| Paper Abstract and Keywords |
| Presentation |
2024-11-28 16:05
Classification of workplace human body postures using 3D point cloud data with data augmentation Jumpei Watanabe, Ryoichi Shinkuma, Gabriele Trovato (SIT) SRW2024-34 SeMI2024-46 |
| Abstract |
(in Japanese) |
(See Japanese page) |
| (in English) |
The office-efficiency-monitoring market has grown. There are some studies for monitoring human postures in office environment. However, there are challenges remain with distinguishing hand movements. This paper proposes an edge computing system for classifying office workers' postures using a LiDAR sensor network. The system acquires human poses, performs preprocessing steps like trimming, calibration, feature estimation, clustering, and normalization, and classifies postures using deep learning. To evaluate the system, human posture data from multiple LiDAR sensors was captured, preprocessed, and classified. By performing data augmentation and increasing the amount of data, we aimed to improve classification accuracy and generalize the data. |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
physical activity in office / light detection and ranging / deep learning / / / / / |
| Reference Info. |
IEICE Tech. Rep., vol. 124, no. 276, SRW2024-34, pp. 37-40, Nov. 2024. |
| Paper # |
SRW2024-34 |
| Date of Issue |
2024-11-21 (SRW, 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 |
SRW2024-34 SeMI2024-46 |
| Conference Information |
| Committee |
SRW SeMI |
| Conference Date |
2024-11-28 - 2024-11-29 |
| Place (in Japanese) |
(See Japanese page) |
| Place (in English) |
Tokyo Univ. of Science Katsushika Campus |
| Topics (in Japanese) |
(See Japanese page) |
| Topics (in English) |
IoT Workshop |
| Paper Information |
| Registration To |
SRW |
| Conference Code |
2024-11-SRW-SeMI |
| Language |
Japanese |
| Title (in Japanese) |
(See Japanese page) |
| Sub Title (in Japanese) |
(See Japanese page) |
| Title (in English) |
Classification of workplace human body postures using 3D point cloud data with data augmentation |
| Sub Title (in English) |
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| Keyword(1) |
physical activity in office |
| Keyword(2) |
light detection and ranging |
| Keyword(3) |
deep learning |
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| 1st Author's Name |
Jumpei Watanabe |
| 1st Author's Affiliation |
Shibaura Institute of technology (SIT) |
| 2nd Author's Name |
Ryoichi Shinkuma |
| 2nd Author's Affiliation |
Shibaura Institute of technology (SIT) |
| 3rd Author's Name |
Gabriele Trovato |
| 3rd Author's Affiliation |
Shibaura Institute of technology (SIT) |
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| Speaker |
Author-1 |
| Date Time |
2024-11-28 16:05:00 |
| Presentation Time |
25 minutes |
| Registration for |
SRW |
| Paper # |
SRW2024-34, SeMI2024-46 |
| Volume (vol) |
vol.124 |
| Number (no) |
no.276(SRW), no.277(SeMI) |
| Page |
pp.37-40 |
| #Pages |
4 |
| Date of Issue |
2024-11-21 (SRW, SeMI) |