Skip navigation

DSpace

機構典藏 DSpace 系統致力於保存各式數位資料(如:文字、圖片、PDF)並使其易於取用。

點此認識 DSpace
DSpace logo
English
中文
  • 瀏覽論文
    • 校院系所
    • 出版年
    • 作者
    • 標題
    • 關鍵字
    • 指導教授
  • 搜尋 TDR
  • 授權 Q&A
    • 我的頁面
    • 接受 E-mail 通知
    • 編輯個人資料
  1. NTU Theses and Dissertations Repository
  2. 醫學院
  3. 物理治療學系所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/95098
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor徐瑋勵zh_TW
dc.contributor.advisorWei-Li HSUen
dc.contributor.author黃怡婷zh_TW
dc.contributor.authorYi-Ting Huangen
dc.date.accessioned2024-08-28T16:15:21Z-
dc.date.available2024-08-29-
dc.date.copyright2024-08-28-
dc.date.issued2024-
dc.date.submitted2024-07-29-
dc.identifier.citation[1] G. L. K. Shum, J. Crosbie,R.Y.W. Lee, Effect of low back pain on the kinematics and joint coordination of the lumbar spine and hip during sit-to-stand and stand-to-sit movements. Spine, 2005. 30(17).
[2] J. A. Sedrez, P.V. Mesquita, G.M. Gelain,C. T. Candotti, Kinematic characteristics of sit-to-stand movements in patients with low back pain: a systematic review. Journal of Manipulative and Physiological Therapeutics, 2019. 42(7): p. 532-540.
[3] L. Muñoz-Bermejo, J.C. Adsuar, M. Mendoza-Muñoz, S. Barrios-Fernández, M.A. Garcia-Gordillo, J. Pérez-Gómez, et al., Test-retest reliability of the Five Times Sit to Stand Test (FTSST) in adults: a systematic review and meta-analysis. Biology (Basel), 2021. 10(6).
[4] S. L. Colyer, M. Evans, D.P. Cosker,A.I.T. Salo, A review of the evolution of vision-based motion analysis and the integration of advanced computer vision methods towards developing a markerless system. Sports Medicine-Open, 2018. 4(1): p. 24.
[5] Y. I. Abdel-Aziz, H.M. Karara,M. Hauck, Direct linear transformation from comparator coordinates into object space coordinates in close-range photogrammetry. Photogrammetric Engineering & Remote Sensing, 2015. 81(2): p. 103-107.
[6] B. Scott, M. Seyres, F. Philp, E.K. Chadwick,D. Blana., Healthcare applications of single camera markerless motion capture: a scoping review. PeerJ, 2022. 10: p. e13517.
[7] G. Faity, D. Mottet,J. Froger, Validity and reliability of Kinect v2 for quantifying upper body kinematics during seated reaching. Sensors (Basel), 2022. 22(7).
[8] L. Wade, L. Needham, P. McGuigan,J. Bilzon, Applications and limitations of current markerless motion capture methods for clinical gait biomechanics. PeerJ, 2022. 10: p. e12995.
[9] J. T. Andersen, A.M. McCarthy, J.A. Wills, J.T. Fuller, G.K. Lenton,T.L.A. Doyle, A markerless motion capture system can reliably determine peak trunk flexion while squatting with and without a weighted vest. Journal of Biomechanics, 2023. 152: p. 111587.
[10] B. K. Lahkar, A. Muller, R. Dumas, L. Reveret,T. Robert, Accuracy of a markerless motion capture system in estimating upper extremity kinematics during boxing. Frontiers in Sports and Active Living, 2022. 4: p. 939980.
[11] C. D. Metcalf, R. Robinson, A.J. Malpass, T.P. Bogle, T.A. Dell, C. Harris, et al., Markerless motion capture and measurement of hand kinematics: validation and application to home-based upper limb rehabilitation. IEEE Transactions on Biomedical Engineering, 2013. 60(8): p. 2184-92.
[12] P. Natarajan, R.D. Fonseka, L.W. Sy, M.M. Maharaj,R.J. Mobbs, Analyzing gait patterns in degenerative lumbar spine disease using inertial wearable sensors: an observational study. World Neurosurgery, 2022. 163: p. e501-e515.
[13] J. K. Eliyas,D. Karahalios, Surgery for degenerative lumbar spine disease. Disease-a-Month, 2011. 57(10): p. 592-606.
