使用映射模型和跨資料集遷移式學習的輕量化居家衰弱症訓練系統

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：4

、訪客IP：3.14.83.223

姓名

胡瑄(Hsuan Hu) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

使用映射模型和跨資料集遷移式學習的輕量化居家衰弱症訓練系統
(A Lightweight Home-Based Frailty Training System using Mapping Model and Cross-Dataset Transfer Learning)

相關論文

★ 探討Media-Pipe和Leap Motion藉由用於發展遲緩的VR系統在分層共現網路的對指手勢預測精準度比較	★ 基於眼動的閱讀障礙分析與診斷
★ 在有干擾的虛擬教室環境下大人小孩的行為表現與腦神經反應的異同	★ 心率生理回饋放鬆訓練對於海洛因使用疾患(HUD)生理資訊之影響分析
★ 基於深度學習模型的3D心理旋轉對認知障礙的診斷與評估	★ 評估注意力偵測之穿戴式腦電電極放置有效性

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

隨著人口老齡化，衰弱症的問題越來越嚴重。許多研究指出，運動可以有效減緩衰弱症狀。與劇烈運動相比，八段錦相當適合衰弱症患者。它由八個簡單的動作組成。許多物理治療師也使用這種常見的氣功來訓練體弱的病人。為了給他們提供更好的訓練方法，本文提出了一種輕量級的基於家庭的衰弱症訓練系統。在系統中，我們設計了一個虛擬八段錦教練。為了達到輕量化的目的，我們使用網路攝影機作為主要設備。該系統還支持 Kinect 框架。我們使用姿勢估計和運動識別方法來分析使用者的運動。除此之外我們提出了一種新的遷移學習方法。我們設計了一個名為“Skeleton Mapnet”的映射模型。其目的是轉換不同框架的骨架數據。該方法使不同框架的數據集能夠共享分類模型。它還可以混合不同框架的骨架數據，解決網路攝影機數據集的不足。這樣的設計提供了系統輕鬆移植到其他平台的能力。此外，該系統還適用於物聯網人工智能的使用。它可以使衰弱症的患者更容易學習和操作。

摘要(英)

The problem of frailty is becoming more and more serious with the aging of the population. Many studies have pointed out that exercise can effectively slow down frailty. Compared with vigorous exercise, Baduanjin is quite suitable for frailty patients. It is a traditional Chinese qigong and consists of eight simple movements. Many physical therapists also use this exercise for training frailty patients. To provide them with a better training method, this paper proposes a lightweight family-based frailty training system. In the system, we designed a virtual Baduanjin coach. To achieve the purpose of being lightweight, we use a webcam as the main device. The system also supports the Kinect framework. We use pose estimation and motion recognition methods to analyze the user′s movements. In addition, a novel transfer learning method is proposed. We designed a mapping model called "Skeleton Mapnet". Its purpose is to convert skeleton data of different frameworks. This method enables datasets of different frameworks to share classification models. It can also mix skeleton data of different frameworks to solve the lack of webcam datasets. Such a design provides the ability of the system to be easily ported to other platforms. In addition, the system is also suitable for the use of the Artificial Intelligence of Things. It can make it easier for frailty patients to learn and operate.

關鍵字(中)

★ 衰弱症
★ 虛擬現實
★ 姿態估算
★ 動作辨識
★ 遷移式學習

關鍵字(英)

★ Frailty
★ Virtual Reality
★ Pose Estimation
★ Action Recognition
★ Transfer Learning

論文目次

摘要 I
Abstract II
致謝 III
Table of Contents IV
List of Figures V
List of Tables VI
1. Introduction 1
2. Related Works 5
3. Method 10
4. Results 21
5. Discussions 27
6. Conclusion and Future Works 29
Reference 31

