{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T00:47:49Z","timestamp":1780102069259,"version":"3.54.0"},"reference-count":52,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T00:00:00Z","timestamp":1716508800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Universidad Pedag\u00f3gica y Tecnol\u00f3gica de Colombia","award":["Project number SGI 3474"],"award-info":[{"award-number":["Project number SGI 3474"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Technological advancements have expanded the range of methods for capturing human body motion, including solutions involving inertial sensors (IMUs) and optical alternatives. However, the rising complexity and costs associated with commercial solutions have prompted the exploration of more cost-effective alternatives. This paper presents a markerless optical motion capture system using a RealSense depth camera and intelligent computer vision algorithms. It facilitates precise posture assessment, the real-time calculation of joint angles, and acquisition of subject-specific anthropometric data for gait analysis. The proposed system stands out for its simplicity and affordability in comparison to complex commercial solutions. The gathered data are stored in comma-separated value (CSV) files, simplifying subsequent analysis and data mining. Preliminary tests, conducted in controlled laboratory environments and employing a commercial MEMS-IMU system as a reference, revealed a maximum relative error of 7.6% in anthropometric measurements, with a maximum absolute error of 4.67 cm at average height. Stride length measurements showed a maximum relative error of 11.2%. Static joint angle tests had a maximum average error of 10.2%, while dynamic joint angle tests showed a maximum average error of 9.06%. The proposed optical system offers sufficient accuracy for potential application in areas such as rehabilitation, sports analysis, and entertainment.<\/jats:p>","DOI":"10.3390\/s24113371","type":"journal-article","created":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T06:59:04Z","timestamp":1716533944000},"page":"3371","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Development of a Low-Cost Markerless Optical Motion Capture System for Gait Analysis and Anthropometric Parameter Quantification"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7195-1570","authenticated-orcid":false,"given":"Laura Alejandra","family":"Espitia-Mora","sequence":"first","affiliation":[{"name":"Software Research Group, Universidad Pedag\u00f3gica y Tecnol\u00f3gica de Colombia, Tunja 150002, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2105-1742","authenticated-orcid":false,"given":"Manuel Andr\u00e9s","family":"V\u00e9lez-Guerrero","sequence":"additional","affiliation":[{"name":"Software Research Group, Universidad Pedag\u00f3gica y Tecnol\u00f3gica de Colombia, Tunja 150002, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9894-8737","authenticated-orcid":false,"given":"Mauro","family":"Callejas-Cuervo","sequence":"additional","affiliation":[{"name":"Software Research Group, Universidad Pedag\u00f3gica y Tecnol\u00f3gica de Colombia, Tunja 150002, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/s40798-018-0139-y","article-title":"A Review of the Evolution of Vision-Based Motion Analysis and the Integration of Advanced Computer Vision Methods Towards Developing a Markerless System","volume":"4","author":"Colyer","year":"2018","journal-title":"Sports Med.-Open"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Valencia-Marin, C.K., Pulgarin-Giraldo, J.D., Velasquez-Martinez, L.F., Alvarez-Meza, A.M., and Castellanos-Dominguez, G. (2021). An enhanced joint hilbert embedding-based metric to support mocap data classification with preserved interpretability. Sensors, 21.","DOI":"10.3390\/s21134443"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Mendes, J.J.A., Vieira, M.E.M., Pires, M.B., and Stevan, S.L. (2016). Sensor fusion and smart sensor in sports and biomedical applications. Sensors, 16.","DOI":"10.3390\/s16101569"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"James, D.A., and Petrone, N. (2016). Sensors and Wearable Technologies in Sport: Technologies, Trends and Approaches for Implementation, Springer.","DOI":"10.1007\/978-981-10-0992-1_5"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.gaitpost.2018.11.029","article-title":"Three-dimensional cameras and skeleton pose tracking for physical function assessment: A review of uses, validity, current developments and Kinect alternatives","volume":"68","author":"Clark","year":"2019","journal-title":"Gait Posture"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1007\/s10916-018-0905-x","article-title":"Utilising the Intel RealSense Camera for Measuring Health Outcomes in Clinical Research","volume":"42","author":"Siena","year":"2018","journal-title":"J. Med. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"14065","DOI":"10.1038\/s41598-021-93530-z","article-title":"Algorithm based on one monocular video delivers highly valid and reliable gait parameters","volume":"11","author":"Azhand","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/s00371-019-01741-3","article-title":"Bidirectional long short-term memory networks and sparse hierarchical modeling for scalable educational learning of dance choreographies","volume":"37","author":"Rallis","year":"2021","journal-title":"Vis. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Callejas-Cuervo, M., Espitia-Mora, L.A., and V\u00e9lez-Guerrero, M.A. (2023). Review of Optical and Inertial Technologies for Lower Body Motion Capture. J. Hunan Univ. Nat. Sci., 50.","DOI":"10.55463\/issn.1674-2974.50.6.11"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4100313","DOI":"10.1109\/JTEHM.2018.2859992","article-title":"Kinect-Based In-Home Exercise System for Lymphatic Health and Lymphedema Intervention","volume":"6","author":"Chiang","year":"2018","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"176241","DOI":"10.1109\/ACCESS.2020.3026276","article-title":"Human4D: A human-centric multimodal dataset for motions and immersive media","volume":"8","author":"Chatzitofis","year":"2020","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e12995","DOI":"10.7717\/peerj.12995","article-title":"Applications and limitations of current markerless motion capture methods for clinical gait biomechanics","volume":"10","author":"Wade","year":"2022","journal-title":"PeerJ"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ripic, Z., Signorile, J.F., Best, T.M., Jacobs, K.A., Nienhuis, M., Whitelaw, C., Moenning, C., and Eltoukhy, M. (2023). Validity of artificial intelligence-based markerless motion capture system for clinical gait analysis: Spatiotemporal results in healthy adults and adults with Parkinson\u2019s disease. J. Biomech., 155.","DOI":"10.1016\/j.jbiomech.2023.111645"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Moro, M., Marchesi, G., Hesse, F., Odone, F., and Casadio, M. (2022). Markerless vs. Marker-Based Gait Analysis: A Proof of Concept Study. Sensors, 22.","DOI":"10.3390\/s22052011"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1177\/09544119231163634","article-title":"Development of a new low-cost computer vision system for human gait analysis: A case study","volume":"237","year":"2023","journal-title":"Proc. Inst. Mech. Eng. Part H J. Eng. Med."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Krausz, N.E., Hu, B.H., and Hargrove, L.J. (2019). Subject- and environment-based sensor variability for wearable lower-limb assistive devices. Sensors, 19.","DOI":"10.3390\/s19224887"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1109\/TNSRE.2019.2895221","article-title":"Environmental Features Recognition for Lower Limb Prostheses Toward Predictive Walking","volume":"27","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hu, B.H., Krausz, N.E., and Hargrove, L.J. (2018, January 26\u201329). A Novel Method for Bilateral Gait Segmentation Using a Single Thigh-Mounted Depth Sensor and IMU. Proceedings of the 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), Enschede, The Netherlands.","DOI":"10.1109\/BIOROB.2018.8487806"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Cimolin, V., Vismara, L., Ferraris, C., Amprimo, G., Pettiti, G., Lopez, R., Galli, M., Cremascoli, R., Sinagra, S., and Mauro, A. (2022). Computation of Gait Parameters in Post Stroke and Parkinson\u2019s Disease: A Comparative Study Using RGB-D Sensors and Optoelectronic Systems. Sensors, 22.","DOI":"10.3390\/s22030824"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"31249","DOI":"10.1109\/ACCESS.2018.2816816","article-title":"Using Body-Worn Sensors for Preliminary Rehabilitation Assessment in Stroke Victims With Gait