[14] V. M. Ravindra, S.S. Senglaub, A. Rattani, M.C. Dewan, R. Härtl, E. Bisson, et al., Degenerative lumbar spine disease: estimating global incidence and worldwide volume. Global Spine Journal, 2018. 8(8): p. 784-794.
[15] W. J. Wong, D.M. Lai, S.F. Wang, J.L. Wang,W.L. Hsu, Changes in balance control in individuals with lumbar degenerative spine disease after lumbar surgery: a longitudinal study. The Spine Journal, 2019. 19(7): p. 1210-1220.
[16] Lin, Y.C., C.C. Niu, M. Nikkhoo, M.L. Lu, W.C. Chen, C.J. Fu, et al., Postural stability and trunk muscle responses to static and perturbed balance tasks in individuals with and without symptomatic degenerative lumbar disease. Gait & Posture, 2018. 64: p. 159-164.
[17] V. Leinonen, M. Kankaanpää, M. Luukkonen, M. Kansanen, O. Hänninen, O. Airaksinen, et al., Lumbar paraspinal muscle function, perception of lumbar position, and postural control in disc herniation-related back pain. Spine, 2003. 28(8): p. 842-8.
[18] V. Leinonen, M. Kankaanpää, M. Luukkonen, O. Hänninen, O. Airaksinen,S. Taimela, Disc herniation-related back pain impairs feed-forward control of paraspinal muscles. Spine, 2001. 26(16): p. E367-72.
[19] P. Sakulsriprasert, R. Vachalathiti,P. Kingcha, Responsiveness of pain, functional capacity tests, and disability levels in individuals with chronic nonspecific low back pain. Hong Kong Physiotherapy Journal, 2020. 40(1): p. 11-17.
[20] D. C. C. de Abreu, J.M. Porto, P.S. Tofani, R.M.B. Braghin,R.C.F. Junior, Prediction of reduced gait speed using 5-time sit-to-stand test in healthy older adults. Journal of the American Medical Directors Association, 2022. 23(5): p. 889-892.
[21] L. Khuna, T. Thaweewannakij, P. Wattanapan, P. Amatachaya,S. Amatachaya, Five times sit-to-stand test for ambulatory individuals with spinal cord injury: a psychometric study on the effects of arm placements. Spinal Cord, 2020. 58(3): p. 356-364.
[22] S. C. Im, S.W. Seo, N.Y. Kang, H. Jo,K. Kim, The effect of lumbar belts with different extensibilities on kinematic, kinetic, and muscle activity during sit-to-stand motions in patients with nonspecific low back pain. Journal of Personalized Medicine, 2022. 12(10).
[23] Y. Mong, T.W. Teo,S.S. Ng, 5-Repetition sit-to-stand test in subjects with chronic stroke: reliability and validity. Archives of Physical Medicine and Rehabilitation, 2010. 91(3): p. 407-13.
[24] L. Muñoz-Bermejo, J.C. Adsuar, M. Mendoza-Muñoz, S. Barrios-Fernández, M.A. Garcia-Gordillo, J. Pérez-Gómez, et al., Test-retest reliability of five times sit to stand test (FTSST) in adults: a systematic review and meta-analysis. Biology (Basel), 2021. 10(6).
[25] J. G. Richards, The measurement of human motion: a comparison of commercially available systems. Human Movement Science, 1999. 18(5): p. 589-602.
[26] S.L. Colyer, M. Evans, D.P. Cosker,A.I.T. Salo, A review of the evolution of vision-based motion analysis and the integration of advanced computer vision methods towards developing a markerless system. Sports Medicine-Open, 2018. 4(1): p. 1-15.
[27] N. Ito, H.B. Sigurðsson, K.D. Seymore, E.K. Arhos, T.S. Buchanan, L. Snyder-Mackler, et al., Markerless motion capture: what clinician-scientists need to know right now. Journal of Science and Medicine in Sport, 2022. 1.
[28] C. Armitano-Lago, D. Willoughby,A.W. Kiefer, A SWOT analysis of portable and low-cost markerless motion capture systems to assess lower-limb musculoskeletal kinematics in sport. Frontiers in Sports and Active Living, 2021. 3: p. 809898.
[29] R. A. Clark, Y.H. Pua, K. Fortin, C. Ritchie, K.E. Webster, L. Denehy, et al., Validity of the Microsoft Kinect for assessment of postural control. Gait & Posture, 2012. 36(3): p. 372-377.
