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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 詹魁元 | zh_TW |
dc.contributor.advisor | Kuei-Yuan Chan | en |
dc.contributor.author | 林易玄 | zh_TW |
dc.contributor.author | Yi-Hsuan Lin | en |
dc.date.accessioned | 2023-08-09T16:35:07Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-08-09 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-07-24 | - |
dc.identifier.citation | [1] I. Roupa, M. R. da Silva, F. Marques, S. B. Gonçalves, P. Flores, and M. T. da Silva, “On the modeling of biomechanical systems for human movement analysis: a narrative review,” Archives of Computational Methods in Engineering, vol. 29, no. 7, pp. 4915–4958, 2022.
[2] S. D. Uhlrich, T. K. Uchida, M. R. Lee, and S. L. Delp, “Ten steps to becoming a musculoskeletal simulation expert: A half-century of progress and outlook for the future,” Journal of Biomechanics, p. 111623, 2023. [3] H. Barnamehei, F. T. Ghomsheh, A. S. Cherati, and M. Pouladian, “Muscle and joint force dependence of scaling and skill level of athletes in high-speed overhead task: musculoskeletal simulation study,” Informatics in Medicine Unlocked, vol. 20, p. 100415, 2020. [4] G. S. Bullock, J. Mylott, T. Hughes, K. F. Nicholson, R. D. Riley, and G. S. Collins, “Just how confident can we be in predicting sports injuries? a systematic review of the methodological conduct and performance of existing musculoskeletal injury prediction models in sport,” Sports Medicine, vol. 52, no. 10, pp. 2469–2482, 2022. [5] S. L. Delp, J. P. Loan, M. G. Hoy, F. E. Zajac, E. L. Topp, and J. M. Rosen, “An interactive graphics-based model of the lower extremity to study orthopaedic surgical procedures,” IEEE Transactions on Biomedical Engineering, vol. 37, no. 8, pp. 757–767, 1990. [6] A. Rajagopal, Ł. Kidziński, A. S. McGlaughlin, J. L. Hicks, S. L. Delp, and M. H. Schwartz, “Pre-operative gastrocnemius lengths in gait predict outcomes following gastrocnemius lengthening surgery in children with cerebral palsy,” PLoS One, vol. 15, no. 6, p. e0233706, 2020. [7] C. Winby, D. Lloyd, and T. Kirk, “Evaluation of different analytical methods for subject-specific scaling of musculotendon parameters,” Journal of Biomechanics, vol. 41, no. 8, pp. 1682–1688, 2008. [8] R. Akhundov, D. J. Saxby, L. E. Diamond, S. Edwards, P. Clausen, K. Dooley, S. Blyton, and S. J. Snodgrass, “Is subject-specific musculoskeletal modelling worth the extra effort or is generic modelling worth the shortcut?,” PLoS One, vol. 17, no. 1, p. e0262936, 2022. [9] C. Pizzolato, D. G. Lloyd, R. S. Barrett, J. L. Cook, M. H. Zheng, T. F. Besier, and D. J. Saxby, “Bioinspired technologies to connect musculoskeletal mechanobiology to the person for training and rehabilitation,” Frontiers in Computational Neuroscience, vol. 11, p. 96, 2017. [10] D. Stanev, K. Filip, D. Bitzas, S. Zouras, G. Giarmatzis, D. Tsaopoulos, and K. Moustakas, “Real-time musculoskeletal kinematics and dynamics analysis using marker-and imu-based solutions in rehabilitation,” Sensors, vol. 21, no. 5, p. 1804, 2021. [11] L. Guo, J. Wang, Q. Wu, X. Li, B. Zhang, L. Zhou, and D. Xiong, “Clinical study of a wearable remote rehabilitation training system for patients with stroke: Randomized controlled pilot trial,” JMIR mHealth and uHealth, vol. 11, p. e40416, 2023. [12] P. Falkowski, T. Osiak, J. Wilk, N. Prokopiuk, B. Leczkowski, Z. Pilat, and C. Rzymkowski, “Study on the applicability of digital twins for home remote motor rehabilitation,” Sensors, vol. 23, no. 2, p. 911, 2023. [13] S. B. da Luz, L. Modenese, N. Sancisi, P. M. Mills, B. Kennedy, B. R. Beck, and D. G. Lloyd, “Feasibility of using mris to create subject-specific parallelmechanism joint models,” Journal of Biomechanics, vol. 53, pp. 45–55, 