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Paper Abstract and Keywords
Presentation 2023-12-07 11:15
On Modeling Daily Activities of the Elderly Living Alone Using Markov Series of Dirichlet Multinomial Mixture Models
Ken Sadohara (AIST) WIT2023-30
Abstract (in Japanese) (See Japanese page) 
(in English) To develop smart home technology designed to analyze the activity of residents based on the logs of installed sensors, an activity model tailored to individuals must be constructed from less privacy-invasive sensors to avoid interference in daily life. Unsupervised machine learning techniques are desirable to automatically construct such models without costly data annotation, but their application has not yet been sufficiently successful. In this study, we show that an activity model without activity labels can be effectively estimated via the Dirichlet multinomial mixture (DMM) model. The DMM model assumes that sensor signals are generated according to a Dirichlet multinomial distribution conditioned on a single unobservable activity and can capture the burstiness of sensors, in that even sensors that rarely fire may fire repeatedly after being triggered. We demonstrate the burstiness phenomenon in real data using passive infrared ray motion sensors. For such data, the assumptions of the DMM model are more suitable than the assumptions employed in models used in previous studies. Moreover, we extend the DMM model so that each activity depends on the preceding activity to capture the Markov dependency of activities, and a Gibbs sampler used in the model estimation algorithm is also presented. An empirical study using publicly available data collected in real-life settings shows that the DMM models can discover activities more correctly than the other models.
Keyword (in Japanese) (See Japanese page) 
(in English) activity discovery / ADLs / unsupervised learning / topic models / smart home / burstiness / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 294, WIT2023-30, pp. 31-36, Dec. 2023.
Paper # WIT2023-30 
Date of Issue 2023-11-29 (WIT) 
ISSN Online edition: ISSN 2432-6380
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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 WIT HI-SIGACI  
Conference Date 2023-12-06 - 2023-12-07 
Place (in Japanese) (See Japanese page) 
Place (in English) AIST Tokyo Waterfront (TBD) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Well-being Information Technology 
Paper Information
Registration To WIT 
Conference Code 2023-12-WIT-SIGACI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) On Modeling Daily Activities of the Elderly Living Alone Using Markov Series of Dirichlet Multinomial Mixture Models 
Sub Title (in English)  
Keyword(1) activity discovery  
Keyword(2) ADLs  
Keyword(3) unsupervised learning  
Keyword(4) topic models  
Keyword(5) smart home  
Keyword(6) burstiness  
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1st Author's Name Ken Sadohara  
1st Author's Affiliation National Institute of Advanced Industrial Science and Technology (AIST)
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Speaker Author-1 
Date Time 2023-12-07 11:15:00 
Presentation Time 25 minutes 
Registration for WIT 
Paper # WIT2023-30 
Volume (vol) vol.123 
Number (no) no.294 
Page pp.31-36 
#Pages
Date of Issue 2023-11-29 (WIT) 


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