[30] T. B. G. Lafayette, V. H. L. Kunst, P. V. S. Melo, P. O. Guedes, J. Teixeira, C. R. Vasconcelos, et al., Validation of angle estimation based on body tracking data from RGB-D and RGB cameras for biomechanical assessment. Sensors (Basel), 2022. 23(1).
[31] H. S. Fang, J. Li, H. Tang, C. Xu, H. Zhu, Y. Xiu, et al., AlphaPose: whole-body regional multi-person pose estimation and tracking in real-time. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. 45(6): p. 7157-7173.
[32] Z. Cao, G. Hidalgo, T. Simon, S.E. Wei,Y. Sheikh, OpenPose: realtime multi-person 2D pose estimation using part affinity fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021. 43(01): p. 172-186.
[33] H. S. Fang, S. Xie, Y.W. Tai,C. Lu. RMPE: regional multi-person pose estimation. in Proceedings of the IEEE International Conference on Computer Vision. 2017.
[34] V. Bazarevsky, I. Grishchenko, K. Raveendran, T. Zhu, F. Zhang,M. Grundmann. BlazePose: on-device real-time body pose tracking. 2020.
[35] A. R. Anwary, M.A. Rahman, A.J.M. Muzahid, A.W.U. Ashraf, M. Patwary,A. Hussain, Deep learning enabled fall detection exploiting gait analysis. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2022. 2022: p. 4683-4686.
[36] N. L. Mya, S. Deepaisarn, W. Chonnaparamutt, S. Laitrakun,M. Nakayama. Exercise pose recognition and counting system using robust topological landmarks. in 18th International Joint Symposium on Artificial Intelligence and Natural Language Processing. 2023. IEEE.
[37] U. Dedhia, P. Bhoir, P. Ranka,P. Kanani. Pose estimation and virtual gym assistant using MediaPipe and machine learning. in International Conference on Network, Multimedia and Information Technology 2023. IEEE.
[38] V. S. P. Bhamidipati, I. Saxena, D. Saisanthiya,M. Retnadhas. Robust intelligent posture estimation for an AI gym trainer using Mediapipe and OpenCV. in International Conference on Networking and Communications. 2023. IEEE.
[39] R. I. Hartley,P. Sturm, Triangulation. Computer vision and image understanding, 1997. 68(2): p. 146-157.
[40] J. C. Gower, Generalized procrustes analysis. Psychometrika, 1975. 40(1): p. 33-51.
[41] Y. C. Pai,M.W. Rogers, Control of body mass transfer as a function of speed of ascent in sit-to-stand. Medicine & Science in Sports & Exercise, 1990. 22(3): p. 378-84.
[42] K. M. Kerr, J.A. White, D.A. Barr,R.A. Mollan, Analysis of the sit-stand-sit movement cycle in normal subjects. Clinical biomechanics (Bristol, Avon), 1997. 12(4): p. 236-245.
[43] Stevermer, C.A.,J.C. Gillette, Kinematic and Kinetic Indicators of Sit-to-Stand. Journal of Applied Biomechanics, 2016. 32(1): p. 7-15.
[44] Y. Y. Lee, M.H. Li, J.J. Luh,C.H. Tai, Reliability of using foot-worn devices to measure gait parameters in people with Parkinson's disease. NeuroRehabilitation, 2021. 49(1): p. 57-64.
[45] J. R. Landis,G.G. Koch, The measurement of observer agreement for categorical data. Biometrics, 1977. 33(1): p. 159-74.
[46] K. Otte, B. Kayser, S. Mansow-Model, J. Verrel, F. Paul, A.U. Brandt, et al., Accuracy and reliability of the Kinect version 2 for clinical measurement of motor function. PLOS ONE, 2016. 11(11): p. e0166532.
[47] R. A. Clark, Y.H. Pua, C.C. Oliveira, K.J. Bower, S. Thilarajah, R. McGaw, et al., Reliability and concurrent validity of the Microsoft Xbox One Kinect for assessment of standing balance and postural control. Gait & Posture, 2015. 42(2): p. 210-3.
[48] J. H. Chen, P.J. Chen, P. Kantha, Y.C. Tsai, D.M. Lai,W.L. Hsu, Examining the influence of body fat distribution on standing balance and functional performance in overweight female patients with degenerative lumbar disease. Frontiers in Bioengineering and Biotechnology, 2024. 12.
