1.
Helten, T, Baak, A, Müller, M, Theobalt, C. Full-body human motion capture from monocular depth images. In: Grzegorzek, M, Theobalt, C, Koch, R, et al, eds. Time-of-Flight and Depth Imaging. Sensors, Algorithms, and Applications. Springer; 2013: 188-206.
Google Scholar |
Crossref2.
Colyer, SL, Evans, M, Cosker, DP, Aki, IT, Salo, AIT. A review of the evolution of vision-based motion analysis and the integration of advanced computer vision methods towards developing a marker-less system. Sports Med Open. 2018;4:24.
Google Scholar |
Crossref |
Medline3.
Pavlakos, G, Zhu, L, Zhou, X, Daniilidis, K. Learning to estimate 3D human pose and shape from a single color image. Paper presented at: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2018: 459-468; Salt Lake City, UT.
Google Scholar |
Crossref4.
Zhou, H, Hu, H. Human motion tracking for rehabilitation–a survey. Biomed Signal Process Control. 2008;3:1-18.
Google Scholar |
Crossref |
ISI5.
Moeslund, TB, Hilton, A, Krüger, V. A survey of advances in vision-based human motion capture and analysis. Comput Vis Image Underst. 2006;104:90-126.
Google Scholar |
Crossref |
ISI6.
Poppe, R. Vision-based human motion analysis: an overview. Comput Vis Image Underst. 2007;108:4-18.
Google Scholar |
Crossref7.
Holte, MB, Tran, C, Trivedi, MM, Moeslund, TB. Human pose estimation and activity recognition from multi-view videos: comparative explorations of recent developments. Select Topics Signal Process. 2012;6:538-552.
Google Scholar |
Crossref8.
Yang, SXM, Christiansen, MS, Larsen, PK, et al. Markerless motion capture systems for tracking of persons in forensic biomechanics: an overview. Comput Methods Biomech Biomed Eng Imaging Vis. 2014;2:46-65.
Google Scholar |
Crossref9.
Mündermann, L, Corazza, S, Andriacchi, TP. The evolution of methods for the capture of human movement leading to markerless motion capture for biomechanical applications. J Neuroeng Rehabil. 2006;3:6.
Google Scholar |
Crossref |
Medline |
ISI10.
Webster, D, Celik, O. Systematic review of Kinect applications in elderly care and stroke rehabilitation. J Neuroeng Rehabil. 2014;11:108.
Google Scholar |
Crossref |
Medline |
ISI11.
Microsoft Azure . Azure Kinect DK. Accessed November 23, 2020.
https://azure.microsoft.com/en-us/services/kinect-dk/ Google Scholar12.
Richmond, T, Peterson, C, Cason, J, et al. American telemedicine association’s principles for delivering telerehabilitation services. Int J Telerehabil. 2017;9:63-68.
Google Scholar |
Crossref |
Medline13.
Peretti, A, Amenta, F, Tayebati, SK, Nittari, G, Mahdi, SS. Telerehabilitation: review of the state-of-the-art and areas of application. JMIR Rehabil Assist Technol. 2017;4:e7.
Google Scholar |
Crossref |
Medline14.
Russell, TG. Telerehabilitation: a coming of age. Aust J Physiother. 2009;55:5-6.
Google Scholar |
Crossref |
Medline15.
Wijekoon, A . Reasoning with multi-modal sensor streams for m-health applications. Paper presented at: Workshop proceedings for the 26th International conference on case-based reasoning (ICCBR 2018); 2018: 234-238; Stockholm, Sweden.
Google Scholar16.
Rybarczyk, Y, Medina, JLP, Leconte, L, Jimenes, K, González, M, Esparza, D. Implementation and assessment of an intelligent motor tele-rehabilitation platform. Electronics. 2019;8:58.
Google Scholar |
Crossref17.
Mani, S, Sharma, S, Omar, B, Ahmad, K, Muniandy, Y, Singh, DKA. Quantitative measurements of forward head posture in a clinical settings: a technical feasibility study. Eur J Physiother. 2017;19:119-123.
Google Scholar |
Crossref18.
Norkin, CC, White, DJ. Measurement of Joint Motion: a Guide to Goniometry. 5th ed. F.A. Davis PT Collection; 2017.
Google Scholar19.
Mohsin, F, McGarry, A, Bowers, R. The reliability of a video analysis system (PnO clinical movement data) and the universal goniometer in the measurement of hip, knee, and ankle sagittal plane motion among healthy subjects. J Prosthet Orthot. 2018;30:145-151.
