博碩士論文 111522132 詳細資訊




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姓名 錢儀安(Yi-An Chien)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 FrAIlti:利用人工智慧和3D攝影技術提升老年照護的自動化衰弱評估系統
(FrAIlti: Enhancing Elderly Care with Automated Frailty Assessment Using AI and 3D Camera Technology)
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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 (2026-7-29以後開放)
摘要(中) 當社會面臨高齡化現象,老年人口比例逐漸攀升,衰弱成為影響老年人健康的重要因素之一。衰弱的特徵在於身體在應對壓力事件後變得脆弱,這是多個生理系統長期衰退的結果。雖然臨床衰弱量表(CFS)是一種常用的評估工具,但容易受到主觀因素和測量時間長的限制。因此,本研究採用了3D攝影機Kinect來收集步態骨架數據,以取代傳統的評估量表,將CFS前五個級別合併為四級。鑒於Kinect可能將要停產的情況,研究團隊將收集的資料轉換為可支援RealSense D435以及其他深度攝影機的Nuitrack骨架偵測點位,並利用機器學習提取三維骨架特徵進行訓練,取得了高達97%的分類準確率。同時,研究還將骨架資訊輸入LSTM(長短期記憶)分類器進行訓練,其準確率達到77%。最終,結合骨架提取的特徵和LSTM分類器找到的時間運動特徵進行訓練,分類準確率提高至100%。此外,實驗證實了利用Nuitrack骨架點配合的深度攝影機進行步態測驗並使用訓練好的模型進行預測的可行性,初步證實了其他多樣深度攝影機作為取代Kinect的深度攝影機的潛力。
摘要(英) As society faces the phenomenon of aging populations, the proportion of elderly individuals gradually increases, with frailty emerging as a significant factor affecting their health. Frailty is characterized by the body becoming vulnerable in response to stressful events, a result of long-term decline in multiple physiological systems. Although the Clinical Frailty Scale (CFS) is a commonly used assessment tool, it is susceptible to subjective factors and lengthy measurement times. Therefore, this study utilized a 3D camera, Kinect, to collect gait skeleton data, replacing traditional assessment scales and consolidating the top five levels of CFS into four levels. Recognizing the potential discontinuation of Kinect, the research team converted collected data into Nuitrack skeleton detection points compatible with RealSense D435. Leveraging machine learning, three-dimensional skeleton features were extracted, achieving an impressive classification accuracy of 97%. Additionally, the study trained a Long Short-Term Memory (LSTM) classifier with skeleton information, achieving a 77% accuracy rate. Ultimately, by combining skeleton-derived features with time-motion characteristics identified by the LSTM classifier, the classification accuracy increased to 100%. Furthermore, experiments confirmed that depth cameras with Nuitrack skeleton points can be used for gait testing and prediction, showing potential as substitutes for the Kinect depth camera.
關鍵字(中) ★ 三維骨架
★ 深度攝影機
★ 衰弱
★ 步態
關鍵字(英) ★ 3D skeletal data
★ Depth camera
★ Frailty
★ Gait
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 8
3. Method 13
4. Results 34
5. Discussions 41
6. Conclusion 43
Reference 45
參考文獻 [1] P. Doody, J. M. Lord, C. A. Greig, and A. C. Whittaker, “Frailty: patho physiology, theoretical and operational definition (s), impact, prevalence, management and prevention, in an increasingly economically developed and ageing world,” Gerontology, 2022.
[2] E. Topinkov´a, “Aging, disability and frailty,” Annals of Nutrition and Metabolism, vol. 52, no. Suppl. 1, pp. 6–11, 2008.
[3] T. Strandberg, K. Pitk¨al¨ a, and R. Tilvis, “Frailty in older people,” European geriatric medicine, vol. 2, no. 6, pp. 344–355, 2011.