參考文獻

[1] Clegg, A., Young, J., Iliffe, S., Rikkert, M. O., & Rockwood, K. (2013). Frailty in elderly people. The lancet, 381(9868), 752-762.
[2] Church, S., Rogers, E., Rockwood, K., & Theou, O. (2020). A scoping review of the Clinical Frailty Scale. BMC geriatrics, 20(1), 1-18.
[3] Bagshaw, S. M., Stelfox, H. T., McDermid, R. C., Rolfson, D. B., Tsuyuki, R. T., Baig, N., ... & Majumdar, S. R. (2014). Association between frailty and short-and long-term outcomes among critically ill patients: a multicentre prospective cohort study. Cmaj, 186(2), E95-E102.
[4] Bagshaw, S. M., Stelfox, H. T., Johnson, J. A., McDermid, R. C., Rolfson, D. B., Tsuyuki, R. T., ... & Majumdar, S. R. (2015). Long-term association between frailty and health-related quality of life among survivors of critical illness: a prospective multicenter cohort study. Critical care medicine, 43(5), 973-982.
[5] Apóstolo, J., Cooke, R., Bobrowicz-Campos, E., Santana, S., Marcucci, M., Cano, A., ... & Holland, C. (2018). Effectiveness of interventions to prevent pre-frailty and frailty progression in older adults: a systematic review. JBI database of systematic reviews and implementation reports, 16(1), 140.
[6] Jadczak, A. D., Luscombe-Marsh, N., Taylor, P., Barnard, R., Makwana, N., & Visvanathan, R. (2018). The express study: exercise and protein effectiveness supplementation study supporting autonomy in community dwelling frail older people‐study protocol for a randomized controlled pilot and feasibility study. Pilot and feasibility studies, 4(1), 1-10.
[7] Liu, X., Ng, D. H. M., Seah, J. W. T., Munro, Y. L., & Wee, S. L. (2019). Update on interventions to prevent or reduce frailty in community-dwelling older adults: A scoping review and community translation. Current Geriatrics Reports, 8(2), 72-86.
[8] Yamada, M., Arai, H., Sonoda, T., & Aoyama, T. (2012). Community-based exercise program is cost-effective by preventing care and disability in Japanese frail older adults. Journal of the American Medical Directors Association, 13(6), 507-511.
[9] Zheng, G., Chen, B., Fang, Q., Yi, H., Lin, Q., Chen, L., ... & Lan, X. (2014). Primary prevention for risk factors of ischemic stroke with Baduanjin exercise intervention in the community elder population: study protocol for a randomized controlled trial. Trials, 15(1), 1-10.
[10] Guo, Y., Shi, H., Yu, D., & Qiu, P. (2016). Health benefits of traditional Chinese sports and physical activity for older adults: a systematic review of evidence. Journal of Sport and Health Science, 5(3), 270-280.
[11] Paliokas, I., Kalamaras, E., Votis, K., Doumpoulakis, S., Lakka, E., Kotsani, M., ... & Tzovaras, D. (2020). Using a virtual reality serious game to assess the performance of older adults with frailty. In GeNeDis 2018 (pp. 127-139). Springer, Cham.
[12] Zak, M., Sikorski, T., Wasik, M., Courteix, D., Dutheil, F., & Brola, W. (2022). Frailty Syndrome—Fall Risk and Rehabilitation Management Aided by Virtual Reality (VR) Technology Solutions: A Narrative Review of the Current Literature. International journal of environmental research and public health, 19(5), 2985.
[13] Akbari, G., Nikkhoo, M., Wang, L., Chen, C. P., Han, D. S., Lin, Y. H., ... & Cheng, C. H. (2021). Frailty level classification of the community elderly using Microsoft Kinect-based skeleton pose: A machine learning approach. Sensors, 21(12), 4017.
[14] Gamra, M. B., & Akhloufi, M. A. (2021). A review of deep learning techniques for 2D and 3D human pose estimation. Image and Vision Computing, 114, 104282.
[15] Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., ... & Grundmann, M. (2019). Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172.
[16] Mroz, S., Baddour, N., McGuirk, C., Juneau, P., Tu, A., Cheung, K., & Lemaire, E. (2021, December). Comparing the Quality of Human Pose Estimation with BlazePose or OpenPose. In 2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART) (pp. 1-4). IEEE.
[17] Bai, J., & Song, A. (2019). Development of a novel home based multi-scene upper limb rehabilitation training and evaluation system for post-stroke patients. IEEE access, 7, 9667-9677.