Google Scholar |
Crossref20.
Reissner, L, Fischer, G, List, R, Taylor, WR, Giovanoli, P, Calcagni, M. Minimal detectable difference of the finger and wrist range of motion: comparison of goniometry and 3D motion analysis. J Orthop Surg Res. 2019;14:173.
Google Scholar |
Crossref |
Medline21.
Zed Camera and SDK Overview . Published October 2018. Accessed November 23, 2020.
https://cdn.stereolabs.com/assets/datasheets/zed-camera-datasheet.pdf Google Scholar22.
Sarbolandi, H, Lefloch, D, Andreas Kolb, A. Kinect range sensing: structured-light versus time-of-flight kinect. Comput Vis Image Underst. 2015;139:1-20.
Google Scholar |
Crossref23.
Bengio, Y, Courville, A, Vincent, P. Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell. 2013;35:1798-1828.
Google Scholar |
Crossref |
Medline |
ISI24.
Yang, W, Zhang, X, Tian, Y, Wang, W, Xue, J, Liao, Q. Deep learning for single image super-resolution: a brief review. IEEE Trans Multimedia. 2019;21:3106-3121.
Google Scholar |
Crossref25.
Yang, Y, Ramanan, D. Articulated pose estimation using flexible mixtures of parts. Paper presented at: Conference on Computer Vision and Pattern Recognition (CVPR); 2011: 1385-1389; Providence, RI.
Google Scholar26.
Wang, F, Li, Y. Beyond physical connections: Tree models in human pose estimation. Paper presented at: 2013 IEEE Conference on Computer Vision and Pattern Recognition; 2013: 596-603; Portland, OR.
Google Scholar |
Crossref27.
Eichner, M, Ferrari, V. We are family: Joint pose estimation of multiple persons. Paper presented at: European Conference on Computer Vision; 2010: 228-242; Heraklion, Crete, Greece.
Google Scholar |
Crossref28.
Dang, Q, Yin, J, Wang, B, Zheng, W. Deep learning based 2D human pose estimation: a survey. Tsinghua Sci Technol. 2019;24:663-676.
Google Scholar |
Crossref29.
Toshev, A, Szegedy, C. DeepPose: Human pose estimation via deep neural networks. Paper presented at: 2014 IEEE Conference on Computer Vision and Pattern Recognition; 2014: 1653-1660; Columbus, OH.
Google Scholar |
Crossref30.
Newell, A, Yang, K, Deng, J. Stacked hourglass networks for human pose estimation. Paper presented at: European Conference on Computer Vision; 2016: 483-499; Amsterdam, The Netherlands.
Google Scholar |
Crossref31.
Cao, Z, Martinez, GH, Simon, T, Wei, SE, Sheikh, YA. OpenPose: realtime multi-person 2D pose estimation using part affinity fields. IEEE transactions on pattern analysis and machine intelligence, 17 July 2019, pp.172-186. New York: IEEE.
Google Scholar |
Crossref32.
Boukhayma, A, Bem, R, Torr, PHS. 3D hand shape and pose from images in the wild. Paper presented at: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2019: 10835-10844; Long Beach, CA.
Google Scholar |
Crossref33.
Pavlakos, G, Zhou, X, Derpanis, KG, Daniilidis, K. Harvesting multiple views for marker-less 3D human pose annotations. Paper presented at: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2017: 1253-1262; Honolulu, HI.
Google Scholar |
Crossref34.
Felzenszwalb, PF, Huttenlocher, DP. Pictorial structures for object recognition. Int J Comput Vis. 2005;61:55-79.
Google Scholar |
Crossref35.
Iskakov, K, Burkov, E, Lempitsky, V, Malkov, Y. Learnable triangulation of human pose. Paper presented at: 2019 IEEE/CVF International Conference on Computer Vision (ICCV); 2019:7717-7726; Seoul, Korea (South).
Google Scholar |
Crossref36.
Xiao, B, Wu, H, Wei, Y. Simple baselines for human pose estimation and tracking. Paper presented at: European Conference on Computer Vision; 2018: 472-487; Munich, Germany.
Google Scholar |
Crossref37.
Hartley, R, Zisserman, A. Multiple View Geometry in Computer Vision. 2nd ed. Cambridge University Press; 2003.
Google Scholar38.