Impairment","volume":"6","author":"Qiu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"112544","DOI":"10.1109\/ACCESS.2019.2934863","article-title":"Depth Maps Restoration for Human Using RealSense","volume":"7","author":"Yin","year":"2019","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, L., Xia, H., and Qiao, Y. (2020). Texture Synthesis Repair of RealSense D435i Depth Images with Object-Oriented RGB Image Segmentation. Sensors, 20.","DOI":"10.3390\/s20236725"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"938","DOI":"10.1177\/0954411919859922","article-title":"Comparison of depth cameras for three-dimensional reconstruction in medicine","volume":"233","author":"Chiu","year":"2019","journal-title":"Proc. Inst. Mech. Eng. Part H J. Eng. Med."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1007\/s00530-021-00754-0","article-title":"Understanding the limits of 2D skeletons for action recognition","volume":"27","author":"Elias","year":"2021","journal-title":"Multimed. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"12486","DOI":"10.1038\/s41598-021-91861-5","article-title":"Evaluation of the Intel RealSense T265 for tracking natural human head motion","volume":"11","author":"Hausamann","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Gutta, V., Baddour, N., Fallavollita, P., and Lemaire, E. (2019, January 27). Multiple depth sensor setup and synchronization for marker-less 3D human foot tracking in a hallway. Proceedings of the 2019 IEEE\/ACM 1st International Workshop on Software Engineering for Healthcare (SEH), Montreal, QC, Canada.","DOI":"10.1109\/SEH.2019.00021"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1817","DOI":"10.1109\/TNSRE.2020.3007532","article-title":"Reliability and Agreement of 3D Anthropometric Measurements in Facial Palsy Patients Using a Low-Cost 4D Imaging System","volume":"28","author":"Harkel","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"112847","DOI":"10.1016\/j.eswa.2019.112847","article-title":"A review on video-based active and assisted living technologies for automated lifelogging","volume":"139","author":"Spinsante","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1038\/s41597-021-00801-5","article-title":"Optical motion capture dataset of selected techniques in beginner and advanced Kyokushin karate athletes","volume":"8","author":"Pawlyta","year":"2021","journal-title":"Sci. Data"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chatzitofis, A., Zarpalas, D., Kollias, S., and Daras, P. (2019). DeepMoCap: Deep optical motion capture using multiple depth sensors and retro-reflectors. Sensors, 19.","DOI":"10.3390\/s19020282"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lee, J.-N., Byeon, Y.-H., and Kwak, K.-C. (2018). Design of ensemble stacked auto-encoder for classification of horse gaits with MEMS inertial sensor technology. Micromachines, 9.","DOI":"10.3390\/mi9080411"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"60736","DOI":"10.1109\/ACCESS.2019.2913393","article-title":"Robust Human Activity Recognition Using Multimodal Feature-Level Fusion","volume":"7","author":"Javed","year":"2019","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"166678","DOI":"10.1109\/ACCESS.2020.3022971","article-title":"Sensor location analysis and minimal deployment for fall detection system","volume":"8","author":"Ponce","year":"2020","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ahuja, K., Jiang, Y., Goel, M., and Harrison, C. (2021, January 8\u201313). Vid2doppler: Synthesizing doppler radar data from videos for training privacy-preserving activity recognition. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, Yokohama, Japan.","DOI":"10.1145\/3411764.3445138"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Echeverria, J., and Santos, O.C. (2021). Toward modeling psychomotor performance in karate combats using computer vision pose estimation. Sensors, 21.","DOI":"10.3390\/s21248378"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/s00530-021-00815-4","article-title":"A review of computer vision-based approaches for physical rehabilitation and assessment","volume":"28","author":"Debnath","year":"2021","journal-title":"Multimed. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"e764","DOI":"10.7717\/peerj-cs.764","article-title":"Syntactic model-based human body 3D reconstruction and event classification via association based features mining and deep learning","volume":"7","author":"Ghadi","year":"2021","journal-title":"PeerJ Comput. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Song, Y., Jin, T., Dai, Y., Song, Y., and Zhou, X. (2021). Through-Wall Human Pose Reconstruction via UWB MIMO Radar and 3D CNN. Remote Sens., 13.","DOI":"10.3390\/rs13020241"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Kico, I., Grammalidis, N., Christidis, Y., and Liarokapis, F. (2018). Digitization and visualization of folk dances in cultural heritage: A review. Inventions, 3.","DOI":"10.3390\/inventions3040072"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2800210","DOI":"10.1109\/JTEHM.2018.2791609","article-title":"Passive Radar for Opportunistic Monitoring in E-Health Applications","volume":"6","author":"Li","year":"2018","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Elaoud, A., Barhoumi, W., Drira, H., and Zagrouba, E. (2019, January 25\u201327). Weighted linear combination of distances within two manifolds for 3D human action recognition. Proceedings of the 14th International Conference on Computer Vision Theory and Applications, Prague, Czech Republic.","DOI":"10.5220\/0007369000002108"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Kaichi, T., Maruyama, T., Tada, M., and Saito, H. (2020). Resolving position ambiguity of IMU-based human pose with a single RGB camera. Sensors, 20.","DOI":"10.3390\/s20195453"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Yang, C., Wei, Q., Wu, X., Ma, Z., Chen, Q., Wang, X., Wang, H., and Fan, W. (2018). Physical extraction and feature fusion for multi-mode signals in a measurement system for patients in rehabilitation exoskeleton. Sensors, 18.","DOI":"10.3390\/s18082588"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1109\/TMECH.2019.2892069","article-title":"Uncertainty-Based IMU Orientation Tracking Algorithm for Dynamic Motions","volume":"24","author":"Yuan","year":"2019","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2000000094","article-title":"Using inertial sensors for position and orientation estimation","volume":"11","author":"Kok","year":"2017","journal-title":"Found. Trends Signal Process."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Pham, T.T., and Suh, Y.S. (2018). Spline function simulation data generation for walking motion using foot-mounted inertial sensors. Electronics, 8.","DOI":"10.3390\/electronics8010018"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Vijayan, V., Connolly, J.P., Condell, J., McKelvey, N., and Gardiner, P. (2021). Review of Wearable devices and data collection considerations for connected health. Sensors, 21.","DOI":"10.3390\/s21165589"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Kim, J.-W., Choi, J.-Y., Ha, E.-J., and Choi, J.-H. (2023). Human Pose Estimation Using MediaPipe Pose and Optimization Method Based on a Humanoid Model. Appl. Sci., 13.","DOI":"10.3390\/app13042700"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1007\/s00371-021-02070-0","article-title":"Clustering and Identification of key body extremities through topological analysis of multi-sensors 3D data","volume":"38","author":"Mohsin","year":"2022","journal-title":"Vis. Comput."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Uhl\u00e1r, \u00c1., Ambrus, M., K\u00e9kesi, M., Fodor, E., Grand, L., Szathm\u00e1ry, G., R\u00e1cz, K., and Lacza, Z. (2021). Kinect azure\u2013based accurate measurement of dynamic valgus position of the knee\u2014A corrigible predisposing factor of osteoarthritis. Appl. Sci., 11.","DOI":"10.3390\/app11125536"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Xu, C., Zhao, L., Zhu, A., Hu, F., and Li, Y. (2022). CSI-Former: Pay More Attention to Pose Estimation with WiFi. Entropy, 25.","DOI":"10.3390\/e25010020"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Chen, W., Jiang, Z., Guo, H., and Ni, X. (2020). Fall Detection Based on Key Points of Human-Skeleton Using Openpose. Symmetry, 12.","DOI":"10.3390\/sym12050744"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/11\/3371\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:48:00Z","timestamp":1760107680000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/11\/3371"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,24]]},"references-count":52,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["s24113371"],"URL":"https:\/\/doi.org\/10.3390\/s24113371","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,24]]}}}