[4] HPA, “The assessment of the six abilities in the elderly refers to an integrated evaluation of six capabilities in older individuals,”
[5] A. Tchalla, C. Laubarie-Mouret, N. Cardinaud, C. Gayot, M. Rebiere, N. Dumoitier, K. Rudelle, M. Druet-Cabanac, M.-L. Laroche, and S. Boyer, “Risk factors of frailty and functional disability in community dwelling older adults: a cross-sectional analysis of the freedom-lna cohort study,” BMC geriatrics, vol. 22, no. 1, pp. 1–9, 2022.
[6] L. P. Fried, C. M. Tangen, J. Walston, A. B. Newman, C. Hirsch, J. Gottdiener, T. Seeman, R. Tracy, W. J. Kop, G. Burke, et al., “Frailty in older adults: evidence for a phenotype,” The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, vol. 56, no. 3, pp. M146–M157, 2001.
[7] S. D. Searle, A. Mitnitski, E. A. Gahbauer, T. M. Gill, and K. Rockwood, “A standard procedure for creating a frailty index,” BMC geriatrics, vol. 8, pp. 1–10, 2008. [8] D. M. Jones, X. Song, and K. Rockwood, “Operationalizing a frailty index from a standardized comprehensive geriatric assessment,” Journal of the American Geriatrics Society, vol. 52, no. 11, pp. 1929–1933, 2004.
[9] Y.-C. Chou, H.-H. Tsou, D.-C. D. Chan, C.-J. Wen, F.-P. Lu, K.-P. Lin, M.-C. Wu, Y.-M. Chen, and J.-H. Chen, “Validation of clinical frailty scale in chinese translation,” BMC geriatrics, vol. 22, no. 1, pp. 1–9, 2022.
[10] S. Church, E. Rogers, K. Rockwood, and O. Theou, “A scoping review of the clinical frailty scale,” BMC geriatrics, vol. 20, no. 1, pp. 1–18, 2020.
[11] K. Rockwood and O. Theou, “Using the clinical frailty scale in allocat ing scarce health care resources,” Canadian Geriatrics Journal, vol. 23, no. 3, p. 210, 2020.
[12] S. Juma, M.-M. Taabazuing, and M. Montero-Odasso, “Clinical frailty scale in an acute medicine unit: a simple tool that predicts length of stay,” Canadian Geriatrics Journal, vol. 19, no. 2, p. 34, 2016.
[13] A. K. Triantafyllidis and A. Tsanas, “Applications of machine learning in real-life digital health interventions: review of the literature,” Journal of medical Internet research, vol. 21, no. 4, p. e12286, 2019.
[14] M. Chen, B. Huang, and Y. Xu, “Intelligent shoes for abnormal gait detection,” in 2008 IEEE international conference on robotics and automation, pp. 2019–2024, IEEE, 2008.
[15] J. D. Mejia-Trujillo, Y. J. Castano-Pino, A. Navarro, J. D. Arango Paredes, D. Rinc´on, J. Valderrama, B. Munoz, and J. L. Orozco, “Kinect™ and intel realsense™ d435 comparison: A preliminary study for motion analysis,” in 2019 IEEE International Conference on E-health Networking, Application & Services (HealthCom), pp. 1–4, IEEE, 2019.
[16] R. Liu, Z. Wang, S. Qiu, H. Zhao, C. Wang, X. Shi, and F. Lin, “A wearable gait analysis and recognition method for parkinson’s disease based on error state kalman filter,” IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 8, pp. 4165–4175, 2022.
[17] N. Roth, M. Ullrich, A. K¨ uderle, T. Gladow, F. Marxreiter, H. Gassner, F. Kluge, J. Klucken, and B. M. Eskofier, “Real-world stair ambulation characteristics differ between prospective fallers and non-fallers in parkinson’s disease,” IEEE journal of biomedical and health informatics, vol. 26, no. 9, pp. 4733–4742, 2022.
[18] M. Ullrich, N. Roth, A. K¨uderle, R. Richer, T. Gladow, H. Gaßner, F. Marxreiter, J. Klucken, B. M. Eskofier, and F. Kluge, “Fall risk prediction in parkinson’s disease using real-world inertial sensor gait data,” IEEE journal of biomedical and health informatics, vol. 27, no. 1, pp. 319–328, 2022.
