簡易檢索 / 詳目顯示

研究生: 王詠民
Wang, Yung-Min
論文名稱: 針對於長照機構之人體姿態識別及其應用
An Application of Human Posture Recognition for Long-Term Care Institutions
指導教授: 王偉彥
Wang, Wei-Yen
口試委員: 王偉彥
Wang, Wei-Yen
李宜勳
Li, I-Hsum
彭正偉
Peng, Cheng-Wei
許閔傑
Hsu, Min-Jie
口試日期: 2024/12/30
學位類別: 碩士
Master
系所名稱: 電機工程學系
Department of Electrical Engineering
論文出版年: 2025
畢業學年度: 113
語文別: 中文
論文頁數: 54
中文關鍵詞: 合併式模糊類神經網路小資料量人體姿態識別跌倒偵測離房偵測廁所久待偵測
英文關鍵詞: Merged Fuzzy Neural Network, Small Dataset, Human Posture Recognition, Fall Detection, Room Exit Monitoring, Prolonged Bathroom Stay Detection
DOI URL: http://doi.org/10.6345/NTNU202500436
論文種類: 學術論文
相關次數: 點閱:194下載:3
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 本論文的主要目標為利用彩色影像,結合即時又快速的演算法來進行人體姿態識別佈署在醫院或長照中心以銜接智慧長照場景之各種應用。本研究結合人體估測演算法與合併式模糊類神經網路,提出了一種新的架構來準確地識別人體姿態。首先,我們利用DWPose來從影像中提取人體關鍵點,接著將這些關鍵點進行擴增與不同組合之合併,接著,這些關鍵點被送入合併式模糊類神經網路中進行訓練。針對輸入在不同的擴增與合併組合下,分析出最高準確率的組合,在不同的場景驗證此組合的有效性。根據實驗結果指出所提出方法具有小資料量訓練、受環境影響低、運算速度快的優勢。最後,本文基於此姿態識別,延伸出一些符合長照場景之應用,如跌倒偵測、離房偵測、廁所久待等實際場景。

    The main objective of this thesis is to utilize color images combined with real-time and efficient algorithms for human posture recognition, enabling deployment in hospitals or long-term care centers to support various applications in smart long-term care scenarios. This study integrates human pose estimation algorithms with a Merged Fuzzy Neural Network to propose a novel approach for accurately recognizing human postures. Firstly, DWPose is used to extract the human body's keypoints from images, which are augmented and merged in various onfigurations. Subsequently, the keypoints are fed into the Merged Fuzzy Neural Network for training. Different augmentation and merging configurations are analyzed to identify the one with the highest accuracy, which is further validated for effectiveness in various scenarios. Experimental results indicate that the proposed method offers advantages such as low data requirements for training, robustness to environmental influences, and high computational efficiency. Finally, based on this posture recognition, several applications relevant to long-term care scenarios are developed, such as fall detection, room exit monitoring, and prolonged bathroom stay detection in real-life settings.

    第一章 緒論 1 1.1 研究背景與動機 1 1.2 論文架構 2 第二章 文獻探討 3 2.1 人體姿態識別 3 2.2 人體骨架點估計 5 2.3 DWPose (Distillation for Whole-body Pose Estimators) 6 第三章 基於骨架點之人體姿態辨識系統 11 3.1 合併式模糊類神經網路 11 3.2 合併式模糊類神經網路之不同輸入數量、及組合之分析 17 3.3 系統整合 21 第四章 實驗結果 24 4.1 資料集說明 24 4.2 模型性能指標說明 26 4.3 實驗結果與分析 27 4.4 監控平台 45 4.4.1 跌倒偵測 45 4.4.2 廁所久待 46 4.4.3 離房偵測 47 第五章 結論與未來展望 48 5.1 結論 48 5.2 未來展望 49 參考文獻 50 自 傳 53 學術成就 54

