{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T01:36:59Z","timestamp":1772242619957,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,6,25]],"date-time":"2023-06-25T00:00:00Z","timestamp":1687651200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"companies Sebach S.p.A. and Caff\u00e8 dei Cercatori S.r.L."}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Despite the automatization of many industrial and logistics processes, human workers are still often involved in the manual handling of loads. These activities lead to many work-related disorders that reduce the quality of life and the productivity of aged workers. A biomechanical analysis of such activities is the basis for a detailed estimation of the biomechanical overload, thus enabling focused prevention actions. Thanks to wearable sensor networks, it is now possible to analyze human biomechanics by an inverse dynamics approach in ecological conditions. The purposes of this study are the conceptualization, formulation, and implementation of a deep learning-assisted fully wearable sensor system for an online evaluation of the biomechanical effort that an operator exerts during a manual material handling task. In this paper, we show a novel, computationally efficient algorithm, implemented in ROS, to analyze the biomechanics of the human musculoskeletal systems by an inverse dynamics approach. We also propose a method for estimating the load and its distribution, relying on an egocentric camera and deep learning-based object recognition. This method is suitable for objects of known weight, as is often the case in logistics. Kinematic data, along with foot contact information, are provided by a fully wearable sensor network composed of inertial measurement units. The results show good accuracy and robustness of the system for object detection and grasp recognition, thus providing reliable load estimation for a high-impact field such as logistics. The outcome of the biomechanical analysis is consistent with the literature. However, improvements in gait segmentation are necessary to reduce discontinuities in the estimated lower limb articular wrenches.<\/jats:p>","DOI":"10.3390\/s23135885","type":"journal-article","created":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T05:28:02Z","timestamp":1687757282000},"page":"5885","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Closed-Chain Inverse Dynamics for the Biomechanical Analysis of Manual Material Handling Tasks through a Deep Learning Assisted Wearable Sensor Network"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9934-4952","authenticated-orcid":false,"given":"Riccardo","family":"Bezzini","sequence":"first","affiliation":[{"name":"Institute of Mechanical Intelligence, Scuola Superiore Sant\u2019Anna, 56127 Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7941-5484","authenticated-orcid":false,"given":"Luca","family":"Crosato","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1718-7719","authenticated-orcid":false,"given":"Massimo","family":"Teppati Los\u00e8","sequence":"additional","affiliation":[{"name":"Institute of Mechanical Intelligence, Scuola Superiore Sant\u2019Anna, 56127 Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5802-541X","authenticated-orcid":false,"given":"Carlo Alberto","family":"Avizzano","sequence":"additional","affiliation":[{"name":"Institute of Mechanical Intelligence, Scuola Superiore Sant\u2019Anna, 56127 Pisa, Italy"},{"name":"Department of Excellence in Robotics and AI, Scuola Superiore Sant\u2019Anna, 56127 Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7418-2332","authenticated-orcid":false,"given":"Massimo","family":"Bergamasco","sequence":"additional","affiliation":[{"name":"Institute of Mechanical Intelligence, Scuola Superiore Sant\u2019Anna, 56127 Pisa, Italy"},{"name":"Department of Excellence in Robotics and AI, Scuola Superiore Sant\u2019Anna, 56127 Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6078-6429","authenticated-orcid":false,"given":"Alessandro","family":"Filippeschi","sequence":"additional","affiliation":[{"name":"Institute of Mechanical Intelligence, Scuola Superiore Sant\u2019Anna, 56127 Pisa, Italy"},{"name":"Department of Excellence in Robotics and AI, Scuola Superiore Sant\u2019Anna, 56127 Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Krishnan, K., Raju, G., and Shawkataly, O. (2021). Prevalence of work-related musculoskeletal disorders: Psychological and physical risk factors. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18179361"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Matheson, E., Minto, R., Zampieri, E., Faccio, M., and Rosati, G. (2019). Human\u2013robot collaboration in manufacturing applications: A review. Robotics, 8.","DOI":"10.3390\/robotics8040100"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.mechatronics.2018.02.009","article-title":"Survey on human\u2013robot collaboration in industrial settings: Safety, intuitive interfaces and applications","volume":"55","author":"Villani","year":"2018","journal-title":"Mechatronics"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e22","DOI":"10.1017\/wtc.2022.17","article-title":"Exoworkathlon: A prospective study approach for the evaluation of industrial exoskeletons","volume":"3","author":"Kopp","year":"2022","journal-title":"Wearable Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1080\/00140139.2021.1970823","article-title":"Methodologies for evaluating exoskeletons with industrial applications","volume":"65","author":"Hoffmann","year":"2022","journal-title":"Ergonomics"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Stefana, E., Marciano, F., Rossi, D., Cocca, P., and Tomasoni, G. (2021). Wearable devices for ergonomics: A systematic literature review. Sensors, 21.","DOI":"10.3390\/s21030777"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Giannini, P., Bassani, G., Avizzano, C.A., and Filippeschi, A. (2020). Wearable sensor network for biomechanical overload assessment in manual material handling. Sensors, 20.","DOI":"10.3390\/s20143877"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"323","DOI":"10.5271\/sjweh.3718","article-title":"Scientific basis of ISO standards on biomechanical risk factors","volume":"44","author":"Armstrong","year":"2018","journal-title":"Scand. J. Work. Environ. Health"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1016\/j.promfg.2020.10.175","article-title":"A standardization approach to virtual commissioning strategies in complex production environments","volume":"51","author":"Albo","year":"2020","journal-title":"Procedia Manuf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1016\/j.procir.2019.03.278","article-title":"Virtual Commissioning\u2013Scientific review and exploratory use cases in advanced production systems","volume":"81","author":"Lechler","year":"2019","journal-title":"Procedia CIRP"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1097\/00003677-200201000-00007","article-title":"Optimization-based models of muscle coordination","volume":"30","author":"Prilutsky","year":"2002","journal-title":"Exerc. Sport Sci. Rev."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1152\/jn.00084.2018","article-title":"Muscle coactivation: Definitions, mechanisms, and functions","volume":"120","author":"Latash","year":"2018","journal-title":"J. Neurophysiol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/S0021-9290(02)00432-3","article-title":"Generating dynamic simulations of movement using computed muscle control","volume":"36","author":"Thelen","year":"2003","journal-title":"J. Biomech."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"111383","DOI":"10.1016\/j.jbiomech.2022.111383","article-title":"Electromyography-driven model-based estimation of ankle torque and stiffness during dynamic joint rotations in perturbed and unperturbed conditions","volume":"145","author":"Cop","year":"2022","journal-title":"J. Biomech."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1109\/TASE.2020.3033664","article-title":"Ankle joint torque estimation using an EMG-driven neuromusculoskeletal model and an artificial neural network model","volume":"18","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1109\/TBME.2017.2704085","article-title":"Robust real-time musculoskeletal modeling driven by electromyograms","volume":"65","author":"Durandau","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1940","DOI":"10.1109\/TBME.2007.901024","article-title":"OpenSim: Open-source software to create and analyze dynamic simulations of movement","volume":"54","author":"Delp","year":"2007","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.piutam.2011.04.023","article-title":"Simbody: Multibody dynamics for biomedical research","volume":"2","author":"Sherman","year":"2011","journal-title":"Procedia IUTAM"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1080\/10255842.2016.1240789","article-title":"Real-time inverse kinematics and inverse dynamics for lower limb applications using OpenSim","volume":"20","author":"Pizzolato","year":"2017","journal-title":"Comput. Methods Biomech. Biomed. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Huston, R. (2008). Principles of Biomechanics, CRC Press.","DOI":"10.1201\/9781420018400"},{"key":"ref_21","unstructured":"Demircan, E. (2012). Robotics-Based Reconstruction and Synthesis of Human Motion, Stanford University."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1007\/s00221-005-0039-5","article-title":"Determining natural arm configuration along a reaching trajectory","volume":"167","author":"Kang","year":"2005","journal-title":"Exp. Brain Res."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kim, W., Lee, J., Tsagarakis, N., and Ajoudani, A. (2017, January 17\u201320). A real-time and reduced-complexity approach to the detection and monitoring of static joint overloading in humans. Proceedings of the 2017 International Conference On Rehabilitation Robotics (ICORR), London, UK.","DOI":"10.1109\/ICORR.2017.8009351"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kim, W., Lorenzini, M., Balatti, P., Wu, Y., and Ajoudani, A. (2019, January 3\u20138). Towards ergonomic control of collaborative effort in multi-human mobile-robot teams. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8967628"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Mortensen, J., Trkov, M., and Merryweather, A. (2018, January 26\u201328). Improved ergonomic risk factor assessment using opensim and inertial measurement units. Proceedings of the 2018 IEEE\/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, Washington, DC, USA.","DOI":"10.1145\/3278576.3278589"},{"key":"ref_26","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":"McAtamney","year":"1993","journal-title":"Appl. Ergon."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Katayama, S., and Ohtsuka, T. (June, January 30). Efficient solution method based on inverse dynamics for optimal control problems of rigid body systems. Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi\u2019an, China.","DOI":"10.1109\/ICRA48506.2021.9561109"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Borik, S., Kmecova, A., Gasova, M., and Gaso, M. (2019, January 1\u20133). Smart glove to measure a grip force of the workers. Proceedings of the 2019 42nd International Conference on Telecommunications and Signal Processing (TSP), Budapest, Hungary.","DOI":"10.1109\/TSP.2019.8768848"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2980179.2982440","article-title":"Data-driven inverse dynamics for human motion","volume":"35","author":"Lv","year":"2016","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"24731","DOI":"10.1109\/JSEN.2021.3113123","article-title":"A Dataset of Human Motion and Muscular Activities in Manual Material Handling Tasks for Biomechanical and Ergonomic Analyses","volume":"21","author":"Bassani","year":"2021","journal-title":"IEEE Sensors J."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1109\/JAS.2021.1003865","article-title":"Deep learning for EMG-based human-machine interaction: A review","volume":"8","author":"Xiong","year":"2021","journal-title":"IEEE-CAA J. Autom. Sin."},{"key":"ref_32","unstructured":"Featherstone, R. (2014). Rigid Body Dynamics Algorithms, Springer."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wong, A., Famuori, M., Shafiee, M., Li, F., Chwyl, B., and Chung, J. (2019, January 13). YOLO nano: A highly compact you only look once convolutional neural network for object detection. Proceedings of the 2019 Fifth Workshop on Energy Efficient Machine Learning and Cognitive Computing-NeurIPS Edition (EMC2-NIPS), Vancouver, BC, Canada.","DOI":"10.1109\/EMC2-NIPS53020.2019.00013"},{"key":"ref_35","unstructured":"Jocher, G., Chaurasia, A., and Qiu, J. (2023, March 15). YOLO by Ultralytics (Version 8.0.0). Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lin, T., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C. (2014;, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the Computer Vision\u2014ECCV 2014: 13th European Conference, Zurich, Switzerland. Proceedings,  Part V 13.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2750","DOI":"10.1016\/j.jbiomech.2008.06.001","article-title":"Whole body inverse dynamics over a complete gait cycle based only on measured kinematics","volume":"41","author":"Ren","year":"2008","journal-title":"J. Biomech."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"103156","DOI":"10.1016\/j.apergo.2020.103156","article-title":"Biomechanical assessment of two back-support exoskeletons in symmetric and asymmetric repetitive lifting with moderate postural demands","volume":"88","author":"Madinei","year":"2020","journal-title":"Appl. Ergon."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.apergo.2017.10.008","article-title":"Physiological consequences of using an upper limb exoskeleton during manual handling tasks","volume":"67","author":"Theurel","year":"2018","journal-title":"Appl. Ergon."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"102985","DOI":"10.1016\/j.apergo.2019.102985","article-title":"Workers\u2019 biomechanical loads and kinematics during multiple-task manual material handling","volume":"83","author":"Harari","year":"2020","journal-title":"Appl. Ergon."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.apergo.2018.08.018","article-title":"Biomechanical analysis of manual material handling movement in healthy weight and obese workers","volume":"74","author":"Corbeil","year":"2019","journal-title":"Appl. Ergon."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/LRA.2017.2729666","article-title":"Anticipatory robot assistance for the prevention of human static joint overloading in human\u2013robot collaboration","volume":"3","author":"Kim","year":"2017","journal-title":"IEEE Robot. Autom. Lett."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/5885\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:00:24Z","timestamp":1760126424000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/5885"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,25]]},"references-count":42,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["s23135885"],"URL":"https:\/\/doi.org\/10.3390\/s23135885","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,25]]}}}