[49] Y. C. Tsai, W.L. Hsu, P. Kantha, P.J. Chen,D.M. Lai, Virtual reality skateboarding training for balance and functional performance in degenerative lumbar spine disease. Journal of NeuroEngineering and Rehabilitation, 2024. 21(1): p. 74.
[50] J. Thomas, J. B. Hall, R. Bliss,T. M. Guess, Comparison of Azure Kinect and optical retroreflective motion capture for kinematic and spatiotemporal evaluation of the sit-to-stand test. Gait & Posture, 2022. 94: p. 153-159.
[51] H. Tang, J. Pan, B. Munkasy, K. Duffy,L. Li, Comparison of lower extremity joint moment and power estimated by markerless and marker-based systems during treadmill running. Bioengineering, 2022. 9(10).
[52] K. Song, T.J. Hullfish, S.R. Scattone, K.G. Silbernagel,J.R. Baxter, Markerless motion cpture estimates of lower extremity kinematics and kinetics are comparable to marker-based across 8 movements. Journal of Biomechanics, 2023. 157: p. 111751.
[53] R. M. Kanko, E.K. Laende, E.M. Davis, W.S. Selbie,K.J. Deluzio, Concurrent assessment of gait kinematics using marker-based and markerless motion capture. Journal of Biomechanics, 2021. 127: p. 110665.
[54] K. Claeys, W. Dankaerts, L. Janssens,S. Brumagne, Altered preparatory pelvic control during the sit-to-stance-to-sit movement in people with non-specific low back pain. Journal of Electromyography and Kinesiology, 2012. 22(6): p. 821-828.
[55] J. Wang, A.C. Severin, S.F. Siddicky, B.C. Lowry,E.M. Mannen, Effect of movement speed on lower and upper body biomechanics during sit-to-stand-to-sit transfers: Self-selected speed vs. fast imposed speed. Human Movement Science, 2021. 77: p. 102797.
[56] A. Schmitz, M. Ye, R. Shapiro, R. Yang,B. Noehren, Accuracy and repeatability of joint angles measured using a single-camera markerless motion capture system. Journal of Biomechanics, 2014. 47(2): p. 587-91.
-
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/95098-
dc.description.abstract背景: 退化性腰椎疾病(Degenerative lumbar disease, DLD)是一種常見的老年疾病,會導致腰椎退化以及下肢功能障礙,包含下肢疼痛、無力、感覺改變;下背疼痛、軀幹無力、神經性跛行、與平衡控制能力缺損。研究發現,這些患者在進行臨床五次坐站測試時其下肢運動學存在明顯的異常和改變。過去這些運動學變化主要是由標記式動作捕捉系統偵測,然而此系統的複雜設置卻不便於運用在臨床。隨著科技發展,無標記式動作捕捉系統由於其便攜性強、使用簡易、能檢測人體關節位置等優點,若臨床上的功能性測試輔以無標記動作捕捉系統對患者做評估,可得到的臨床資訊除了時間之參數,更可擴及空間之參數,實現直接反映患者三維動作特徵之優勢。
研究目的:驗證無標記式動作捕捉系統在檢測年輕健康人上,並應用至退化性腰椎疾病患者的下肢運動學表現,以提供新興評估工具其遠距醫療應用支持性證據。
研究假說:驗證無標記式動作捕捉系統於年輕健康人中預計有良好的信度與效度,並可應用於退化性腰椎狹窄的病患,進一步評估運動學相關資訊。
研究設計:橫斷型研究
研究方法:本研究共招募11位(年紀:27.28 ± 6.92歲)無其他骨骼肌肉方面疾病之年輕健康人,以及10位(年紀:70.00 ± 8.08歲)經國立臺灣大學附設醫院神經外科醫師診斷為退化性腰椎疾病患者,量測內容包含基本資料與下肢段長度量測,並同時使用標記式動作捕捉系統與無標記式動作捕捉系統,偵測受測者動作,以量化五次坐到站測試之結果。統計分析使用組內相關係數,描述標記式動作捕捉系統與無標記式動作捕捉系統於空間參數的可重複性;標記式動作捕捉系統與無標記式動作捕捉系統的絕對一致性使用皮爾森相關係數作為描述,顯著程度設定於0.01;兩組(年輕健康人與退化性腰椎疾病患者)運動學資料之比較使用獨立樣本t檢定分析,顯著程度設定於0.05。
結果:標記式動作捕捉系統與無標記式動作捕捉系統,在下肢髖關節、膝關節與踝關節的各軸向於健康人具有高的再測信度表現;且不論是在健康人或是退化性腰椎疾病患者其應用上皆具有高到非常高的共同效度表現 (p < 0.01)。在動作學資料應用上顯示退化性腰椎疾病患者比健康人需要花更長時間完成五次坐到站動作 (p < 0.05);在動作期間關節移動範圍顯示退化性腰椎疾病患者比健康人有較小的關節活動 (p < 0.05);此外,退化性腰椎疾病患者比健康人有更慢的關節移動角速度 (p < 0.05)。
結論:使用無標記動作評估系統應用在退化性腰椎疾病患者具有良好的信效度表現,且評估下肢動態學特徵時,可提供量化之運動學參數,並與健康人做出區別。可為臨床提供治療介入前後的運動學客觀參數,將助於近一步實踐遠距醫療評估的實施。
zh_TW
