{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T17:42:40Z","timestamp":1789234960117,"version":"build-2803163510"},"reference-count":42,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2025,9,14]],"date-time":"2025-09-14T00:00:00Z","timestamp":1757808000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"TransMedTech","award":["RRGPIN-2022-04217"],"award-info":[{"award-number":["RRGPIN-2022-04217"]}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council","doi-asserted-by":"publisher","award":["RRGPIN-2022-04217"],"award-info":[{"award-number":["RRGPIN-2022-04217"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fonds de Recherche Qu\u00e9bec (FRQ) (Sant\u00e9 sector)","award":["RRGPIN-2022-04217"],"award-info":[{"award-number":["RRGPIN-2022-04217"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As advancements in inertial measurement units (IMUs) for motion analysis progress, the inability to directly apply decades of research-based optical motion capture (OMC) methodologies presents a significant challenge. This study aims to bridge this gap by proposing an innovative deep learning approach to predict marker positions from IMU data, allowing traditional OMC-based calculations to estimate joint kinematics. Eighteen participants walked on a treadmill with seven IMUs and retroreflective markers. Trials were divided into normalized gait cycles (101 frames), and an autoencoder network with a custom Biomech loss function was used to predict 16 marker positions from IMU data. The model was validated using the leave-one-subject-out method and assessed using root mean squared error (RMSE). Joint angles in the sagittal plane were calculated using OMC methods, and RMSE was computed with and without alignment using dynamic time warping (DTW). The models were also tested on external datasets. Marker predictions achieved RMSE values of 2\u20134 cm, enabling joint angle predictions with 4\u20137\u00b0 RMSE without alignment and 2\u20134\u00b0 RMSE after DTW for sagittal plane joint angles (ankle, knee, hip). Validation using separate and open-source datasets confirmed the model\u2019s generalizability, with similar RMSE values across datasets (4\u20137\u00b0 RMSE without DTW and 2\u20134\u00b0 with DTW). This study demonstrates the feasibility of applying conventional biomechanical models to IMUs, enabling accurate movement analysis and visualization outside controlled environments. This approach to predicting marker positions helps to bridge the gap between IMUs and OMC systems, enabling decades of research-based biomechanical methodologies to be applied to IMU data.<\/jats:p>","DOI":"10.3390\/s25185728","type":"journal-article","created":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T11:51:43Z","timestamp":1757937103000},"page":"5728","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Bridging the Methodological Gap Between Inertial Sensors and Optical Motion Capture: Deep Learning as the Path to Accurate Joint Kinematic Modelling Using Inertial Sensors"],"prefix":"10.3390","volume":"25","author":[{"given":"Vaibhav R.","family":"Shah","sequence":"first","affiliation":[{"name":"Institute of Biomedical Engineering, Faculty of Medicine, University of Montreal, Montreal, QC H3T 1J4, Canada"},{"name":"Centre de Recherche Azrieli du CHU Sainte-Justine (CRCHUSJ), Montreal, QC H3T 1C5, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3581-7259","authenticated-orcid":false,"given":"Philippe C.","family":"Dixon","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Engineering, Faculty of Medicine, University of Montreal, Montreal, QC H3T 1J4, Canada"},{"name":"Centre de Recherche Azrieli du CHU Sainte-Justine (CRCHUSJ), Montreal, QC H3T 1C5, Canada"},{"name":"Department of Kinesiology and Physical Activity, McGill University, Montreal, QC H2W 1S4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gurchiek, R.D., Cheney, N., and McGinnis, R.S. (2019). McGinnis. Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques. Sensors, 19.","DOI":"10.20944\/preprints201911.0006.v1"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jbiomech.2018.09.009","article-title":"Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities","volume":"81","author":"Halilaj","year":"2018","journal-title":"J. Biomech."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Vienne, A., Barrois, R.P., Buffat, S., Ricard, D., and Vidal, P.-P. (2017). Inertial Sensors to Assess Gait Quality in Patients with Neurological Disorders: A Systematic Review of Technical and Analytical Challenges. Front. Psychol., 8.","DOI":"10.3389\/fpsyg.2017.00817"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1123\/jsr.2016-0125","article-title":"A Comparison of Both Motorized and Nonmotorized Treadmill Gait Kinematics to Overground Locomotion","volume":"27","author":"Fullenkamp","year":"2017","journal-title":"J. Sport Rehabil."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Renggli, D., Graf, C., Tachatos, N., Singh, N., Meboldt, M., Taylor, W.R., Stieglitz, L., and Daners, M.S. (2020). Wearable Inertial Measurement Units for Assessing Gait in Real-World Environments. Front. Physiol., 11.","DOI":"10.3389\/fphys.2020.00090"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Carcreff, L., Gerber, C.N., Paraschiv-Ionescu, A., De Coulon, G., Newman, C.J., Aminian, K., and Armand, S. (2020). Comparison of gait characteristics between clinical and daily life settings in children with cerebral palsy. