Standing Posture Monitoring System for the Elderly
Article Sidebar
Main Article Content
Abstract
The elderly population is increasing rapidly in Thailand, with the majority of seniors living alone at home. Statistics reveal that, on average, four elderly individuals die each day due to falls. This research develops a standing up notification system for the elderly using Raspberry Pi to transmit motion image when they rise from bed. The image is processed using keypoint extraction, and the extracted points are used to verify the correctness of the standing posture by calculating suitable angles from various body parts. The comparison of the detection accuracy of standing postures between two libraries, MediaPipe and OpenPose, using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) shows that MediaPipe has lower errors at 25.19 and 20.35, respectively. The accuracy of standing posture detection is further validated by calculating angles in various degrees, revealing an overall highest average accuracy of 74.55%.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
I/we certify that I/we have participated sufficiently in the intellectual content, conception and design of this work or the analysis and interpretation of the data (when applicable), as well as the writing of the manuscript, to take public responsibility for it and have agreed to have my/our name listed as a contributor. I/we believe the manuscript represents valid work. Neither this manuscript nor one with substantially similar content under my/our authorship has been published or is being considered for publication elsewhere, except as described in the covering letter. I/we certify that all the data collected during the study is presented in this manuscript and no data from the study has been or will be published separately. I/we attest that, if requested by the editors, I/we will provide the data/information or will cooperate fully in obtaining and providing the data/information on which the manuscript is based, for examination by the editors or their assignees. Financial interests, direct or indirect, that exist or may be perceived to exist for individual contributors in connection with the content of this paper have been disclosed in the cover letter. Sources of outside support of the project are named in the cover letter.
I/We hereby transfer(s), assign(s), or otherwise convey(s) all copyright ownership, including any and all rights incidental thereto, exclusively to the Journal, in the event that such work is published by the Journal. The Journal shall own the work, including 1) copyright; 2) the right to grant permission to republish the article in whole or in part, with or without fee; 3) the right to produce preprints or reprints and translate into languages other than English for sale or free distribution; and 4) the right to republish the work in a collection of articles in any other mechanical or electronic format.
We give the rights to the corresponding author to make necessary changes as per the request of the journal, do the rest of the correspondence on our behalf and he/she will act as the guarantor for the manuscript on our behalf.
All persons who have made substantial contributions to the work reported in the manuscript, but who are not contributors, are named in the Acknowledgment and have given me/us their written permission to be named. If I/we do not include an Acknowledgment that means I/we have not received substantial contributions from non-contributors and no contributor has been omitted.
References
กรมกิจการผู้สูงอายุ, “สถิติผู้สูงอายุ มิถุนายน 2566 by Looker Studio,” [ออนไลน์] 2566. สืบค้นวันที่ 18 พฤศจิกายน 2566, https://www.dop.go.th/th/know/side/1/1/2449
กรมควบคุมโรค, “กรมควบคุมโรค ห่วงผู้สูงอายุพลัดตกหกล้มในช่วงฤดูฝน แนะสวมใส่รองเท้าที่พื้นมีดอกยาง และประเมินความเสี่ยงการหกล้มอย่างน้อยปีละ 1 ครั้ง ที่สถานพยาบาลใกล้บ้าน,” [ออนไลน์] 2565. สืบค้นวันที่ 18 พฤศจิกายน 2566, https://ddc.moph.go.th/odpc7/news.php?news=26077&deptcode=odpc7
Maxwell, Vayyar Home. ระบบตรวจจับการล้ม หมดกังวลกับเหตุการณ์ผู้สูงอายุล้มแล้วไม่มีใครรู้. สืบค้นเมื่อ 18 พฤศจิกายน 2566, https://www.maxwell.co.th/vayyar-home
Pobpad. ลุกแล้วหน้ามืด อาการที่คุณไม่ควรเพิกเฉย. สืบค้นวันที่ 18 พฤศจิกายน 2566, https://www.pobpad.com/ลุกแล้วหน้ามืด-อาการที่
La-Z-boy of Thailand. การลุกยืนอย่างปลอดภัย...ในผู้สูงอายุ, La-Z-boy of Thailand. สืบค้นวันที่ 21 ธันวาคม 2567,https://www.lazboythailand.com/th/inspiration/21/standing-safely-...-in-the-elderly
D. Kowerko, D. Richter, M. Heinzig, S. Kahl, S. Helmert, and G. Brunnett, “Evaluation of CNN-based algorithms for human pose analysis of persons in red carpet scenarios,” in Gesellschaft für Informatik, Bonn, pp. 2201–2209, 2017.
Z. Cao, G. Hidalgo, T. Simon, S. -E. Wei and Y. Sheikh, "OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields," in IEEE Transactions on Pattern Analysis and Machine Intelligence, 43 (1): 172-186, 2021.
F. Akhyar et al., "Shoulder and Knee Abnormality Examination Based on Artificial Landmark Estimation," in 2023 Sixth International Symposium on Computer, Consumer and Control (IS3C), Taichung, Taiwan, pp. 36-39, 2023.
V. Bazarevsky, Y. Kartynnik, K. Raveendran, M. Grundmann, and A. Vakunov, "BlazePose: On-device Real-time Body Pose tracking," arXiv:2006.10204v1 [cs.CV], Jun. 2020.
X. Dong et al., “Yh-Pose: Human pose estimation in complex coal mine scenarios,” Engineering Applications of Artificial Intelligence, 127: 107338, 2024.
C. -Y. Wang, A. Bochkovskiy and H. -Y. M. Liao, "YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors," in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada, pp. 7464-7475, 2023.
A. Lobo, J. Lobo, A. Joy and I. A. Mirza, "Yoga Correction Using Machine Learning," in 2022 2nd Asian Conference on Innovation in Technology (ASIANCON), Ravet, India, pp. 1-6, 2022.
C. Lugaresi et al., “MediaPipe: A Framework for Building Perception Pipelines,” arXiv:1906.08172 [cs.DC], Jun. 2019.
M. Gao et al., “Fall detection based on OpenPose and mobilenetv2 network,” IET Image Processing, 17(3): 722–732, 2022.