{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T10:41:13Z","timestamp":1784889673743,"version":"3.55.0"},"reference-count":51,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:00:00Z","timestamp":1699574400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003141","name":"Consejo Nacional de Humanidades, Ciencias y Tecnolog\u00edas (CONAHCYT)","doi-asserted-by":"publisher","award":["1028589"],"award-info":[{"award-number":["1028589"]}],"id":[{"id":"10.13039\/501100003141","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Tecnologico de Monterrey","award":["1028589"],"award-info":[{"award-number":["1028589"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>By observing the actions taken by operators, it is possible to determine the risk level of a work task. One method for achieving this is the recognition of human activity using biosignals and inertial measurements provided to a machine learning algorithm performing such recognition. The aim of this research is to propose a method to automatically recognize physical exertion and reduce noise as much as possible towards the automation of the Job Strain Index (JSI) assessment by using a motion capture wearable device (MindRove armband) and training a quadratic support vector machine (QSVM) model, which is responsible for predicting the exertion depending on the patterns identified. The highest accuracy of the QSVM model was 95.7%, which was achieved by filtering the data, removing outliers and offsets, and performing zero calibration; in addition, EMG signals were normalized. It was determined that, given the job strain index\u2019s purpose, physical exertion detection is crucial to computing its intensity in future work.<\/jats:p>","DOI":"10.3390\/s23229100","type":"journal-article","created":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T02:46:47Z","timestamp":1699843607000},"page":"9100","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Physical Exertion Recognition Using Surface Electromyography and Inertial Measurements for Occupational Ergonomics"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9712-7192","authenticated-orcid":false,"given":"Elsa","family":"Concha-P\u00e9rez","sequence":"first","affiliation":[{"name":"School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, NL, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6495-9980","authenticated-orcid":false,"given":"Hugo G.","family":"Gonzalez-Hernandez","sequence":"additional","affiliation":[{"name":"School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, NL, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2797-9381","authenticated-orcid":false,"given":"Jorge A.","family":"Reyes-Avenda\u00f1o","sequence":"additional","affiliation":[{"name":"School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, NL, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,10]]},"reference":[{"key":"ref_1","unstructured":"National Institute for Occupational Safety and Health (2021, June 28). How to Prevent Musculoskeletal Disorders, Available online: https:\/\/www.cdc.gov\/niosh\/docs\/2012-120\/default.html."},{"key":"ref_2","unstructured":"Podniece, Z., Heuvel, S., and Blatter, B. (2008). Work-Related Musculoskeletal Disorders: Prevention Report, European Agency for Safety and Health at Work."},{"key":"ref_3","unstructured":"(2007). Ergonomics\u2014Manual Handling\u2014Part 3: Handling of Low Loads at High Frequency (Standard No. ISO 11228-3:2007)."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1080\/15428119591016863","article-title":"The strain index: A proposed method to analyze jobs for risk of distal upper extremity disorders","volume":"56","author":"Moore","year":"1995","journal-title":"Am. Ind. Hyg. Assoc. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"04020077","DOI":"10.1061\/(ASCE)CO.1943-7862.0001849","article-title":"Construction activity recognition and ergonomic risk assessment using a wearable insole pressure system","volume":"146","author":"Li","year":"2020","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Rybnik\u00e1r, F., Ka\u010derov\u00e1, I., Ho\u0159ej\u0161\u00ed, P., and \u0160imon, M. (2022). Ergonomics Evaluation Using Motion Capture Technology\u2014Literature Review. Appl. Sci., 13.","DOI":"10.3390\/app13010162"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.procir.2018.03.198","article-title":"Automatic assessment of the ergonomic risk for manual manufacturing and assembly activities through optical motion capture technology","volume":"72","author":"Bortolini","year":"2018","journal-title":"Procedia CIRP"},{"key":"ref_8","unstructured":"Aiello, G., Certa, A., Abusohyon, I., Longo, F., and Padovano, A. (2021, January 7\u20139). Machine Learning approach towards real time assessment of hand-arm vibration risk. Proceedings of the 17th IFAC Symposium on Information Control Problems in Manufacturing INCOM 2021, Budapest, Hungary."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Cerqueira, S.M., Moreira, L., Alpoim, L., Siva, A., and Santos, C.P. (2020, January 15\u201317). An inertial data-based upper body posture recognition tool: A machine learning study approach. Proceedings of the 2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Ponta Delgada, Portugal.","DOI":"10.1109\/ICARSC49921.2020.9096167"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1177\/1071181320641296","article-title":"Hand Posture and Force Estimation Using Surface Electromyography and an Artificial Neural Network","volume":"Volume 64","author":"Wang","year":"2020","journal-title":"Proceedings of the Human Factors and Ergonomics Society Annual Meeting"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"102937","DOI":"10.1016\/j.ergon.2020.102937","article-title":"A narrative review on contemporary and emerging uses of inertial sensing in occupational ergonomics","volume":"76","author":"Lim","year":"2020","journal-title":"Int. J. Ind. Ergon."