Shere, M, Kim, H, Hilton, A. 3D human pose estimation from multi person stereo 360° scenes. Paper presented at: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops; 2019: 1-8.
Google Scholar39.
Agarwal, S, Mierle, K and Others. Ceres Solver. Updated 2020. Accessed May 28, 2021.
http://ceres-solver.org/ Google Scholar40.
Huang, Y, Bogo, F, Lassner, C, et al. Towards accurate marker-less human shape and pose estimation over time. Paper presented at: 2017 International Conference on 3D Vision (3DV); 2017:421-430; Qingdao, China.
Google Scholar |
Crossref41.
Anguelov, D, Srivassan, P, Koller, D, Thrun, S, Rodgers, J, Davis, J. SCAPE: shape completion and animation of people. ACM Trans Graph. 2005;24:408-416.
Google Scholar |
Crossref |
ISI42.
Chang, AX, Funkhouser, T, Guibas, L, et al. ShapeNet: an information-rich 3D model repository. ArXiv, abs/1512.03012. 2015. Published December 9, 2015. Accessed November 23, 2020.
https://arxiv.org/pdf/1512.03012.pdf Google Scholar43.
Zimmermann, C, Brox, T. Learning to estimate 3D hand pose from single RGB images. Paper presented at: 2017 IEEE International Conference on Computer Vision (ICCV); 2017: 4913-4921; Venice, Italy.
Google Scholar |
Crossref44.
Mueller, F, Benard, F, Oleksandr, S, et al. GANerated hands for real-time 3D hand tracking from monocular RGB. Paper presented at: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2018:49-59; Salt Lake City, UT.
Google Scholar |
Crossref45.
Simon, T, Joo, H, Matthews, I, Sheikh, Y. Hand keypoint detection in single images using multiview bootstrapping. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2017: 4645-4653; Honolulu, HI.
Google Scholar |
Crossref46.
Tome, D, Russell, C, Agapito, L. Lifting from the deep: convolutional 3D pose estimation from a single image. Paper presented at: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2017: 5689-5698; Honolulu, HI.
Google Scholar |
Crossref47.
Bogo, F, Kanazawa, A, Lassner, C, Gehler, P, Romero, J, Black, MJ. Keep it SMPL: automatic estimation of 3D human pose and shape from a single image. Paper presented at: European Conference on Computer Vision; 2016; Amsterdam, The Netherlands.
Google Scholar |
Crossref48.
Kanazawa, A, Black, MJ, Jacobs, DW, Malik, J. End-to-end recovery of human shape and pose. Paper presented at: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2018: 7122-7131; Salt Lake City, UT.
Google Scholar |
Crossref49.
Slembrouck, M, Luong, H, Gerlo, J, et al. Multiview 3D markerless human pose estimation from openpose skeletons. Paper presented at: International Conference on Advanced Concepts for Intelligent Vision Systems ACIVS; 2020: 166-178; Auckland, New Zealand.
Google Scholar |
Crossref50.
Gu, X, Deligianni, F, Lo, B, Chen, W, Yang, GZ. Markerless gait analysis based on a single RGB camera. Paper presented at: 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN); 2018: 42-45; Las Vegas, NV.
Google Scholar |
Crossref51.
Ye, M, Yang, C, Stankovic, V, tankovic, L, Kerr, A. A depth camera motion analysis framework for tele-rehabilitation: motion capture and person-centric kinematics analysis. IEEE J Sel Top Signal Process. 2016;10:877-887.
Google Scholar |
Crossref52.
Nakano, N, Sakura, T, Ueda, K, et al. Evaluation of 3D markerless motion capture accuracy using OpenPose with multiple video cameras. Front Sports Act Living. 2020;2.
Google Scholar |
Crossref |
Medline53.
Schmitz, A, Ye, M, Shapiro, R, Yang, R, Noehren, B. Accuracy and repeatability of joint angles measured using a single camera marker-less motion capture system. J Biomech. 2014;47:587-591.
Google Scholar |
Crossref |
Medline |
ISI54.
Ar, I, Akgul, YS. A computerized recognition system for the home-based physiotherapy exercises using an RGBD camera. IEEE Trans Neural Syst Rehabil Eng. 2014;22:1160-1171.
Google Scholar |
Crossref |
Medline |
ISI55.
Tinetti, ME, Speechley, M, Ginter, SF. Risk factors for falls among elderly persons living in the community. N Engl J Med. 1988;319:1701-1707.
Google Scholar |
Crossref |
Medline |
Comments (0)