{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:45:41Z","timestamp":1760402741004,"version":"build-2065373602"},"reference-count":50,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,4,3]],"date-time":"2021-04-03T00:00:00Z","timestamp":1617408000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["822336","820767"],"award-info":[{"award-number":["822336","820767"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In industry, ergonomists apply heuristic methods to determine workers\u2019 exposure to ergonomic risks; however, current methods are limited to evaluating postures or measuring the duration and frequency of professional tasks. The work described here aims to deepen ergonomic analysis by using joint angles computed from inertial sensors to model the dynamics of professional movements and the collaboration between joints. This work is based on the hypothesis that with these models, it is possible to forecast workers\u2019 posture and identify the joints contributing to the motion, which can later be used for ergonomic risk prevention. The modeling was based on the Gesture Operational Model, which uses autoregressive models to learn the dynamics of the joints by assuming associations between them. Euler angles were used for training to avoid forecasting errors such as bone stretching and invalid skeleton configurations, which commonly occur with models trained with joint positions. The statistical significance of the assumptions of each model was computed to determine the joints most involved in the movements. The forecasting performance of the models was evaluated, and the selection of joints was validated, by achieving a high gesture recognition performance. Finally, a sensitivity analysis was conducted to investigate the response of the system to disturbances and their effect on the posture.<\/jats:p>","DOI":"10.3390\/s21072497","type":"journal-article","created":{"date-parts":[[2021,4,3]],"date-time":"2021-04-03T22:03:36Z","timestamp":1617487416000},"page":"2497","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Stochastic-Biomechanic Modeling and Recognition of Human Movement Primitives, in Industry, Using Wearables"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7436-4822","authenticated-orcid":false,"given":"Brenda Elizabeth","family":"Olivas-Padilla","sequence":"first","affiliation":[{"name":"Centre for Robotics, MINES ParisTech, PSL Universit\u00e9, 75006 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4552-1793","authenticated-orcid":false,"given":"Sotiris","family":"Manitsaris","sequence":"additional","affiliation":[{"name":"Centre for Robotics, MINES ParisTech, PSL Universit\u00e9, 75006 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dimitrios","family":"Menychtas","sequence":"additional","affiliation":[{"name":"Centre for Robotics, MINES ParisTech, PSL Universit\u00e9, 75006 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alina","family":"Glushkova","sequence":"additional","affiliation":[{"name":"Centre for Robotics, MINES ParisTech, PSL Universit\u00e9, 75006 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1408","DOI":"10.1080\/00140139508925198","article-title":"Upper limb musculoskeletal disorders in highly repetitive industries: Precise anatomical physical findings","volume":"38","author":"Ranney","year":"1995","journal-title":"Ergonomics"},{"key":"ref_2","unstructured":"De Kok, J., Vroonhof, P., Snijders, J., Roullis, G., Clarke, M., Peereboom, K., van Dorst, P., and Isusi, I. (2019). Work-Related Musculoskeletal Disorders: Prevalence, Costs and Demographics in the EU, European Agency for Safety and Health at Work. Technical Report."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.apergo.2014.12.011","article-title":"Influence of musculoskeletal pain on workers\u2019 ergonomic risk-factor assessments","volume":"49","author":"Chiasson","year":"2015","journal-title":"Appl. Ergon."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/0003-6870(93)90080-S","article-title":"RULA: A survey method for the investigation of work-related upper limb disorders","volume":"24","author":"Lynn","year":"1993","journal-title":"Appl. Ergon."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1080\/1463922X.2012.678283","article-title":"The European Assembly Worksheet","volume":"14","author":"Schaub","year":"2013","journal-title":"Theor. Issues Ergon. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/0003-6870(77)90164-8","article-title":"Correcting working postures in industry: A practical method for analysis","volume":"8","author":"Karhu","year":"1977","journal-title":"Appl. Ergon."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1080\/10803548.2008.11076755","article-title":"An investigation of ergonomics analysis tools used in industry in the identification of work-related musculoskeletal disorders","volume":"14","author":"Pascual","year":"2008","journal-title":"Int. J. Occup. Saf. Ergon."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1080\/00140139108964855","article-title":"The design of manual handling tasks: Revised tables of maximum acceptable weights and forces","volume":"34","author":"Snook","year":"1991","journal-title":"Ergonomics"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1093\/occmed\/kqi082","article-title":"Ergonomic methods for assessing exposure to risk factors for work-related musculoskeletal disorders","volume":"55","author":"David","year":"2005","journal-title":"Occup. Med."