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"103653","DOI":"10.1016\/j.autcon.2021.103653","article-title":"ANN-based automated scaffold builder activity recognition through wearable EMG and IMU sensors","volume":"126","author":"Bangaru","year":"2021","journal-title":"Autom. Constr."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"03120002","DOI":"10.1061\/(ASCE)CO.1943-7862.0001843","article-title":"Automated methods for activity recognition of construction workers and equipment: State-of-the-art review","volume":"146","author":"Sherafat","year":"2020","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"103177","DOI":"10.1016\/j.autcon.2020.103177","article-title":"Human activity classification based on sound recognition and residual convolutional neural network","volume":"114","author":"Jung","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"107024","DOI":"10.1016\/j.measurement.2019.107024","article-title":"Validity of the Perception Neuron inertial motion capture system for upper body motion analysis","volume":"149","author":"Sers","year":"2020","journal-title":"Measurement"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"31314","DOI":"10.3390\/s151229858","article-title":"Physical Human Activity Recognition Using Wearable Sensors","volume":"15","author":"Attal","year":"2015","journal-title":"Sensors"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.patrec.2018.02.010","article-title":"Deep learning for sensor-based activity recognition: A survey","volume":"119","author":"Wang","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Maurer-Grubinger, C., Holzgreve, F., Fraeulin, L., Betz, W., Erbe, C., Brueggmann, D., Wanke, E.M., Nienhaus, A., Groneberg, D.A., and Ohlendorf, D. (2021). Combining Ergonomic Risk Assessment (RULA) with Inertial Motion Capture Technology in Dentistry\u2014Using the Benefits from Two Worlds. Sensors, 21.","DOI":"10.3390\/s21124077"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Conforti, I., Mileti, I., Del Prete, Z., and Palermo, E. (2020). Measuring biomechanical risk in lifting load tasks through wearable system and machine-learning approach. Sensors, 20.","DOI":"10.3390\/s20061557"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Olivas-Padilla, B.E., Manitsaris, S., Menychtas, D., and Glushkova, A. (2021). Stochastic-Biomechanic Modeling and Recognition of Human Movement Primitives, in Industry, Using Wearables. Sensors, 21.","DOI":"10.3390\/s21072497"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"C\u00f4t\u00e9-Allard, U., Gagnon-Turcotte, G., Laviolette, F., and Gosselin, B. (2019). A low-cost, wireless, 3-d-printed custom armband for semg hand gesture recognition. Sensors, 19.","DOI":"10.3390\/s19122811"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Manjarres, J., Narvaez, P., Gasser, K., Percybrooks, W., and Pardo, M. (2020). Physical workload tracking using human activity recognition with wearable devices. Sensors, 20.","DOI":"10.3390\/s20010039"},{"key":"ref_23","first-page":"114","article-title":"A Motion Capture System for Hand Movement Recognition","volume":"Volume 223","year":"2021","journal-title":"Proceedings of the 21st Congress of the International Ergonomics Association (IEA 2021)"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zare, M., Bodin, J., Sagot, J.C., and Roquelaure, Y. (2020). Quantification of Exposure to Risk Postures in Truck Assembly Operators: Neck, Back, Arms and Wrists. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17176062"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Donisi, L., Cesarelli, G., Coccia, A., Panigazzi, M., Capodaglio, E.M., and D\u2019Addio, G. (2021). Work-Related Risk Assessment According to the Revised NIOSH Lifting Equation: A Preliminary Study Using a Wearable Inertial Sensor and Machine Learning. Sensors, 21.","DOI":"10.3390\/s21082593"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","unstructured":"Jahanbanifar, S., and Akhavian, R. (2018, January 9\u201312). Evaluation of wearable sensors to quantify construction workers muscle force: An ergonomic analysis. Proceedings of the 2018 Winter Simulation Conference (WSC), Gothenburg, Sweden.","DOI":"10.1109\/WSC.2018.8632419"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1132","DOI":"10.1109\/LRA.2019.2894389","article-title":"Activity recognition for ergonomics assessment of industrial tasks with automatic feature selection","volume":"4","author":"Maurice","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1080\/24725838.2019.1608873","article-title":"Static and dynamic work activity classification from a single accelerometer: Implications for ergonomic assessment of manual handling tasks","volume":"7","author":"Hosseinian","year":"2019","journal-title":"IISE Trans. Occup. Ergon. Hum. Factors"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Mudiyanselage, S.E., Nguyen, P.H.D., Rajabi, M.S., and Akhavian, R. (2021). Automated workers\u2019 ergonomic risk assessment in manual material handling using sEMG wearable sensors and machine learning. Electronics, 10.","DOI":"10.3390\/electronics10202558"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.1016\/j.promfg.2018.07.152","article-title":"Worker activity recognition in smart manufacturing using IMU and sEMG signals with convolutional neural networks","volume":"26","author":"Tao","year":"2018","journal-title":"Procedia Manuf."