{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T16:14:12Z","timestamp":1762272852073,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,31]],"date-time":"2021-12-31T00:00:00Z","timestamp":1640908800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Human motion tracking is widely applied to rehabilitation tasks, and inertial measurement unit (IMU) sensors are a well-known approach for recording motion behavior. IMU sensors can provide accurate information regarding three-dimensional (3D) human motion. However, IMU sensors must be attached to the body, which can be inconvenient or uncomfortable for users. To alleviate this issue, a visual-based tracking system from two-dimensional (2D) RGB images has been studied extensively in recent years and proven to have a suitable performance for human motion tracking. However, the 2D image system has its limitations. Specifically, human motion consists of spatial changes, and the 3D motion features predicted from the 2D images have limitations. In this study, we propose a deep learning (DL) human motion tracking technology using 3D image features with a deep bidirectional long short-term memory (DBLSTM) mechanism model. The experimental results show that, compared with the traditional 2D image system, the proposed system provides improved human motion tracking ability with RMSE in acceleration less than 0.5 (m\/s2) X, Y, and Z directions. These findings suggest that the proposed model is a viable approach for future human motion tracking applications.<\/jats:p>","DOI":"10.3390\/s22010292","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T23:08:26Z","timestamp":1641769706000},"page":"292","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Human Motion Tracking Using 3D Image Features with a Long Short-Term Memory Mechanism Model\u2014An Example of Forward Reaching"],"prefix":"10.3390","volume":"22","author":[{"given":"Kai-Yu","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 112, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2368-448X","authenticated-orcid":false,"given":"Li-Wei","family":"Chou","sequence":"additional","affiliation":[{"name":"Department of Physical Therapy and Assistive Technology, National Yang Ming Chiao Tung University, Taipei 112, Taiwan"}]},{"given":"Hui-Min","family":"Lee","sequence":"additional","affiliation":[{"name":"The Research Center on ICF and Assistive Technology, National Yang Ming Chiao Tung University, Taipei 112, Taiwan"}]},{"given":"Shuenn-Tsong","family":"Young","sequence":"additional","affiliation":[{"name":"Institute of Geriatric Welfare Technology & Science, MacKay Medical College, New Taipei City 252, Taiwan"}]},{"given":"Cheng-Hung","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Taiwan Normal University, Taipei 106, Taiwan"}]},{"given":"Yi-Shu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 112, Taiwan"}]},{"given":"Shih-Tsang","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Ming Chuan University, Taoyuan 333, Taiwan"}]},{"given":"Ying-Hui","family":"Lai","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 112, Taiwan"},{"name":"Medical Device Innovation & Translation Center, National Yang Ming Chiao Tung University, Taipei 112, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,31]]},"reference":[{"key":"ref_1","unstructured":"United Nations Department of Economic and Social Affairs, Population Division (2020). World Population Ageing 2020 Highlights, United Nations."},{"key":"ref_2","unstructured":"World Health Organization (2020, June 21). Stroke, Cerebrovascular Accident. Available online: http:\/\/www.emro.who.int\/health-topics\/stroke-cerebrovascular-accident\/index.html."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.bspc.2007.09.001","article-title":"Human motion tracking for rehabilitation\u2014A survey","volume":"3","author":"Zhou","year":"2008","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_4","unstructured":"World Health Organization (2011). World Report on Disability 2011, Who Press, World Health Organization."},{"key":"ref_5","unstructured":"World Health Organization (2020, June 21). Rehabilitation. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/rehabilitation."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6891","DOI":"10.3390\/s140406891","article-title":"IMU-Based Joint Angle Measurement for Gait Analysis","volume":"14","author":"Seel","year":"2014","journal-title":"Sensors"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Jung, Y., Kang, D., and Kim, J. (2010, January 14\u201318). Upper body motion tracking with inertial sensors. Proceedings of the IEEE International Conference on Robotics and Biomimetics, Tianjin, China.","DOI":"10.1109\/ROBIO.2010.5723595"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1017\/BrImp.2015.21","article-title":"Exploring the role of accelerometers in the measurement of real world upper-limb use after stroke","volume":"17","author":"Hayward","year":"2016","journal-title":"Brain Impair."},{"key":"ref_9","unstructured":"(2020, June 21). Vicon. Available online: https:\/\/www.vicon.com\/."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1109\/TPAMI.2019.2929257","article-title":"OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields","volume":"43","author":"Cao","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tang, R., Yang, X.D., Bateman, S., Jorge, J., and Tang, A. (2015, January 18\u201323). Physio@ Home: Exploring visual guidance and feedback techniques for physiotherapy exercises. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI \u201915), Seoul, Korea.","DOI":"10.1145\/2702123.2702401"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ganapathi, V., Plagemann, C., Koller, D., and Thrun, S. (2010, January 13\u201318). Real time motion capture using a single time-of-flight camera. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5540141"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., and Blake, A. (2011, January 20\u201325). Real-time human pose recognition in parts from single depth images. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995316"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2085","DOI":"10.1016\/j.jbiomech.2016.05.007","article-title":"Analysis of accuracy in optical motion capture\u2013A protocol for laboratory setup evaluation","volume":"49","author":"Eichelberger","year":"2016","journal-title":"J. Biomech."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Merriaux, P., Dupuis, Y., Boutteau, R., Vasseur, P., and Savatier, X. (2017). A Study of Vicon System Positioning Performance. Sensors, 17.","DOI":"10.3390\/s17071591"},{"key":"ref_16","unstructured":"Bouvrie, J. (2020, December 18). Notes on Convolutional Neural Networks. Available online: http:\/\/cogprints.org\/5869\/1\/cnn_tutorial.pdf."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Viswakumar, A., Rajagopalan, V., Ray, T., and Parimi, C. (2019, January 15\u201317). Human gait analysis using OpenPose. Proceedings of the 5th International Conference on Image Information Processing (ICIIP), Shimla, India.","DOI":"10.1109\/ICIIP47207.2019.8985781"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Otsuka, K., Yagi, N., Yamanaka, Y., Hata, Y., and Sakai, Y. (2020, January 9\u201311). Joint position registration between OpenPose and motion analysis for rehabilitation. Proceedings of the IEEE 50th International Symposium on Multiple-Valued Logic (ISMVL), Miyazaki, Japan.","DOI":"10.1109\/ISMVL49045.2020.00-22"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yan, H., Hu, B., Chen, G., and Zhengyuan, E. (2020, January 24\u201326). Real-time continuous human rehabilitation action recognition using OpenPose and FCN. Proceedings of the 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE), Shenzhen, China.","DOI":"10.1109\/AEMCSE50948.2020.00058"},{"key":"ref_20","unstructured":"Li, Y.R., Miaou, S.G., Hung, C.K., and Sese, J.T. (2011, January 26\u201328). A gait analysis system using two cameras with orthogonal view. Proceedings of the International Conference on Multimedia Technology, Hangzhou, China."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"8609","DOI":"10.1007\/s11042-015-2774-3","article-title":"An adaptive approach for lip-reading using image and depth data","volume":"75","author":"Rekik","year":"2016","journal-title":"Multimed. Tools Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1917","DOI":"10.1109\/JSEN.2010.2101060","article-title":"Lock-in time-of-flight (ToF) cameras: A survey","volume":"11","author":"Foix","year":"2011","journal-title":"IEEE Sens. J."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chen, K.Y., Zheng, W.Z., Lin, Y.Y., Tang, S.T., Chou, L.W., and Lai, Y.H. (2020, January 20\u201324). Deep-learning-based human motion tracking for rehabilitation applications using 3D image features. Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9176120"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ma, Y., Liu, D., and Cai, L. (2020). Deep Learning-Based Upper Limb Functional Assessment Using a Single Kinect V2 Sensor. Sensors, 20.","DOI":"10.3390\/s20071903"},{"key":"ref_26","unstructured":"Huang, Z., Xu, W., and Yu, K. (2015). Bidirectional LSTM-CRF models for sequence tagging. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Graves, A., Jaitly, N., and Mohamed, A.R. (2013, January 8\u201312). Hybrid speech recognition with Deep Bidirectional LSTM. Proceedings of the IEEE Workshop on Automatic Speech Recognition and Understanding, Olomouc, Czech Republic.","DOI":"10.1109\/ASRU.2013.6707742"},{"key":"ref_29","unstructured":"Fell, D.W., Lunnen, K.Y., and Rauk, R.P. (2018). Lifespan Neurorehabilitation: A Patient-Centered Approach from Examination to Interventions and Outcomes, F.A. Davis Company."},{"key":"ref_30","unstructured":"(2020, June 21). Xsens. Available online: https:\/\/www.xsens.com\/."},{"key":"ref_31","unstructured":"(2020, June 21). Focus Vision Technology. Available online: http:\/\/focusvision.tech\/."},{"key":"ref_32","unstructured":"Wechsler, H. (1992). Theory of the backpropagation neural network. Neural Networks for Perception, Elsevier."},{"key":"ref_33","unstructured":"Simard, P.Y., Steinkraus, D., and Platt, J.C. (2003, January 3\u20136). Best practices for convolutional neural networks applied to visual document analysis. Proceedings of the 7th International Conference on Document Analysis and Recognition (ICDAR 2003), Edinburgh, UK."},{"key":"ref_34","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_35","unstructured":"Dozat, T. (2016, January 2\u20134). Incorporating Nesterov momentum into Adam. Proceedings of the International Conference on Learning Representations (ICLR), San Juan, Puerto Rico."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1016\/S0021-9290(01)00231-7","article-title":"Accelerometer and rate gyroscope measurement of kinematics: An inexpensive alternative to optical motion analysis systems","volume":"35","author":"Mayagoitia","year":"2002","journal-title":"J. Biomech."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"956","DOI":"10.1080\/02755947.2012.711270","article-title":"Recommendations for catch-curve analysis","volume":"32","author":"Smith","year":"2012","journal-title":"N. Am. J. Fish. Manag."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Rogness, N., Stephenson, P., Stephenson, P., and Moore, D.S. (2002). SPSS Manual: For Introduction to the Practice of Statistics, Macmillan Publishers. [4th ed.].","DOI":"10.12987\/yale\/9780300092394.003.0001"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Su, T., Sun, H., Ma, C., Jiang, L., and Xu, T. (2019, January 14\u201319). HDL: Hierarchical deep learning model based human activity recognition using smartphone sensors. Proceedings of the International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8851889"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/1\/292\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:56:44Z","timestamp":1760169404000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/1\/292"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,31]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22010292"],"URL":"https:\/\/doi.org\/10.3390\/s22010292","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,12,31]]}}}