{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T14:41:35Z","timestamp":1780065695333,"version":"3.54.0"},"reference-count":25,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,6]],"date-time":"2021-01-06T00:00:00Z","timestamp":1609891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01EB028105"],"award-info":[{"award-number":["R01EB028105"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>(1) Background: Low back disorders are a leading cause of missed work and physical disability in manual material handling due to repetitive lumbar loading and overexertion. Ergonomic assessments are often performed to understand and mitigate the risk of musculoskeletal overexertion injuries. Wearable sensor solutions for monitoring low back loading have the potential to improve the quality, quantity, and efficiency of ergonomic assessments and to expand opportunities for the personalized, continuous monitoring of overexertion injury risk. However, existing wearable solutions using a single inertial measurement unit (IMU) are limited in how accurately they can estimate back loading when objects of varying mass are handled, and alternative solutions in the scientific literature require so many distributed sensors that they are impractical for widespread workplace implementation. We therefore explored new ways to accurately monitor low back loading using a small number of wearable sensors. (2) Methods: We synchronously collected data from laboratory instrumentation and wearable sensors to analyze 10 individuals each performing about 400 different material handling tasks. We explored dozens of candidate solutions that used IMUs on various body locations and\/or pressure insoles. (3) Results: We found that the two key sensors for accurately monitoring low back loading are a trunk IMU and pressure insoles. Using signals from these two sensors together with a Gradient Boosted Decision Tree algorithm has the potential to provide a practical (relatively few sensors), accurate (up to r2 = 0.89), and automated way (using wearables) to monitor time series lumbar moments across a broad range of material handling tasks. The trunk IMU could be replaced by thigh IMUs, or a pelvis IMU, without sacrificing much accuracy, but there was no practical substitute for the pressure insoles. The key to realizing accurate lumbar load estimates with this approach in the real world will be optimizing force estimates from pressure insoles. (4) Conclusions: Here, we present a promising wearable solution for the practical, automated, and accurate monitoring of low back loading during manual material handling.<\/jats:p>","DOI":"10.3390\/s21020340","type":"journal-article","created":{"date-parts":[[2021,1,6]],"date-time":"2021-01-06T20:45:42Z","timestamp":1609965942000},"page":"340","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["A Promising Wearable Solution for the Practical and Accurate Monitoring of Low Back Loading in Manual Material Handling"],"prefix":"10.3390","volume":"21","author":[{"given":"Emily S.","family":"Matijevich","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Vanderbilt University, Nashville, TN 37212, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7478-4317","authenticated-orcid":false,"given":"Peter","family":"Volgyesi","sequence":"additional","affiliation":[{"name":"Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN 37212, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karl E.","family":"Zelik","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Vanderbilt University, Nashville, TN 37212, USA"},{"name":"Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA"},{"name":"Department of Physical Medicine &amp; Rehabilitation, Vanderbilt University, Nashville, TN 37232, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,6]]},"reference":[{"key":"ref_1","unstructured":"U.S. Department of Labor (2020, October 09). Back Injuries Prominent in Work-Related Musculoskeletal Disorder Cases in 2016, Available online: https:\/\/www.bls.gov\/news.release\/archives\/osh_11092017.htm."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"301","DOI":"10.7326\/M18-3602","article-title":"Prevalence, Recognition of Work-Relatedness, and Effect on Work of Low Back Pain Among U.S. Workers","volume":"171","author":"Luckhaupt","year":"2019","journal-title":"Ann. Intern. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/j.jmpt.2016.07.004","article-title":"Low Back Pain Prevalence and Related Workplace Psychosocial Risk Factors: A Study Using Data From the 2010 National Health Interview Survey","volume":"39","author":"Yang","year":"2016","journal-title":"J. Manip. Physiol. Ther."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.apergo.2017.04.016","article-title":"Development and validation of an easy-to-use risk assessment tool for cumulative low back loading: The Lifting Fatigue Failure Tool (LiFFT)","volume":"63","author":"Gallagher","year":"2017","journal-title":"Appl. Ergon."