2017. [14] D. L. Dejtiar, C. M. Dzialo, P. H. Pedersen, K. K. Jensen, M. K. Fleron, and M. S. Andersen, “Development and evaluation of a subject-specific lower limb model with an eleven-degrees-of-freedom natural knee model using magnetic resonance and biplanar x-ray imaging during a quasi-static lunge,” Journal of Biomechanical Engineering, vol. 142, no. 6, 2020. [15] M. Sartori, J. Rubenson, D. G. Lloyd, D. Farina, and F. A. Panizzolo, “Subjectspecificity via 3d ultrasound and personalized musculoskeletal modeling,” in Converging Clinical and Engineering Research on Neurorehabilitation II: Proceedings of the 3rd International Conference on NeuroRehabilitation (ICNR2016), October 18-21, 2016, Segovia, Spain, pp. 639–642, Springer, 2017. [16] E. Passmore, A. Lai, M. Sangeux, A. G. Schache, and M. G. Pandy, “Application of ultrasound imaging to subject-specific modelling of the human musculoskeletal system,” Meccanica, vol. 52, pp. 665–676, 2017. [17] C. A. Myers, P. J. Laz, K. B. Shelburne, and B. S. Davidson, “A probabilistic approach to quantify the impact of uncertainty propagation in musculoskeletal simulations,” Annals of Biomedical Engineering, vol. 43, pp. 1098–1111, 2015. [18] P. Bujalski, J. Martins, and L. Stirling, “A monte carlo analysis of muscle force estimation sensitivity to muscle-tendon properties using a hill-based muscle model,” Journal of Biomechanics, vol. 79, pp. 67–77, 2018. [19] J. Taborri, J. Keogh, A. Kos, A. Santuz, A. Umek, C. Urbanczyk, E. van der Kruk, and S. Rossi, “Sport biomechanics applications using inertial, force, and emg sensors: A literature overview,” Applied Bionics and Biomechanics, vol. 2020, 2020. [20] M. Menolotto, D.-S. Komaris, S. Tedesco, B. O'Flynn, and M. Walsh, “Motion capture technology in industrial applications: A systematic review,” Sensors, vol. 20, no. 19, p. 5687, 2020. [21] L. M. Dang, K. Min, H. Wang, M. J. Piran, C. H. Lee, and H. Moon, “Sensor-based and vision-based human activity recognition: A comprehensive survey,” Pattern Recognition, vol. 108, p. 107561, 2020. [22] S. L. Colyer, M. Evans, D. P. Cosker, and A. I. 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, vol. 4, no. 1, pp. 1–15, 2018. [23] W. W. Lam, Y. M. Tang, and K. N. Fong, “A systematic review of the applications of markerless motion capture (mmc) technology for clinical measurement in rehabilitation,” Journal of NeuroEngineering and Rehabilitation, vol. 20, no. 1, pp. 1–26, 2023. [24] Vicon Motion Systems Ltd., “Vicon.” WebPage. Online available at: https://www.vicon.com; accessed at 19/5/2023. [25] Google Inc., “Mediapipe.” WebPage. Online available at: https://developers.google.com/mediapipe; accessed at 19/5/2023. [26] A. D. Young, “Comparison of orientation filter algorithms for realtime wireless inertial posture tracking,” in 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks, pp. 59–64, IEEE, 2009. [27] D. Roetenberg, H. Luinge, P. Slycke, et al., “Xsens mvn: Full 6dof human motion tracking using miniature inertial sensors,” Xsens Motion Technologies BV, Tech. Rep, vol. 1, pp. 1–7, 2009. [28] M. Paulich, M. Schepers, N. Rudigkeit, and G. Bellusci, “Xsens mtw awinda: Miniature wireless inertial-magnetic motion tracker for highly accurate 3d kinematic applications,” Xsens: Enschede, The Netherlands, pp. 1–9, 2018. [29] Y. Chen, C. Fu, W. S. W. Leung, and L. Shi, “Drift-free and self-aligned imubased human gait tracking system with augmented precision and robustness,” IEEE Robotics and Automation Letters, vol. 5, no. 3, pp. 4671–4678, 2020. [30] M. Brossard, S. Bonnabel, and A. Barrau, “Denoising imu gyroscopes with deep learning for open-loop attitude estimation,” IEEE Robotics and Automation Letters, vol. 5, no. 3, pp. 4796–4803, 2020. [31] S. O. Madgwick, S. Wilson, R. Turk, J. Burridge, C. Kapatos, and