dc.description.abstractBackground: Degenerative lumbar disease (DLD) is very common in older adults and leads to lumbar degeneration and lower extremity dysfunction. Previous studies have shown that these patients exhibit significant abnormalities and changes in lower extremity kinematics when performing the five times sit-to-stand test clinically. Previously, marker-based motion capture systems were used to detect pathological changes, which were not easy to use in clinics because of the complex settings. With advances in technology, markerless motion capture systems have become popular. They are easy to carry around, simple to use, and can track how our joints move without needing markers. Using markerless motion capture systems into functional performance tests for patient evaluation may provide temporal parameters and extend to spatiotemporal parameters, directly quantifying 3D kinematic data of the patient population.
Purpose: Validation of markerless motion capture systems for assessing lower limb kinematics in young, healthy individuals and application to patients with DLD. This study aimed to establish the reliability and validity of using a markerless motion capture system and to examine the 3D kinematic changes during the five times sit-to-stand test in DLD patients.
Design: Cross-sectional study
Methods: This study recruited 11 young, healthy individuals (27.28 ± 6.92 years old) without other musculoskeletal disorders and 10 patients (70.00 ± 8.08 years old) diagnosed with degenerative lumbar disease by neurosurgeons at National Taiwan University Hospital. Measurements included basic demographic information and lower limb length measurements. Both groups were subjected to five times sit-to-stand tests using marker-based and markerless motion capture systems. Statistical analysis involved assessing the repeatability of spatial parameters between marker-based and markerless motion capture systems using the Intraclass Correlation Coefficient (ICC (3,k)). The correlation between the two systems was also evaluated using Pearson's correlation coefficient, with a significant level set at 0.01. The comparison of kinematic data between the two groups (young healthy individuals and patients with DLD) was analyzed using independent sample t-tests, with the significance level set at 0.05
Results: Both marker-based and markerless motion capture systems showed high test-retest reliability for the hip, knee, and ankle in healthy individuals. Additionally, both systems demonstrated high concurrent validity in applications for healthy individuals and patients with DLD (p < 0.01). Kinematic data analysis revealed that patients with DLD spent more time completing five sit-to-stand than healthy individuals (p < 0.05). During the movement, patients with DLD showed a smaller joint excursion to healthy individuals (p < 0.05). Additionally, patients with DLD exhibited slower joint angular velocities than healthy individuals (p < 0.05).
Conclusions: It is anticipated that when using markerless motion capture systems to evaluate kinematic changes in lower extremities, the spatiotemporal parameters would demonstrate good performance. Conducting kinematic assessments in DLD patients using markerless motion capture systems provides objective kinematic parameters for clinical interventions and facilitates the practical implementation of telemedicine evaluations.