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Busch, B., Maeda, G., Mollard, Y., Demangeat, M., and Lopes, M. (2017, January 24\u201328). Postural optimization for an ergonomic human-robot interaction. Proceedings of the 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8206107"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1016\/j.apergo.2017.02.015","article-title":"Real time RULA assessment using Kinect v2 sensor","volume":"65","author":"Manghisi","year":"2017","journal-title":"Appl. Ergon."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1016\/j.apergo.2016.10.015","article-title":"Validation of an ergonomic assessment method using Kinect data in real workplace conditions","volume":"65","author":"Plantard","year":"2016","journal-title":"Appl. Ergon."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1016\/j.apergo.2012.11.008","article-title":"Innovative system for real-time ergonomic feedback in industrial manufacturing","volume":"44","author":"Vignais","year":"2013","journal-title":"Appl. Ergon."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/frobt.2020.00080","article-title":"Human movement representation on multivariate time series for recognition of professional gestures and forecasting their trajectories","volume":"7","author":"Manitsaris","year":"2020","journal-title":"Front. Robot. AI"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"S13","DOI":"10.1016\/j.kjms.2011.08.004","article-title":"Biomechanics of human movement and its clinical applications","volume":"28","author":"Lu","year":"2012","journal-title":"Kaohsiung J. Med. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"102935","DOI":"10.1016\/j.apergo.2019.102935","article-title":"Motion-based prediction of external forces and moments and back loading during manual material handling tasks","volume":"82","author":"Muller","year":"2020","journal-title":"Appl. Ergon."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12652-020-01926-y","article-title":"Analyzing the kinematic and kinetic contributions of the human upper body\u2019s joints for ergonomics assessment","volume":"11","author":"Menychtas","year":"2020","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"904","DOI":"10.1016\/j.jbiomech.2015.11.042","article-title":"Estimating 3D L5\/S1 moments and ground reaction forces during trunk bending using a full-body ambulatory inertial motion capture system","volume":"49","author":"Faber","year":"2016","journal-title":"J. Biomech."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1016\/j.jbiomech.2015.10.037","article-title":"Age related differences in mechanical demands imposed on the lower back by manual material handling tasks","volume":"49","author":"Shojaei","year":"2016","journal-title":"J. Biomech."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1177\/0278364913478447","article-title":"Probabilistic movement modeling for intention inference in human-robot interaction","volume":"32","author":"Wang","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Agarwal, A., and Triggs, B. (2004, January 11\u201314). Tracking articulated motion using a mixture of autoregressive models. Proceedings of the Computer Vision\u2014ECCV 2004, Prague, Czech Republic.","DOI":"10.1007\/978-3-540-24672-5_5"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.patcog.2016.07.041","article-title":"Motion segment decomposition of RGB-D sequences for human behavior understanding","volume":"61","author":"Devanne","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1016\/j.jbiomech.2008.12.005","article-title":"A stochastic biomechanical model for risk and risk factors of non-contact anterior cruciate ligament injuries","volume":"42","author":"Lin","year":"2009","journal-title":"J. Biomech."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1080\/23310472.2014.983166","article-title":"A stochastic structural reliability model explains rotator cuff repair retears","volume":"1","author":"Donnell","year":"2014","journal-title":"Int. Biomech."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Fardi, B., Schuenert, U., and Wanielik, G. (2005, January 6\u20138). Shape and motion-based pedestrian detection in infrared images: A multi sensor approach. Proceedings of the IEEE Intelligent Vehicles Symposium, Las Vegas, NV, USA.","DOI":"10.1109\/IVS.2005.1505071"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Binelli, E., Broggi, A., Fascioli, A., Ghidoni, S., Grisleri, P., Graf, T., and Meinecke, M. (2005, January 6\u20138). A modular tracking system for far infrared pedestrian recognition. Proceedings of the Intelligent Vehicles Symposium, Las Vegas, NV, USA.","DOI":"10.1109\/IVS.2005.1505196"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Schneider, N., and Gavrila, D.M. (2013). Pedestrian path prediction with recursive Bayesian filters: A comparative study. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer. 8142 LNCS.","DOI":"10.1007\/978-3-642-40602-7_18"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Barth, A., and Franke, U. (2008, January 4\u20136). Where will the oncoming vehicle be the next second?. Proceedings of the 2008 IEEE Intelligent Vehicles Symposium, Eindhoven, The Netherlands.","DOI":"10.1109\/IVS.2008.4621210"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zernetsch, S., Kohnen, S., Goldhammer, M., Doll, K., and Sick, B. (2016, January 19\u201322). Trajectory prediction of cyclists using a physical model and an artificial neural network. Proceedings of the 2016 IEEE Intelligent Vehicles Symposium (IV), Gothenburg, Sweden.","DOI":"10.1109\/IVS.2016.7535484"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Pool, E.A., Kooij, J.F., and Gavrila, D.M. (2017, January 11\u201314). Using road topology to improve cyclist path prediction. Proceedings of the 2017 IEEE Intelligent Vehicles Symposium (IV), Los Angeles, CA, USA.","DOI":"10.1109\/IVS.2017.7995734"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s11263-018-1104-4","article-title":"Context-based path prediction for targets with switching dynamics","volume":"127","author":"Kooij","year":"2019","journal-title":"Int. J. Comput. Vis."