R. Vaidyanathan, “An extended complementary filter for full-body marg orientation estimation,” IEEE/ASME Transactions on Mechatronics, vol. 25, no. 4, pp. 2054–2064, 2020. [32] M. P. van Dijk, M. Kok, M. A. Berger, M. J. Hoozemans, and D. H. Veeger, “Machine learning to improve orientation estimation in sports situations challenging for inertial sensor use,” Frontiers in Sports and Active Living, vol. 3, p. 670263, 2021. [33] L. Sy, M. Raitor, M. Del Rosario, H. Khamis, L. Kark, N. H. Lovell, and S. J. Redmond, “Estimating lower limb kinematics using a reduced wearable sensor count,” IEEE Transactions on Biomedical Engineering, vol. 68, no. 4, pp. 1293–1304, 2020. [34] M. P. van Dijk, R. M. van der Slikke, R. Rupf, M. J. Hoozemans, M. A. Berger, and D. H. Veeger, “Obtaining wheelchair kinematics with one sensor only? the trade-off between number of inertial sensors and accuracy for measuring wheelchair mobility performance in sports,” Journal of Biomechanics, vol. 130, p. 110879, 2022. [35] P. Slade, A. Habib, J. L. Hicks, and S. L. Delp, “An open-source and wearable system for measuring 3d human motion in real-time,” IEEE Transactions on Biomedical Engineering, vol. 69, no. 2, pp. 678–688, 2021. [36] J. C. van den Noort, S. H. Wiertsema, K. M. Hekman, C. P. Schönhuth, J. Dekker, and J. Harlaar, “Reliability and precision of 3d wireless measurement of scapular kinematics,” Medical & Biological Engineering & Computing, vol. 52, pp. 921–931, 2014. [37] J. C. van den Noort, S. H. Wiertsema, K. M. Hekman, C. P. Schönhuth, J. Dekker, and J. Harlaar, “Measurement of scapular dyskinesis using wireless inertial and magnetic sensors: importance of scapula calibration,” Journal of Biomechanics, vol. 48, no. 12, pp. 3460–3468, 2015. [38] M. M. Bhagubai, G. Wolterink, A. Schwarz, J. P. Held, B.-J. F. Van Beijnum, and P. H. Veltink, “Quantifying pathological synergies in the upper extremity of stroke subjects with the use of inertial measurement units: A pilot study,” IEEE Journal of Translational Engineering in Health and Medicine, vol. 9, pp. 1–11, 2020. [39] A. Muro-De-La-Herran, B. Garcia-Zapirain, and A. Mendez-Zorrilla, “Gait analysis methods: An overview of wearable and non-wearable systems, highlighting clinical applications,” Sensors, vol. 14, no. 2, pp. 3362–3394, 2014. [40] S. Gao, J.-L. Chen, Y.-N. Dai, R. Wang, S.-B. Kang, and L.-J. Xu, “Piezoelectricbased insole force sensing for gait analysis in the internet of health things,” IEEE Consumer Electronics Magazine, vol. 10, no. 1, pp. 39–44, 2020. [41] T. Liu, Y. Inoue, and K. Shibata, “A wearable force plate system for the continuous measurement of triaxial ground reaction force in biomechanical applications,” Measurement Science and Technology, vol. 21, no. 8, p. 085804, 2010. [42] Kistler Holding AG, “3d force plate.” WebPage. Online available at: https://www.kistler.com/INT/en/3d-force-plate/C00000090; accessed at 20/5/2023. [43] Advanced Mechanical Technology, Inc., “Force plates products.” WebPage. Online available at: https://www.amti.biz/product/bms464508/; accessed at 20/5/2023. [44] Tec Gihan Co., Ltd, “Wired m3d force platex products.” WebPage. Online available at: https://tecgihan.co.jp/en/products/force-plate/small-forshoes/m3d-force-plate-wired/; accessed at 20/5/2023. [45] H. Kinoshita, S. Obata, D. Nasu, K. Kadota, T. Matsuo, and G. S. Fleisig, “Finger forces in fastball baseball pitching,” Human Movement Science, vol. 54, pp. 172–181, 2017. [46] A. Muller, C. Pontonnier, and G. Dumont, “Motion-based prediction of hands and feet contact efforts during asymmetric handling tasks,” IEEE Transactions on Biomedical Engineering, vol. 67, no. 2, pp. 344–352, 2019. [47] D. R. Seshadri, R. T. Li, J. E. Voos, J. R. Rowbottom, C. M. Alfes, C. A. Zorman, and