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| Presentation | 2020-11-27 10:20 Detection of human activity based on hybrid deep learning model using a low-resolution infrared array sensor. Muthukumar K A, Mondher Bouazizi, Tomoaki Ohtsuki (Keio Univ.)  SeMI2020-39 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Artificial Intelligence (AI) plays a significant role in the healthcare industry. Many applications have been developed using AI in healthcare. Among these, activity detection is one of the most important applications. Many AI-based activity detection systems use conventional machine learning methods to detect various activities. In a conventional machine learning model, activity features are manually extracted and detected which presents one the main drawbacks of this family of techniques.. This report proposes an activity detection approach based on a hybrid deep learning model using a low-resolution infrared array sensor placed on the ceiling. The hybrid deep learning model automatically learns the features and detect the activity. Upon training, the classification is performed faster than that using conventional machine learning models.. The data collected from the infrared array sensor is classified using a CNN (Convolutional Neural Network) where each frame is individually classified. The CNN’s output is passed to the LSTM (Long Short Term Memory) for sequential classification with a time window size equal to five frames. The classification accuracy reach 96.60% and 97.74% for the CNN and the CNN+LSTM models, respectively, respectively. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | AI healthcare / activity detection / hybdrid deep learing / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 261, SeMI2020-39, pp. 99-104, Nov. 2020. | |
| Paper # | SeMI2020-39 | |
| Date of Issue | 2020-11-19 (SeMI) | |
| 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) | |
| Download PDF | SeMI2020-39 |
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| Conference Information | |
| Committee | SRW SeMI CNR |
| Conference Date | 2020-11-26 - 2020-11-27 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | IoT Workshop |
| Paper Information | |
| Registration To | SeMI |
| Conference Code | 2020-11-SRW-SeMI-CNR |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Detection of human activity based on hybrid deep learning model using a low-resolution infrared array sensor. |
| Sub Title (in English) | |
| Keyword(1) | AI healthcare |
| Keyword(2) | activity detection |
| Keyword(3) | hybdrid deep learing |
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| 1st Author's Name | Muthukumar K A |
| 1st Author's Affiliation | Keio University (Keio Univ.) |
| 2nd Author's Name | Mondher Bouazizi |
| 2nd Author's Affiliation | Keio University (Keio Univ.) |
| 3rd Author's Name | Tomoaki Ohtsuki |
| 3rd Author's Affiliation | Keio University (Keio Univ.) |
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| Speaker | Author-1 |
| Date Time | 2020-11-27 10:20:00 |
| Presentation Time | 20 minutes |
| Registration for | SeMI |
| Paper # | SeMI2020-39 |
| Volume (vol) | vol.120 |
| Number (no) | no.261 |
| Page | pp.99-104 |
| #Pages | 6 |
| Date of Issue | 2020-11-19 (SeMI) |
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