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Taborri, J., Bordignon, M., Marcolin, F., Donati, M., and Rossi, S. (2019, January 4\u20136). Automatic identification and counting of repetitive actions related to an industrial worker. Proceedings of the 2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4. 0&IoT), Naples, Italy.","DOI":"10.1109\/METROI4.2019.8792887"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1080\/00140139.2020.1759700","article-title":"Modelling performance during repetitive precision tasks using wearable sensors: A data-driven approach","volume":"63","author":"Tsao","year":"2020","journal-title":"Ergonomics"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"101104","DOI":"10.1016\/j.aei.2020.101104","article-title":"Deep learning-based classification of work-related physical load levels in construction","volume":"45","author":"Yang","year":"2020","journal-title":"Adv. Eng. Inform."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"101177","DOI":"10.1016\/j.aei.2020.101177","article-title":"Convolutional long short-term memory model for recognizing construction workers\u2019 postures from wearable inertial measurement units","volume":"46","author":"Zhao","year":"2020","journal-title":"Adv. Eng. Inform."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1109\/THMS.2015.2470657","article-title":"The grasp taxonomy of human grasp types","volume":"46","author":"Feix","year":"2015","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"ref_37","unstructured":"MindRove (2021, August 25). Armband, Emg-Based Gesture Control Armband. Available online: https:\/\/mindrove.com\/armband\/."},{"key":"ref_38","unstructured":"Xiaomi (2022, October 14). POCO X3 Pro. Available online: https:\/\/www.mi.com\/mx\/product\/poco-x3-pro\/specs,."},{"key":"ref_39","first-page":"8","article-title":"Standards for surface electromyography: The European project Surface EMG for non-invasive assessment of muscles (SENIAM)","volume":"10","author":"Stegeman","year":"2007","journal-title":"Enschede Roessingh Res. Dev."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Anastasiev, A., Kadone, H., Marushima, A., Watanabe, H., Zaboronok, A., Watanabe, S., Matsumura, A., Suzuki, K., Matsumaru, Y., and Ishikawa, E. (2022). Supervised Myoelectrical Hand Gesture Recognition in Post-Acute Stroke Patients with Upper Limb Paresis on Affected and Non-Affected Sides. Sensors, 22.","DOI":"10.3390\/s22228733"},{"key":"ref_41","unstructured":"Ehrenwerth, J., Eisenkraft, J.B., and Berry, J.M. (2013). Anesthesia Equipment, W.B. Saunders. [2nd ed.]."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.1080\/00140139.2013.799235","article-title":"Placement of forearm surface EMG electrodes in the assessment of hand loading in manual tasks","volume":"56","author":"Takala","year":"2013","journal-title":"Ergonomics"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1123\/jab.13.2.135","article-title":"The use of surface electromyography in biomechanics","volume":"13","year":"1997","journal-title":"J. Appl. Biomech."},{"key":"ref_44","first-page":"30","article-title":"The abc of emg","volume":"1","author":"Konrad","year":"2005","journal-title":"A Pract. Introd. Kinesiol. Electromyogr."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.jelekin.2018.11.014","article-title":"Comparing functional dynamic normalization methods to maximal voluntary isometric contractions for lower limb EMG from walking, cycling and running","volume":"44","author":"Chuang","year":"2019","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.gaitpost.2017.10.026","article-title":"EMG normalization method based on grade 3 of manual muscle testing: Within- and between-day reliability of normalization tasks and application to gait analysis","volume":"60","author":"Pittet","year":"2018","journal-title":"Gait Posture"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Salvendy, G. (2012). Handbook of Human Factors and Ergonomics, John Wiley & Sons.","DOI":"10.1002\/9781118131350"},{"key":"ref_48","unstructured":"Motion Lab Systems (2022, November 15). EMG Analysis Software. Available online: https:\/\/www.motion-labs.com\/software_emg_analysis.html."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"576729","DOI":"10.3389\/fneur.2020.576729","article-title":"Analysis and biophysics of surface EMG for physiotherapists and kinesiologists: Toward a common language with rehabilitation engineers","volume":"11","author":"McManus","year":"2020","journal-title":"Front. Neurol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.aei.2018.08.020","article-title":"Automated ergonomic risk monitoring using body-mounted sensors and machine learning","volume":"38","author":"Nath","year":"2018","journal-title":"Adv. Eng. Inform."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"e200126","DOI":"10.1148\/ryai.2021200126","article-title":"Magician\u2019s corner: 9. Performance metrics for machine learning models","volume":"3","author":"Erickson","year":"2021","journal-title":"Radiol. Artif. Intell."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/22\/9100\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:21:08Z","timestamp":1760131268000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/22\/9100"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,10]]},"references-count":51,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["s23229100"],"URL":"https:\/\/doi.org\/10.3390\/s23229100","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,10]]}}}