C. K. Drummond, “Wearable sensors for monitoring the internal and external workload of the athlete,” npj Digital Medicine, vol. 2, no. 1, p. 71, 2019. [48] C. R. Ahn, S. Lee, C. Sun, H. Jebelli, K. Yang, and B. Choi, “Wearable sensing technology applications in construction safety and health,” Journal of Construction Engineering and Management, vol. 145, no. 11, p. 03119007, 2019. [49] Impulse Medical Technologies Inc., “Emg products.” WebPage. Online available at: https://electrodestore.com/collections/emg-electrodes; accessed at 21/5/2023. [50] G. Bassani, A. Filippeschi, and C. A. Avizzano, “A dataset of human motion and muscular activities in manual material handling tasks for biomechanical and ergonomic analyses,” IEEE Sensors Journal, vol. 21, no. 21, pp. 24731–24739, 2021. [51] Delsys Inc., “Trigno avanti sensor products.” WebPage. Online available at: https://delsys.com/trigno-avanti/; accessed at 21/5/2023. [52] M. Febrer-Nafría, A. Nasr, M. Ezati, P. Brown, J. M. Font-Llagunes, and J. McPhee, “Predictive multibody dynamic simulation of human neuromusculoskeletal systems: a review,” Multibody System Dynamics, pp. 1–41, 2022. [53] M. Ezati, B. Ghannadi, and J. McPhee, “A review of simulation methods for human movement dynamics with emphasis on gait,” Multibody System Dynamics, vol. 47, pp. 265–292, 2019. [54] A. V. Hill, “The heat of shortening and the dynamic constants of muscle,” Proceedings of the Royal Society of London. Series B-Biological Sciences, vol. 126, no. 843, pp. 136–195, 1938. [55] B. J. Fregly, J. A. Reinbolt, K. L. Rooney, K. H. Mitchell, and T. L. Chmielewski, “Design of patient-specific gait modifications for knee osteoarthritis rehabilitation,” IEEE Transactions on Biomedical Engineering, vol. 54, no. 9, pp. 1687–1695, 2007. [56] A. Karatsidis, G. Bellusci, H. M. Schepers, M. De Zee, M. S. Andersen, and P. H. Veltink, “Estimation of ground reaction forces and moments during gait using only inertial motion capture,” Sensors, vol. 17, no. 1, p. 75, 2016. [57] M. M. Diraneyya, J. Ryu, E. Abdel-Rahman, and C. T. Haas, “Inertial motion capture-based whole-body inverse dynamics,” Sensors, vol. 21, no. 21, p. 7353, 2021. [58] D. L. Crouch and H. Huang, “Lumped-parameter electromyogram-driven musculoskeletal hand model: A potential platform for real-time prosthesis control,” Journal of Biomechanics, vol. 49, no. 16, pp. 3901–3907, 2016. [59] Y. Zhao, J. Zhang, Z. Li, K. Qian, S. Q. Xie, Y. Lu, and Z.-Q. Zhang, “Computational efficient personalised emg-driven musculoskeletal model of wrist joint,” IEEE Transactions on Instrumentation and Measurement, 2022. [60] D. J. Saxby, B. A. Killen, C. Pizzolato, C. Carty, L. Diamond, L. Modenese, J. Fernandez, G. Davico, M. Barzan, G. Lenton, et al., “Machine learning methods to support personalized neuromusculoskeletal modelling,” Biomechanics and Modeling in Mechanobiology, vol. 19, pp. 1169–1185, 2020. [61] D. G. Thelen, “Adjustment of muscle mechanics model parameters to simulate dynamic contractions in older adults,” Journal of Biomechanical Engineering, vol. 125, no. 1, pp. 70–77, 2003. [62] A. N. Adkins, J. P. Dewald, L. P. Garmirian, C. M. Nelson, and W. M. Murray, “Serial sarcomere number is substantially decreased within the paretic biceps brachii in individuals with chronic hemiparetic stroke,” Proceedings of the National Academy of Sciences, vol. 118, no. 26, p. e2008597118, 2021. [63] A. Bheemreddy, A. Friederich, L. Lombardo, R. J. Triolo, and M. L. Audu, “Estimating total maximum isometric force output of trunk and hip muscles after spinal cord injury,” Medical & Biological Engineering & Computing, vol. 58, pp. 739–751, 2020. [64] H. Kainz, M. Goudriaan, A. Falisse, C. Huenaerts, K. Desloovere, F. De Groote, and I. Jonkers, “The influence of maximum isometric muscle force scaling on estimated muscle forces from musculoskeletal models of children with cerebral palsy,” Gait & posture, vol. 65, pp. 213–220, 2018. [65] K. R. Holzbaur, W. M. Murray, and S. L. Delp, “A model of the upper extremity for simulating musculoskeletal surgery and analyzing neuromuscular control,” Annals of Biomedical Engineering, vol. 33, pp. 829–840, 2005. [66] L. Willemot, A. Thoreson, R. Breighner, A. Hooke, O. Verborgt, and K.