    我國高臨化時程推估, Avaiable: https://ebp.ndc.gov.tw/議題觀測:我國最新人口趨勢與展望|廖育嶒/
    A. Ayre-Storie and L. Zhang, “Deep Learning-Based Human Posture Recognition, ” 2021 International Conference on Machine Learning and Cybernetics (ICMLC), pp. 1-6, 2021.
    Nazaruddin, M. N. Z., Abidin, N. A. Z., Aminuddin, R., Samah, K. A. F. A., Ibrahim, A. Z. M., Yusoh, S. D., ... & Nasir, S. D. N. M, “Utilising the YOLOv3 Algorithm for the Student Posture Recognition System in Classroom Settings, ” 2023 IEEE 8th International Conference on Recent Advances and Innovations in Engineering (ICRAIE), pp. 1-6, 2023.
    K. He, G. Gkioxari, P. Dollár and R. Girshick, “Mask R-CNN, ” 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980-2988, 2017.
    J. Redmon, S. Divvala, R. Girshick and A. Farhadi, “You Only Look Once: Unified, Real Time Object Detection, ” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779-788, 2016.
    A. S. Dileep, N. S. S., S. S., F. K. and S. S., “Suspicious Human Activity Recognition using 2D Pose Estimation and Convolutional Neural Network, ” 2022 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), pp. 19-23, 2022.
    L. Chen, Y. Li and Y. Liu, “Human body gesture recognition method based on deep learning, ” 2020 Chinese Control And Decision Conference (CCDC), pp. 587-591, 2020
    Z. Cao, G. Hidalgo, T. Simon, S. -E. Wei and Y. Sheikh, “OpenPose: Realtime Multi Person 2D Pose Estimation Using Part Affinity Fields, ” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 1, pp. 172-186, 2021.
    Camillo Lugaresi, Jiuqiang Tang, Hadon Nash, Chris McClanahan, Esha Uboweja, Michael Hays, Fan Zhang, Chuo-Ling Chang, Ming Guang Yong, Juhyun Lee, Wan-Teh Chang, Wei Hua, Manfred Georg and Matthias Grundmann, “MediaPipe: A Framework for Building Perception Pipelines, ” arXiv preprint arXiv:1906.08172, 2019
    Z. Yang, A. Zeng, C. Yuan and Y. Li, “Effective Whole-body Pose Estimation with Two stages Distillation,” IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 4212-4222, 2023.
    W. Ding, C. Ding, G. Li and K. Liu, “Skeleton-Based Square Grid for Human Action Recognition With 3D Convolutional Neural Network,” IEEE Access, vol. 9, pp. 54078-54089, 2021.
    K. He, X. Zhang, S. Ren and J. Sun, “Deep Residual Learning for Image Recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, 2016.
    Y.-H. Lin, Y.-H. Chien, M.-J. Hsu, W.-Y. Wang, C.-K. Lu, and C.-C. Hsu, “A Data Monitoring System with Human Action Recognition for Long-Term Care Institutions,” 2023 IEEE International Conference on Consumer Electronics – Taiwan (IEEE ICCE-TW 2023), 2023.
    Looney, Carl G., and Sergiu Dascalu, “A Simple Fuzzy Neural Network,” CAINE, pp. 12-16, 2007.
    Ioffe, Sergey, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” arXiv preprint arXiv:1502.03167, 2015.
    Wenbo Li, Zhicheng Wang, Binyi Yin, Qixiang Peng, Yuming Du, Tianzi Xiao, Gang Yu, Hongtao Lu, Yichen Wei, and Jian Sun, “Rethinking on Multi-Stage Networks for Human Pose Estimation,” arXiv preprint arXiv:1901.00148, 2019.
    Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, Wenyu Liu, and Bin Xiao, “Deep High-Resolution Representation Learning for Visual Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 10, pp. 3349-3364, 2021.
    T. Jiang, P. Lu, L. Zhang, N. Ma, R. Han, C. Lyu, Y. Li, and K. Chen, “RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose,” arXiv preprint arXiv:2303.07399, 2023.
    J. Huang, Z. Zhu, F. Guo and G. Huang, “The Devil Is in the Details: Delving Into Unbiased Data Processing for Human Pose Estimation,” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5699-5708, 2020.
    B. Xiao, H. Wu and Y. Wei, “Simple baselines for human pose estimation and tracking,” European Conference on Computer Vision (ECCV), pp. 466-481, 2018.
    Yanjie Li, Sen Yang, Peidong Liu, Shoukui Zhang, Yunxiao Wang, Zhicheng Wang, Wankou Yang and Shu-Tao Xia, “Simcc: A simple coordinate classification perspective for human pose estimation,” European Conference on Computer Vision, pp. 89-106, 2022.
    紀鴻文, “基於 ROS 之智慧安防自主巡邏履帶式機器人系統,” 國立臺灣師範大學電機工程學系碩士論文, 111年9月.
    Kinect for Windows SDK 2.0, 2014. Available: https://www.microsoft.com/en-us/download/details.aspx?id=44561.
    Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L., “Imagenet: A large-scale hierarchical image database,” 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248-255, 2009.
    J. Chen, “【機器學習】評估模型的方法:混淆矩陣 Confusion Matrix” Available: https://jason-chen-1992.weebly.com/home/-confusion-matrix.
    思創研新, “跌倒偵測腕帶警示器, ” Available: https://www.tcrd.com.tw/product_d.php? lang=tw&tb=1&id=684.
    宜康輔具, “智慧感知墊, ” Available: https://www.yika.com.tw/products_detail/141.html
    Future Life is Here, “環境感測器, ”Available: https://www.flh.com.tw/product-tag/四合一環境感測器/

    下載圖示
    QR CODE