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-59002-6"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Adesida, Y., Papi, E., McGregor, A.H., McGregor, A.H., and McGregor, A.H. (2019). Exploring the Role of Wearable Technology in Sport Kinematics and Kinetics: A Systematic Review. Sensors, 19.","DOI":"10.3390\/s19071597"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1109\/JSEN.2020.3019016","article-title":"Wearable Sensors for Real-Time Kinematics Analysis in Sports: A Review","volume":"21","author":"Rana","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Weygers, I., Kok, M., Konings, M.J., Hallez, H., De Vroey, H., and Claeys, K. (2020). Inertial Sensor-Based Lower Limb Joint Kinematics: A Methodological Systematic Review. Sensors, 20.","DOI":"10.3390\/s20030673"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s12984-022-01001-x","article-title":"OpenSense: An open-source toolbox for inertial-measurement-unit-based measurement of lower extremity kinematics over long durations","volume":"19","author":"Ibarra","year":"2022","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.jbiomech.2018.12.023","article-title":"Assessment of the measurement accuracy of inertial sensors during different tasks of daily living","volume":"84","author":"Mundt","year":"2019","journal-title":"J. Biomech."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.jbiomech.2017.03.015","article-title":"Measuring joint kinematics of treadmill walking and running: Comparison between an inertial sensor based system and a camera-based system","volume":"57","author":"Roos","year":"2017","journal-title":"J. Biomech."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s12283-012-0103-x","article-title":"Impact of hockey skate design on ankle motion and force production","volume":"15","author":"Turcotte","year":"2012","journal-title":"Sports Eng."},{"key":"ref_14","first-page":"1","article-title":"Xsens MVN: Consistent Tracking of Human Motion Using Inertial Sensing","volume":"1","author":"Schepers","year":"2018","journal-title":"Xsens Technol"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Shah, V.R., Dixon, P.C., and Willmott, A.P. (2024). Evaluation of lower-body gait kinematics on outdoor surfaces using wearable sensors. J. Biomech., 177.","DOI":"10.1016\/j.jbiomech.2024.112401"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Dorschky, E., Nitschke, M., Martindale, C.F., van den Bogert, A.J., Koelewijn, A.D., and Eskofier, B.M. (2020). CNN-Based Estimation of Sagittal Plane Walking and Running Biomechanics from Measured and Simulated Inertial Sensor Data. Front. Bioeng. Biotechnol., 8.","DOI":"10.3389\/fbioe.2020.00604"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mundt, M., Johnson, W.R., Potthast, W., Markert, B., Mian, A., and Alderson, J. (2021). A Comparison of Three Neural Network Approaches for Estimating Joint Angles and Moments from Inertial Measurement Units. Sensors, 21.","DOI":"10.3390\/s21134535"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"100183","DOI":"10.1016\/j.mcpdig.2024.11.004","article-title":"Gait Speed and Task Specificity in Predicting Lower-Limb Kinematics: A Deep Learning Approach Using Inertial Sensors","volume":"3","author":"Shah","year":"2025","journal-title":"Mayo Clin. Proc. Digit. Health"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Senanayake, D., Halgamuge, S.K., and Ackland, D.C. (2021). Real-time conversion of inertial measurement unit data to ankle joint angles using deep neural networks. J. Biomech., 125.","DOI":"10.1016\/j.jbiomech.2021.110552"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sung, J., Han, S., Park, H., Cho, H.-M., Hwang, S., Park, J.W., and Youn, I. (2021). Prediction of Lower Extremity Multi-Joint Angles during Overground Walking by Using a Single IMU with a Low Frequency Based on an LSTM Recurrent Neural Network. Sensors, 22.","DOI":"10.3390\/s22010053"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tan, J.-S., Tippaya, S., Binnie, T., Davey, P., Napier, K., Caneiro, J.P., Kent, P., Smith, A., O\u2019sullivan, P., and Campbell, A. (2022). Predicting Knee Joint Kinematics from Wearable Sensor Data in People with Knee Osteoarthritis and Clinical Considerations for Future Machine Learning Models. Sensors, 22.","DOI":"10.3390\/s22020446"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Rapp, E., Shin, S., Thomsen, W., Ferber, R., and Halilaj, E. (2021). Estimation of kinematics from inertial measurement units using a combined deep learning and optimization framework. J. Biomech., 116.","DOI":"10.1016\/j.jbiomech.2021.110229"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Renani, M.S., Eustace, A.M., Myers, C.A., and Clary, C.W. (2021). The Use of Synthetic IMU Signals in the Training of Deep Learning Models Significantly Improves the Accuracy of Joint Kinematic Predictions. Sensors, 21.","DOI":"10.3390\/s21175876"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Warmerdam, E., Hansen, C., Romijnders, R., Hobert, M.A., Welzel, J., and Maetzler, W. (2022). Full-Body Mobility Data to Validate Inertial Measurement Unit Algorithms in Healthy and Neurological Cohorts. Data, 7.","DOI":"10.3390\/data7100136"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1002\/jor.1100080310","article-title":"Measurement of lower extremity kinematics during level walking","volume":"8","author":"Kadaba","year":"1990","journal-title":"J. Orthop. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.gaitpost.2021.09.176","article-title":"The impact of outdoor walking surfaces on lower-limb coordination and variability during gait in healthy adults","volume":"91","author":"Ippersiel","year":"2022","journal-title":"Gait Posture"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Shah, V., Flood, M.W., Grimm, B., and Dixon, P.C. (2022). Generalizability of deep learning models for predicting outdoor irregular walking surfaces. J. Biomech., 139.","DOI":"10.1016\/j.jbiomech.2022.111159"},{"key":"ref_28","first-page":"319","article-title":"Estimate of the Optimum Cutoff Frequency for the Butterworth Low-Pass Digital Filter","volume":"15","author":"Yu","year":"1999","journal-title":"J. Appl. Biomech."},{"key":"ref_29","unstructured":"(2024, July 12). Google Colab. Available online: https:\/\/colab.research.google.com\/."},{"key":"ref_30","unstructured":"(2024, December 16). tf.keras.callbacks.EarlyStopping, v2.16.1; TensorFlow. Available online: https:\/\/www.tensorflow.org\/api_docs\/python\/tf\/keras\/callbacks\/EarlyStopping."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cmpb.2016.11.007","article-title":"Biomechzoo: An open-source toolbox for the processing, analysis, and visualization of biomechanical movement data","volume":"140","author":"Dixon","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ramsay, J.O., and Silverman, B.W. (2005). Functional Data Analysis, Springer.","DOI":"10.1007\/b98888"},{"key":"ref_33","unstructured":"Yurtman, A., Robberechts, P., Vohl, D., Ma, E., Verbruggen, G., Rossi, M., Shaikh, M., Yasirroni, M., Zieli\u0144ski, W., and Van Craenendonck, T. (2022). wannesm\/dtaidistance, Zenodo. Available online: https:\/\/zenodo.org\/records\/5901139."},{"key":"ref_34","unstructured":"Meert, W. (2024, March 10). Dtaidistance: Distance Measures for Time Series (Dynamic Time Warping, Fast C Implementation). Python. Available online: https:\/\/github.com\/wannesm\/dtaidistance."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3714","DOI":"10.21105\/joss.03714","article-title":"Kinetics Toolkit: An Open-Source Python Package to Facilitate Research in Biomechanics","volume":"6","year":"2021","journal-title":"J. Open Source Softw."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1186\/s12984-024-01416-8","article-title":"Inter-joint coordination with and without dopaminergic medication in Parkinson\u2019s disease: A case-control study","volume":"21","author":"Saegner","year":"2024","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Uhlrich, S.D., Falisse, A., Kidzi\u0144ski, \u0141., Muccini, J., Ko, M., Chaudhari, A.S., Hicks, J.L., Delp, S.L., and Marsden, A.L. (2022). OpenCap: 3D human movement dynamics from smartphone videos. bioRxiv.","DOI":"10.1101\/2022.07.07.499061"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1123\/jab.2023-0069","article-title":"Comparison of Concurrent and Asynchronous Running Kinematics and Kinetics from Marker-Based and Markerless Motion Capture Under Varying Clothing Conditions","volume":"40","author":"Kanko","year":"2024","journal-title":"J. Appl. Biomech."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.gaitpost.2007.11.001","article-title":"Predicting lower limb joint kinematics using wearable motion sensors","volume":"28","author":"Findlow","year":"2008","journal-title":"Gait Posture"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Mundt, M., Koeppe, A., David, S., Witter, T., Bamer, F., Potthast, W., and Markert, B. (2020). Estimation of Gait Mechanics Based on Simulated and Measured IMU Data Using an Artificial Neural Network. Front. Bioeng. Biotechnol., 8.","DOI":"10.3389\/fbioe.2020.00041"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wouda, F.J., Giuberti, M., Bellusci, G., Maartens, E., Reenalda, J., van Beijnum, B.-J.F., and Veltink, P.H. (2018). Estimation of Vertical Ground Reaction Forces and Sagittal Knee Kinematics During Running Using Three Inertial Sensors. Front. Physiol., 9.","DOI":"10.3389\/fphys.2018.00218"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/j.gaitpost.2008.09.003","article-title":"The reliability of three-dimensional kinematic gait measurements: A systematic review","volume":"29","author":"McGinley","year":"2009","journal-title":"Gait Posture"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/18\/5728\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:45:21Z","timestamp":1760035521000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/18\/5728"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,14]]},"references-count":42,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["s25185728"],"URL":"https:\/\/doi.org\/10.3390\/s25185728","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,14]]}}}