-N. An, “Mid-range shoulder instability modeled as a cam-follower mechanism,” Journal of Biomechanics, vol. 48, no. 10, pp. 2227–2231, 2015. [67] S. Wood, D. Pearsall, R. Ross, and J. Reid, “Trunk muscle parameters determined from mri for lean to obese males,” Clinical Biomechanics, vol. 11, no. 3, pp. 139–144, 1996. [68] R. Hainisch, M. Gfoehler, M. Zubayer-Ul-Karim, and M. G. Pandy, “Method for determining musculotendon parameters in subject-specific musculoskeletal models of children developed from mri data,” Multibody System Dynamics, vol. 28, pp. 143–156, 2012. [69] J. P. Charles, F. Suntaxi, and W. J. Anderst, “In vivo human lower limb muscle architecture dataset obtained using diffusion tensor imaging,” PLoS One, vol. 14, no. 10, p. e0223531, 2019. [70] J. P. Charles, C.-H. Moon, and W. J. Anderst, “Determining subject-specific lowerlimb muscle architecture data for musculoskeletal models using diffusion tensor imaging,” Journal of Biomechanical Engineering, vol. 141, no. 6, p. 060905, 2019. [71] J. P. Charles, B. Grant, K. D'Août, and K. T. Bates, “Subject-specific muscle properties from diffusion tensor imaging significantly improve the accuracy of musculoskeletal models,” Journal of Anatomy, vol. 237, no. 5, pp. 941–959, 2020. [72] J. Rubenson, N. J. Pires, H. O. Loi, G. J. Pinniger, and D. G. Shannon, “On the ascent: the soleus operating length is conserved to the ascending limb of the force–length curve across gait mechanics in humans,” Journal of Experimental Biology, vol. 215, no. 20, pp. 3539–3551, 2012. [73] F. A. Panizzolo, A. J. Maiorana, L. H. Naylor, G. A. Lichtwark, L. Dembo, D. G. Lloyd, D. J. Green, and J. Rubenson, “Is the soleus a sentinel muscle for impaired aerobic capacity in heart failure?,” Medicine and Science in Sports and Exercise, vol. 47, no. 3, pp. 498–508, 2015. [74] S. J. Lee and J. Hidler, “Biomechanics of overground vs. treadmill walking in healthy individuals,” Journal of Applied Physiology, vol. 104, no. 3, pp. 747–755, 2008. [75] P. Gerus, G. Rao, and E. Berton, “Subject-specific tendon-aponeurosis definition in hill-type model predicts higher muscle forces in dynamic tasks,” PLoS One, vol. 7, no. 8, pp. 1–13, 2012. [76] P. Gerus, G. Rao, and E. Berton, “Ultrasound-based subject-specific parameters improve fascicle behaviour estimation in hill-type muscle model,” Computer Methods in Biomechanics and Biomedical Engineering, vol. 18, no. 2, pp. 116–123, 2015. [77] T. Delabastita, M. Afschrift, B. Vanwanseele, and F. De Groote, “Ultrasound-based optimal parameter estimation improves assessment of calf muscle–tendon interaction during walking,” Annals of Biomedical Engineering, vol. 48, pp. 722–733, 2020. [78] A. Mantoan, C. Pizzolato, M. Sartori, Z. Sawacha, C. Cobelli, and M. Reggiani, “Motonms: A matlab toolbox to process motion data for neuromusculoskeletal modeling and simulation,” Source Code for Biology and Medicine, vol. 10, pp. 1–14, 2015. [79] C. Pizzolato, D. G. Lloyd, M. Sartori, E. Ceseracciu, T. F. Besier, B. J. Fregly, and M. Reggiani, “Ceinms: A toolbox to investigate the influence of different neural control solutions on the prediction of muscle excitation and joint moments during dynamic motor tasks,” Journal of Biomechanics, vol. 48, no. 14, pp. 3929–3936, 2015. [80] A. Falisse, S. Van Rossom, I. Jonkers, and