[19] A. Apsega, L. Petrauskas, V. Alekna, K. Daunoraviciene, V. Sevcenko, A. Mastaviciute, D. Vitkus, M. Tamulaitiene, and J. Griskevicius, “Wearable sensors technology as a tool for discriminating frailty levels during instrumented gait analysis,” Applied Sciences, vol. 10, no. 23, p. 8451, 2020.
[20] M. Montero-Odasso, S. W. Muir, M. Hall, T. J. Doherty, M. Kloseck, O. Beauchet, and M. Speechley, “Gait variability is associated with frailty in community-dwelling older adults,” Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences, vol. 66, no. 5, pp. 568–576, 2011.
[21] G. Akbari, M. Nikkhoo, L. Wang, C. P. Chen, D.-S. Han, Y.-H. Lin, H. B. Chen, and C.-H. Cheng, “Frailty level classification of the community elderly using microsoft kinect-based skeleton pose: A machine learning approach,” Sensors, vol. 21, no. 12, p. 4017, 2021.
[22] N. Eichler, S. Raz, A. Toledano-Shubi, D. Livne, I. Shimshoni, and H. Hel-Or, “Automatic and efficient fall risk assessment based on machine learning,” Sensors, vol. 22, no. 4, p. 1557, 2022.
[23] B. Mobasheri, S. R. K. Tabbakh, and Y. Forghani, “An approach for fall prediction based on kinematics of body key points using lstm,” International journal of environmental research and public health, vol. 19, no. 21, p. 13762, 2022.
[24] W. Zhu, C. Lan, J. Xing, W. Zeng, Y. Li, L. Shen, and X. Xie, “Co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks,” in Proceedings of the AAAI conference on artificial intelligence, vol. 30, 2016.
[25] Physiopedia, “Functional reach test (frt) — physiopedia,,” 2024. [Online; accessed 23-July-2024].
[26] Physiopedia, “Tinetti test — physiopedia,,” 2023. [Online; accessed 23-July-2024].
[27] Physiopedia, “Timed up and go test (tug) — physiopedia,,” 2024. [Online; accessed 23-July-2024].
[28] Physiopedia, “Grip strength — physiopedia,,” 2024. [Online; accessed 23-July-2024].
[29] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, et al., “Scikit-learn: Machine learning in python,” the Journal of machine Learning research, vol. 12, pp. 2825–2830, 2011.
[30] C.-W. Hsu, C.-C. Chang, C.-J. Lin, et al., “A practical guide to support vector classification,” 2003.
[31] Y.-Y. Song and L. Ying, “Decision tree methods: applications for classification and prediction,” Shanghai archives of psychiatry, vol. 27, no. 2, p. 130, 2015.
[32] D. W. Hosmer Jr, S. Lemeshow, and R. X. Sturdivant, Applied logistic regression. John Wiley & Sons, 2013.
[33] L. E. Peterson, “K-nearest neighbor,” Scholarpedia, vol. 4, no. 2, p. 1883, 2009.
[34] A. A. Ibrahim, R. L. Ridwan, M. M. Muhammed, R. O. Abdulaziz, and G. A. Saheed, “Comparison of the catboost classifier with other machine learning methods,” International Journal of Advanced Computer Science and Applications, vol. 11, no. 11, 2020.
[35] B. Parhami, “Voting algorithms,” IEEE transactions on reliability, vol. 43, no. 4, pp. 617–629, 1994.
[36] M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, et al., “{TensorFlow}: a system for {Large-Scale} machine learning,” in 12th USENIX symposium on operating systems design and implementation (OSDI 16), pp. 265–283, 2016.
[37] A. Sherstinsky, “Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network,” Physica D: Nonlinear Phe nomena, vol. 404, p. 132306, 2020.
[38] C. R. Harris, K. J. Millman, S. J. Van Der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N. J. Smith, IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 14 et al., “Array programming with numpy,” Nature, vol. 585, no. 7825, pp. 357–362, 2020.
[39] D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014.
指導教授 葉士青 吳曉光(Shih-Ching Yeh Hsiao-Kuang Wu) 審核日期 2024-8-5
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