  
Keyword(3) Video  
Keyword(4) PoseNet  
Keyword(5) Bone sensing  
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1st Author's Name Sinan Chen  
1st Author's Affiliation Kobe University (Kobe Univ.)
2nd Author's Name Sachio Saiki  
2nd Author's Affiliation Kobe University (Kobe Univ.)
3rd Author's Name Masahide Nakamura  
3rd Author's Affiliation Kobe University (Kobe Univ.)
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Speaker Author-1 
Date Time 2020-05-29 13:30:00 
Presentation Time 25 minutes 
Registration for SC 
Paper # SC2020-1 
Volume (vol) vol.120 
Number (no) no.49 
Page pp.1-6 
#Pages 6 
Date of Issue 2020-05-22 (SC) 

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