F. De Groote, “Emg-driven optimal estimation of subject-specific hill model muscle–tendon parameters of the knee joint actuators,” IEEE Transactions on Biomedical Engineering, vol. 64, no. 9, pp. 2253– 2262, 2016. [81] Y. Zhao, Z. Li, Z. Zhang, A. Asker, and S. Q. Xie, “A direct collocation method for optimization of emg-driven wrist muscle musculoskeletal model,” in 2021 IEEE In-ternational Conference on Robotics and Automation (ICRA), pp. 1759–1765, IEEE, 2021. [82] C. L. Dembia, N. A. Bianco, A. Falisse, J. L. Hicks, and S. L. Delp, “Opensim moco: Musculoskeletal optimal control,” PLoS Computational Biology, vol. 16, no. 12, p. e1008493, 2020. [83] A. Van Campen, G. Pipeleers, F. De Groote, I. Jonkers, and J. De Schutter, “A new method for estimating subject-specific muscle–tendon parameters of the knee joint actuators: a simulation study,” International Journal for Numerical Methods in Biomedical Engineering, vol. 30, no. 10, pp. 969–987, 2014. [84] F. M. Colacino, R. Emiliano, and B. R. Mace, “Subject-specific musculoskeletal parameters of wrist flexors and extensors estimated by an emg-driven musculoskeletal model,” Medical Engineering & Physics, vol. 34, no. 5, pp. 531–540, 2012. [85] R. Hinson Jr, K. Saul, D. Kamper, and H. Huang, “Sensitivity analysis guided improvement of an electromyogram-driven lumped parameter musculoskeletal hand model,” Journal of Biomechanics, vol. 141, p. 111200, 2022. [86] E. Martín-Sosa, J. Martínez-Reina, J. Mayo, and J. Ojeda, “Influence of musculotendon geometry variability in muscle forces and hip bone-on-bone forces during walking,” PLoS One, vol. 14, no. 9, p. e0222491, 2019. [87] R. A. Brand, D. R. Pedersen, and J. A. Friederich, “The sensitivity of muscle force predictions to changes in physiologic cross-sectional area,” Journal of Biomechanics, vol. 19, no. 8, pp. 589–596, 1986. [88] C. Y. Scovil and J. L. Ronsky, “Sensitivity of a hill-based muscle model to perturbations in model parameters,” Journal of Biomechanics, vol. 39, no. 11, pp. 2055–2063, 2006. [89] C. Redl, M. Gfoehler, and M. G. Pandy, “Sensitivity of muscle force estimates to variations in muscle–tendon properties,” Human Movement Science, vol. 26, no. 2, pp. 306–319, 2007. [90] F. De Groote, A. Van Campen, I. Jonkers, and J. De Schutter, “Sensitivity of dynamic simulations of gait and dynamometer experiments to hill muscle model parameters of knee flexors and extensors,” Journal of Biomechanics, vol. 43, no. 10, pp. 1876–1883, 2010. [91] M. Xiao and J. Higginson, “Sensitivity of estimated muscle force in forward simulation of normal walking,” Journal of Applied Biomechanics, vol. 26, no. 2, pp. 142– 149, 2010. [92] D. C. Ackland, Y.-C. Lin, and M. G. Pandy, “Sensitivity of model predictions of muscle function to changes in moment arms and muscle–tendon properties: a monte-carlo analysis,” Journal of Biomechanics, vol. 45, no. 8, pp. 1463–1471, 2012. [93] J. L. Hicks, T. K. Uchida, A. Seth, A. Rajagopal, and S. L. Delp, “Is my model good enough? best practices for verification and validation of musculoskeletal models and simulations of movement,” Journal of Biomechanical Engineering, vol. 137, no. 2, 2015. [94] F. E. Zajac, “Muscle and tendon: properties, models, scaling, and application to biomechanics and motor control.,” Critical Reviews in Biomedical Engineering, vol. 17, no. 4, pp. 359–411, 1989. [95] S. L. Delp, F. C. Anderson, A. S. Arnold, P. Loan, A. Habib, C. T. John, E. Guendelman, and D. G. Thelen, “Opensim: open-source software to create and analyze dynamic simulations of movement,” IEEE Transactions on Biomedical Engineering, vol. 54, no. 11, pp. 1940–1950, 2007. [96] M. Millard, T. Uchida, A. Seth, and S. L. Delp, “Flexing computational muscle: modeling and simulation of musculotendon