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1080\/00140139.2016.1208848","article-title":"Musculoskeletal disorders as a fatigue failure process: Evidence, implications and research needs","volume":"60","author":"Gallagher","year":"2016","journal-title":"Ergonomics"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Edwards, W.B. (2018). Modeling Overuse Injuries in Sport as a Mechanical Fatigue Phenomenon. Exerc. Sport Sci. Rev.","DOI":"10.1249\/JES.0000000000000163"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.1080\/00140130701674364","article-title":"New procedure for assessing sequential manual lifting jobs using the revised NIOSH lifting equation","volume":"50","author":"Waters","year":"2007","journal-title":"Ergonomics"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Faber, G.S., Kingma, I., Chang, C.C., Dennerlein, J.T., and van Die\u00ebn, J.H. (2020). Validation of a wearable system for 3D ambulatory L5\/S1 moment assessment during manual lifting using instrumented shoes and an inertial sensor suit. J. Biomech., 109671.","DOI":"10.1016\/j.jbiomech.2020.109671"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Conforti, I., Mileti, I., Panariello, D., Caporaso, T., Grazioso, S., Del Prete, Z., Lanzotti, A., Di Gironimo, G., and Palermo, E. (2020, January 3\u20135). Validation of a novel wearable solution for measuring L5\/S1 load during manual material handling tasks. Proceedings of the 2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT, Rome, Italy.","DOI":"10.1109\/MetroInd4.0IoT48571.2020.9138259"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1007\/s10439-019-02409-8","article-title":"Estimation of Spinal Loading During Manual Materials Handling Using Inertial Motion Capture","volume":"48","author":"Larsen","year":"2020","journal-title":"Ann. Biomed. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.jbiomech.2017.10.001","article-title":"Estimating the L5S1 flexion\/extension moment in symmetrical lifting using a simplified ambulatory measurement system","volume":"70","author":"Koopman","year":"2018","journal-title":"J. Biomech."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"102690","DOI":"10.1016\/j.humov.2020.102690","article-title":"Combining wearable sensor signals, machine learning and biomechanics to estimate tibial bone force and damage during running","volume":"74","author":"Matijevich","year":"2020","journal-title":"Hum. Mov. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, J., Wang, P., and Huang, H.J. (2020). Dry Epidermal Electrodes Can Provide Long-Term High Fidelity Electromyography for Limited Dynamic Lower Limb Movements. Sensors, 20.","DOI":"10.3390\/s20174848"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Colombini, D., Occhipinti, E., Alvarez-Casado, E., and Waters, T.R. (2012). Manual Lifting: A Guide to the Study of Simple and Complex Lifting Tasks, CRC Press.","DOI":"10.1201\/b12276"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1016\/S0268-0033(98)00020-5","article-title":"A comparison of peak vs. cumulative physical work exposure risk factors for the reporting of low back pain in the automotive industry","volume":"13","author":"Norman","year":"1998","journal-title":"Clin. Biomech."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic gradient boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Natekin, A., and Knoll, A. (2013). Gradient boosting machines, a tutorial. Front. Neurorobot., 7.","DOI":"10.3389\/fnbot.2013.00021"},{"key":"ref_19","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems 30, Available online: http:\/\/papers.nips.cc\/paper\/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree.pdf."},{"key":"ref_20","unstructured":"Steel, R., and Torrie, J. (1960). Principles and Procedures of Statistics, McGraw-Hill."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Matijevich, E.S., Branscombe, L.M., Scott, L.R., and Zelik, K.E. (2019). Ground reaction force metrics are not strongly correlated with tibial bone load when running across speeds and slopes: Implications for science, sport and wearable tech. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0210000"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1097\/00007632-198603000-00011","article-title":"Moment Arm Lengths of Trunk Muscles to the Lumbosacral Joint Obtained In Vivo with Computed Tomography","volume":"11","author":"Nemeth","year":"1986","journal-title":"Spine"},{"key":"ref_24","unstructured":"(2019). Guryanov, Histogram-Based Algorithm for Building Gradient Boosting Ensembles of Piecewise Linear Decision Trees. Analysis of Images, Social Networks and Texts, Springer International Publishing."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1016\/S0021-9290(97)00010-9","article-title":"Spine loading during trunk lateral bending motions","volume":"30","author":"Marras","year":"1997","journal-title":"J. Biomech."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/2\/340\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:07:43Z","timestamp":1760159263000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/2\/340"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,6]]},"references-count":25,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["s21020340"],"URL":"https:\/\/doi.org\/10.3390\/s21020340","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,6]]}}}