[18] Morando, M., Trombini, M., & Dellepiane, S. (2019, April). Application of svm for evaluation of training performance in exergames for motion rehabilitation. In Proceedings of the 2019 international conference on intelligent medicine and image processing (pp. 1-5).
[19] Lowes, L. P., Alfano, L. N., Yetter, B. A., Worthen-Chaudhari, L., Hinchman, W., Savage, J., ... & Mendell, J. R. (2013). Proof of concept of the ability of the kinect to quantify upper extremity function in dystrophinopathy. PLoS currents, 5.
[20] Cao, L., Fan, C., Wang, H., & Zhang, G. (2019). A novel combination model of convolutional neural network and long short-term memory network for upper limb evaluation using kinect-based system. IEEE Access, 7, 145227-145234.
[21] Yan, S., Xiong, Y., & Lin, D. (2018, April). Spatial temporal graph convolutional networks for skeleton-based action recognition. In Thirty-second AAAI conference on artificial intelligence.
[22] Song, S., Lan, C., Xing, J., Zeng, W., & Liu, J. (2018). Spatio-temporal attention-based LSTM networks for 3D action recognition and detection. IEEE Transactions on image processing, 27(7), 3459-3471.
[23] Plizzari, C., Cannici, M., & Matteucci, M. (2021, January). Spatial temporal transformer network for skeleton-based action recognition. In International Conference on Pattern Recognition (pp. 694-701). Springer, Cham.
[24] Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., ... & He, Q. (2020). A comprehensive survey on transfer learning. Proceedings of the IEEE, 109(1), 43-76.
[25] Pan, S. J., & Yang, Q. (2009). A survey on transfer learning. IEEE Transactions on knowledge and data engineering, 22(10), 1345-1359.
[26] Li, H., Khoo, S., & Yap, H. J. (2022). Implementation of Sequence-Based Classification Methods for Motion Assessment and Recognition in a Traditional Chinese Sport (Baduanjin). International Journal of Environmental Research and Public Health, 19(3), 1744.
[27] Li, C., Zhong, Q., Xie, D., & Pu, S. (2018). Co-occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation. arXiv preprint arXiv:1804.06055.
[28] Sers, Ryan, et al. "Validity of the Perception Neuron inertial motion capture system for upper body motion analysis." Measurement 149 (2020): 107024.
[29] Wang, Hongsong, and Liang Wang. "Learning content and style: Joint action recognition and person identification from human skeletons." Pattern Recognition 81 (2018): 23-35.
[30] Jegham, Imen, et al. "Vision-based human action recognition: An overview and real world challenges." Forensic Science International: Digital Investigation 32 (2020): 200901.
[31] Zhang, Zixuan, et al. "Artificial Intelligence‐Enabled Sensing Technologies in the 5G/Internet of Things Era: From Virtual Reality/Augmented Reality to the Digital Twin." Advanced Intelligent Systems (2022): 2100228.
[32] Lai, Yi-Chun, et al. "AIoT-Enabled Rehabilitation Recognition System—Exemplified by Hybrid Lower-Limb Exercises." Sensors 21.14 (2021): 4761.
[33] Busto, Pau Panareda, Ahsan Iqbal, and Juergen Gall. "Open set domain adaptation for image and action recognition." IEEE transactions on pattern analysis and machine intelligence 42.2 (2018): 413-429.
[34] Long, Chhaihuoy, Eunhye Jo, and Yunyoung Nam. "Development of a yoga posture coaching system using an interactive display based on transfer learning." The Journal of Supercomputing 78.4 (2022): 5269-5284.
[35] Nguyen, Tien‐Thanh, et al. "A robust and efficient method for skeleton‐based human action recognition and its application for cross‐dataset evaluation." IET Computer Vision (2022).
[36] Tölgyessy, Michal, et al. "Evaluation of the azure Kinect and its comparison to Kinect V1 and Kinect V2." Sensors 21.2 (2021): 413.
[37] Tölgyessy, Michal, Martin Dekan, and Ľuboš Chovanec. "Skeleton tracking accuracy and precision evaluation of Kinect V1, Kinect V2, and the azure kinect." Applied Sciences 11.12 (2021): 5756.
[38] Bengio, Yoshua. "Learning deep architectures for AI." Foundations and trends® in Machine Learning 2.1 (2009): 1-127.
[39] Yu, Jun, et al. "Multitask autoencoder model for recovering human poses." IEEE Transactions on Industrial Electronics 65.6 (2017): 5060-5068.
[40] Jun, Kooksung, et al. "Feature extraction using an RNN autoencoder for skeleton-based abnormal gait recognition." IEEE Access 8 (2020): 19196-19207.

指導教授

葉士青吳曉光(Shih-Ching Yeh Eric Hsiao-Kuang Wu)

審核日期

2022-8-1

推文