dynamics,” Journal of Biomechanical Engineering, vol. 135, no. 2, 2013. [97] OpenSim, “Forward dynamics documentation in user’s guide.” WebPage. Online available at: https://simtk-confluence.stanford.edu:8443/display/OpenSim/Forward+Dynamics; accessed at 19/5/2023. [98] OpenSim, “Inverse dynamics documentation in user’s guide.” WebPage. Online available at: https://simtk-confluence.stanford.edu:8443/display/OpenSim/Inverse+Dynamics; accessed at 19/5/2023. [99] OpenSim, “Static optimization documentation in user’s guide.” WebPage. Online available at: https://simtk-confluence.stanford.edu:8443/display/OpenSim/Static+Optimization; accessed at 19/5/2023. [100] OpenSim, “Computed muscle control documentation in user’s guide.” Web-Page. Online available at: https://simtk-confluence.stanford.edu:8443/display/OpenSim/Computed+Muscle+Control; accessed at 19/5/2023. [101] D. G. Thelen, F. C. Anderson, and S. L. Delp, “Generating dynamic simulations of movement using computed muscle control,” Journal of Biomechanics, vol. 36, no. 3, pp. 321–328, 2003. [102] D. G. Thelen and F. C. Anderson, “Using computed muscle control to generate forward dynamic simulations of human walking from experimental data,” Journal of Biomechanics, vol. 39, no. 6, pp. 1107–1115, 2006. [103] I. M. Sobol’, “On the distribution of points in a cube and the approximate evaluation of integrals,” Zhurnal Vychislitel’noi Matematiki i Matematicheskoi Fiziki, vol. 7, no. 4, pp. 784–802, 1967. [104] The MathWorks Inc., “Particle swarm documentation in global optimization toolbox.” WebPage. Online available at: https://www.mathworks.com/help/gads/particle-swarm.html; accessed at 19/5/2023. [105] The MathWorks Inc., “Direct search documentation in global optimization toolbox.” WebPage. Online available at: https://www.mathworks.com/help/gads/direct-search.html; accessed at 19/5/2023. [106] The MathWorks Inc., “Searching and polling documentation in global optimization toolbox.” WebPage. Online available at: https://www.mathworks.com/help/gads/searching-and-polling.html; accessed at 19/5/2023. [107] M. Akhavanfar, T. K. Uchida, A. L. Clouthier, and R. B. Graham, “Sharing the load: modeling loads in opensim to simulate two-handed lifting,” Multibody System Dynamics, vol. 54, no. 2, pp. 213–234, 2022. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88332 | - |
dc.description.abstract | 科技日益發展,電腦模擬與分析使得研究學者在生物力學、生理訊號等研究不再局限於臨床實驗的探討,透過軟體模擬亦可得到複雜的分析結果,像是神經訊號、肌肉力量、關節扭矩等資訊,不管是在臨床醫學、醫療復健還是運動科學等領域,皆帶來了前所未有的影響。在人體動作模擬與分析中,除了模擬過程的計算評估方法會影響準確度外,模型的選擇亦影響結果重大,使用通用模型雖能減去模型建立的繁雜步驟,但模擬結果並不能完全代表受試者本人,因此在個人化的模型建立上是必要的,不過同時也充滿了挑戰性。
本研究結合生物力學軟體 OpenSim 與數學計算軟體 MATLAB,以最佳化方法來估計肌肉骨骼模型中的肌肉肌腱參數,整體研究以運動軌跡預測任務作為核心,參數估計前利用敏感度分析結果來選擇欲執行任務,再透過多預測任務的執行,以預測軌跡與目標軌跡間的誤差來尋找欲評估肌肉之參數值,而評估完成得到的最佳模型則需經過模型驗證的考驗。一連串的研究方法流程以數個模擬案例來呈現,藉由普及的上肢肌肉骨骼模型來驗證方法的可行性與有效性,此外亦探討關於參數不可識別性問題,並證實多預測任務可有效地避免其影響。綜合上述,所提出之研究方法能有效評估肌肉骨骼模型中的肌肉肌腱參數,對於未來在個人化模型的建立上,將具有實質上的幫助。 | zh_TW |
dc.description.abstract | The advancement of technology has significantly broadened the possibilities of research in fields such as biomechanics and physiological signals, surpassing the limitations of clinical experiments. Through computer simulation and analysis, researchers can also obtain complex results like neural signals, muscle force, and joint torque. This progress has had unprecedented implications in various domains, including clinical medicine, rehabilitation, and sports science. However, achieving accurate results in human motion simulation and analysis relies not only on the evaluation methods but also on the choice of models. While generic models simplify the model-building process, they fail to fully capture the unique characteristics of individual subjects. Thus, the development of subject-specific models is crucial, albeit challenging.