en
dc.description.provenanceSubmitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-28T16:15:21Z
No. of bitstreams: 0
en
dc.description.provenanceMade available in DSpace on 2024-08-28T16:15:21Z (GMT). No. of bitstreams: 0en
dc.description.tableofcontents口試委員審定書 i
誌謝 ii
中文摘要 iv
ABSTRACT vi
CONTENTS ix
CHAPTER 1. INTRODUCTION 1
CHAPTER 2. LITERATURE REVIEW 2
2.1 Degenerative Lumbar Disease 2
2.2 The Relationship between DLD and Functional Performance Test 3
2.3 Motion Capture System 6
2.3.1 The Definition and Historical Progression of Motion Capture System 6
2.3.2 Markerless Motion Capture System 7
2.4 Research Questions 10
2.5 Hypotheses 11
CHAPTER 3. METHODOLOGY 12
3.1 Study Design 12
3.2 Study Procedure 12
3.3 Participants Recruitment 12
3.4 Data Collection and Analysis 13
3.4.1 Participant Demographics and Anthropometry 13
3.4.2 Experimental Setup 13
3.4.3 Joint Kinematic Data 18
3.5 Statistical Analysis 19
CHAPTER 4. RESULTS 21
4.1 Demographic and Anthropometry Data 21
4.2 Test-Retest Reliability 22
4.3 Concurrent Validity 23
4.4 Joint Kinematic Data 24
CHAPTER 5. DISCUSSION 26
5.1 Test-Retest Reliability 27
5.2 Concurrent Validity 28
5.3 Clinical Application 30
5.4 Study Limitations 32
CHAPTER 6. CONCLUSION 34
CHAPTER 7. REFERENCES 35
APPENDIX 1. Clinical Trial/ Research Approval 57
INDEX OF FIGURE
Figure 1. Flowchart of the study 42
Figure 2. Pipeline of 3D human pose reconstruction 43
Figure 3. Definition of 33 landmarks in MediaPipe 44
Figure 4-1. Movement time of five times sit-to-stand test between healthy group and DLD group 45
Figure 4-2. Joint excursion of sit-to-stand movement between healthy group and DLD group 46
Figure 4-3. Joint excursion of stand-to-sit movement between healthy group and DLD group 47
Figure 4-4. Angular velocity of sit-to-stand movement between healthy group and DLD group 48
Figure 4-5. Angular velocity of stand-to-sit movement between healthy group and DLD group 49
INDEX OF TABLE
Table 1. The differences among different human pose estimation models 50
Table 2. Demographic and anthropometry data of the participants 51
Table 3. Test-retest reliability of MediaPipe for joint positions on the A-P/M-L/Vertical axis 52
Table 4-1. Concurrent validity of MediaPipe for joint positions of the healthy group on the A-P/M-L/Vertical axis 53
Table 4-2. Concurrent validity of MediaPipe for joint positions of the DLD group on the A-P/M-L/Vertical axis 54
Table 5. The MPJPE for the distance between the two cameras 55
Table 6. The MAE for the distance between the two cameras on three axes 56
-
dc.language.isoen-
dc.subject退化性腰椎疾病患者zh_TW
dc.subject無標記運動捕捉系統zh_TW
dc.subject坐到站測試zh_TW
dc.subjectmarkerless motion capture systemen
dc.subjectDegenerative lumbar diseaseen
dc.subjectsit-to-stand testen
dc.title利用無標記運動捕捉系統評估退化性腰椎疾病患者下肢運動學特徵的信度與效度研究: 以坐到站測試為例zh_TW
dc.titleReliability and Validity of Markerless Motion Capture System for Qualifying Lower Extremity Kinematics of the Sit-to-Stand Test in Patients with Degenerative Lumbar Diseaseen
dc.typeThesis-
dc.date.schoolyear112-2-
dc.description.degree碩士-
dc.contributor.oralexamcommittee吳沛遠;賴達明;林玨赫zh_TW
dc.contributor.oralexamcommitteePei-Yuan Wu ;Dar-Ming Lai ;Chueh-Ho Linen
dc.subject.keyword退化性腰椎疾病患者,坐到站測試,無標記運動捕捉系統,zh_TW
dc.subject.keywordDegenerative lumbar disease,sit-to-stand test,markerless motion capture system,en
dc.relation.page57-
dc.identifier.doi10.6342/NTU202401918-
dc.rights.note同意授權(限校園內公開)-
dc.date.accepted2024-07-29-
dc.contributor.author-college醫學院-
dc.contributor.author-dept物理治療學研究所-
dc.date.embargo-lift2029-07-26-
顯示於系所單位:物理治療學系所

文件中的檔案:
檔案 大小格式 
ntu-112-2.pdf
  未授權公開取用
1.9 MBAdobe PDF檢視/開啟
顯示文件簡單紀錄


系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。

社群連結
聯絡資訊
10617臺北市大安區羅斯福路四段1號
No.1 Sec.4, Roosevelt Rd., Taipei, Taiwan, R.O.C. 106
Tel: (02)33662353
Email: ntuetds@ntu.edu.tw
意見箱
相關連結
館藏目錄
國內圖書館整合查詢 MetaCat
臺大學術典藏 NTU Scholars
臺大圖書館數位典藏館
本站聲明
© NTU Library All Rights Reserved