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1803","DOI":"10.1109\/TITS.2018.2836305","article-title":"Pedestrian path, pose and intention prediction through Gaussian process dynamical models and pedestrian activity recognition","volume":"20","author":"Quintero","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1109\/LRA.2017.2660060","article-title":"Enabling flow awareness for mobile robots in partially observable environments","volume":"2","author":"Kucner","year":"2017","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sun, L., Yan, Z., Mellado, S.M., Hanheide, M., and Duckett, T. (2017, January 21\u201325). 3DOF pedestrian trajectory prediction learned from long-term autonomous mobile robot deployment data. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia.","DOI":"10.1109\/ICRA.2018.8461228"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Xue, H., Huynh, D.Q., and Reynolds, M. (2018, January 12\u201315). SS-LSTM: A Hierarchical LSTM Model for Pedestrian Trajectory Prediction. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00135"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Srikanth, S., Ansari, J.A., Ram, R.K., Sharma, S., Murthy, J.K., and Krishna, K.M. (2019, January 4\u20138). INFER: INtermediate representations for FuturE pRediction. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8968553"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1177\/0278364920917446","article-title":"Human motion trajectory prediction: A survey","volume":"39","author":"Rudenko","year":"2020","journal-title":"Int. J. Robot. Res."},{"key":"ref_38","unstructured":"Best, G., and Fitch, R. (October, January 28). Bayesian intention inference for trajectory prediction with an unknown goal destination. Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Hamburg, Germany."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1016\/j.apergo.2017.03.016","article-title":"An evaluation of wearable sensors and their placements for analyzing construction worker\u2019s trunk posture in laboratory conditions","volume":"65","author":"Lee","year":"2017","journal-title":"Appl. Ergon."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ergon.2015.07.002","article-title":"A novel wearable system for the online assessment of risk for biomechanical load in repetitive efforts","volume":"52","author":"Peppoloni","year":"2014","journal-title":"Int. J. Ind. Ergon."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ryu, J., Seo, J., Jebelli, H., and Lee, S. (2019). Automated action recognition using an accelerometer-embedded wristband-type activity tracker. J. Constr. Eng. Manag., 145.","DOI":"10.1061\/(ASCE)CO.1943-7862.0001579"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"103138","DOI":"10.1016\/j.autcon.2020.103138","article-title":"Construction activity recognition with convolutional recurrent networks","volume":"113","author":"Slaton","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3153","DOI":"10.1109\/LRA.2019.2925305","article-title":"Toward ergonomic risk prediction via segmentation of indoor object manipulation actions using spatiotemporal convolutional networks","volume":"4","author":"Parsa","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Caramiaux, B., Montecchio, N., Tanaka, A., and Bevilacqua, F. (2015). Adaptive gesture recognition with variation estimation for interactive systems. ACM Trans. Interact. Intell. Syst., 4.","DOI":"10.1145\/2643204"},{"key":"ref_45","unstructured":"Pavlovic, V., Rehg, J.M., and MacCormick, J. (2001, January 3\u20136). Learning switching linear models of human motion. Proceedings of the 13th International Conference on Neural Information Processing Systems, ACM, Hong Kong, China."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Aksan, E., Kaufmann, M., and Hilliges, O. (2019, January 27\u201328). Structured Prediction Helps 3D Human Motion Modelling. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00724"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1007\/s11263-019-01245-6","article-title":"Modeling Human Motion with Quaternion-Based Neural Networks","volume":"128","author":"Pavllo","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_48","unstructured":"Wang, J., and Tang, S. (2020, January 22\u201323). Time series classification based on arima and adaboost. Proceedings of the International Conference on Computer Science Communication and Network Security (CSCNS2019), Sanya, China."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.1109\/34.643892","article-title":"A state-based approach to the representation and recognition of gesture","volume":"19","author":"Bobick","year":"1997","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_50","first-page":"929","article-title":"Kalman filtering for maximum likelihood estimation given corrupted observations","volume":"22","author":"Holmes","year":"2003","journal-title":"Natl. Mar. Fish. Serv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/7\/2497\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:58:16Z","timestamp":1760363896000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/7\/2497"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,3]]},"references-count":50,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["s21072497"],"URL":"https:\/\/doi.org\/10.3390\/s21072497","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,4,3]]}}}