In this study, the combination of biomechanics software, OpenSim, and mathematical computing software, MATLAB, is used to estimate musculotendon parameters in musculoskeletal models through optimization methods. The primary focus of the research centers around prediction tasks. Prior to parameter estimation, sensitivity analysis is performed to determine the desired tasks to be executed. Subsequently, multiple prediction tasks are executed to quantify the discrepancy between the predicted trajectories and the target trajectories, enabling the determination of parameter values for the evaluated muscles. Finally, the optimal models resulting from the evaluation process are subjected to model validation to ensure their accuracy. The methodology is demonstrated through several simulation cases using a widely used upper extremity musculoskeletal model, confirming the feasibility and effectiveness of the proposed methods. Moreover, the study investigates the issue of parameter non-identifiability and affirms that engaging in multiple prediction tasks is an effective means to circumvent its influence. In conclusion, the proposed methodology effectively estimates musculotendon parameters in musculoskeletal models, providing substantial support for future development of subject-specific models. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-09T16:35:07Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-08-09T16:35:07Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 中文摘要 iv Abstract v 目錄 vii 圖目錄 x 表目錄 xii 第一章 緒論 1 1.1 前言 1 1.2 研究動機與目的 2 1.3 論文架構 4 第二章 文獻回顧 5 2.1 人體動作量測 5 2.1.1 動作捕捉系統 5 2.1.2 力學感測裝置 8 2.1.3 生理訊號感測裝置 9 2.2 人體動作模擬與分析 11 2.2.1 模擬流程與系統介紹 11 2.2.2 模型分類 13 2.2.3 動作模擬與分析 13 2.3 個人化模型 14 2.3.1 肌肉參數量測 16 2.3.2 肌肉參數估測. 17 2.3.3 敏感度分析 19 2.4 小結 20 第三章 研究方法 21 3.1 肌肉模型 23 3.1.1 希爾式肌肉模型 23 3.1.2 肌肉參數 24 3.2 OpenSim 模擬軟體與工具箱應用 25 3.2.1 人體肌肉骨骼模型 26 3.2.2 人體動作模擬與分析 26 3.2.3 OpenSim-MATLAB 介面 32 3.3 運動軌跡預測任務 32 3.3.1 運動軌跡生成 33 3.3.2 預測任務與誤差 34 3.4 敏感度分析 35 3.4.1 Sobol 序列與擾動參數生成 35 3.4.2 敏感度指標 36 3.5 多運動軌跡預測任務與最佳化 37 3.5.1 問題定義 38 3.5.2 最佳化問題 38 3.5.3 最佳化流程與演算法 39 3.6 模型驗證 41 3.7 小結 42 第四章 上肢特定肌肉之參數評估與最佳化 43 4.1 肌肉骨骼模型 43 4.1.1 上肢肌肉骨骼模型 43 4.1.2 具負重之上肢肌肉骨骼模型 45 4.1.3 欲評估之肌肉參數 46 4.2 運動軌跡與控制訊號生成 47 4.2.1 期望動作生成 47 4.2.2 控制訊號生成 48 4.2.3 運動軌跡生成 48 4.3 任務與肌肉參數間之敏感度分析 49 4.3.1 任務種類 49 4.3.2 任務挑選準則 50 4.3.3 敏感度分析 51 4.4 肌肉參數最佳化與驗證 53 4.4.1 最佳化與模型驗證 53 4.4.2 執行任務介紹 56 4.5 小結 56 第五章 參數評估模擬案例與成果探討 58 5.1 參數評估模擬案例 58 5.1.1 單肌肉單參數案例評估 59 5.1.2 單肌肉多參數案例評估 60 5.1.3 多肌肉多參數案例評估 61 5.1.4 單軌跡案例評估 62 5.2 參數評估結果 62 5.3 肌肉參數不可識別性探討 64 5.4 小結 66 第六章 結論與未來工作 67 6.1 研究成果與貢獻 67 6.2 未來工作 68 參考文獻 70 | - |
dc.language.iso | zh_TW | - |
dc.title | 利用多特定運動軌跡估測希爾式肌肉骨骼模型之肌肉肌腱參數的最佳化方法 | zh_TW |
dc.title | Optimization-based Estimation of Musculotendon Parameters in Hill-type Musculoskeletal Models using Multiple Specific Kinematic Trajectories | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 張秉純;徐瑋勵 | zh_TW |
dc.contributor.oralexamcommittee | Biing-Chwen Chang;Wei-Li HSU | en |
dc.subject.keyword | 個人化肌肉骨骼模型,希爾式肌肉模型,肌肉肌腱參數評估,參數不可識別性,最佳化, | zh_TW |
dc.subject.keyword | subject-specific musculoskeletal model,Hill-type muscle model,musculotendon parameter estimation,parameter non-identifiability,optimization, | en |
dc.relation.page | 83 | - |
dc.identifier.doi | 10.6342/NTU202302001 | - |
dc.rights.note | 同意授權(全球公開) | - |
dc.date.accepted | 2023-07-25 | - |
dc.contributor.author-college | 工學院 | - |
dc.contributor.author-dept | 機械工程學系 | - |
顯示於系所單位: | 機械工程學系 |
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