Dataset Information

A Promising Wearable Solution for the Practical and Accurate Monitoring of Low Back Loading in Manual Material Handling.

ABSTRACT: (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.

SUBMITTER: Matijevich ES

PROVIDER: S-EPMC7825414 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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A Promising Wearable Solution for the Practical and Accurate Monitoring of Low Back Loading in Manual Material Handling.

Matijevich Emily S ES Volgyesi Peter P Zelik Karl E KE

Sensors (Basel, Switzerland) 20210106 2

(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, contin ...[more]

PMID: 33419101

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Project description:BackgroundIn Germany and other European countries, many occupations still involve manual handling of loads (MHL), an activity that puts the musculoskeletal system at risk of low back pain (LBP). This study aims to describe the current prevalence of MHL in different occupational groups stratified by gender in Germany, the association between MHL and LBP and the adjusted prevalence of LBP in different respond-categories of MHL.MethodsData was collected in telephone interviews conducted as part of the 2018 BIBB/BAuA Employment Survey, which covers work-related topics like working conditions, education, health status and job satisfaction. The analyses were limited to full-time workers (> 35 h/week) aged between 15 and 67. The frequency of MHL was analysed descriptively. BLOSSFELD classification was used to group the participants in occupational categories. The analysis of the association between MHL and the prevalence of LBP over the last 12 months was based on robust log-linear Poisson regression that results in prevalence ratios (PR). The main regression model was adjusted for gender, age, working hours, and working conditions. Adjusted estimates for the prevalence of LBP were calculated based on regression analysis.ResultsThe sample consists of n = 14,331 participants (men: n = 8828, 61.6%; women: n = 5503, 38.4%; median age 49 years). Of these, 52.8% say they were exposed to MHL at work. MHL is most common in agricultural occupations, skilled and unskilled occupations. In the regression model, participants who said they were "often" exposed to MHL reported more frequently LBP than those participants who said they were "never" exposed to MHL. The PR as estimate for the association is 1.41 (95%CI [1.32; 1.49]). Postestimation of the prevalence of LBP began with 47.3% (95%CI [43.8%; 51.1%]) for participants who said they were "never" exposed to MHL and rose to 66.5% (95%CI [62.4%; 71.0%]) for participants who indicated they were "often" exposed to MHL.ConclusionsThe 2018 BIBB/BAuA Employment Survey emphasizes that MHL is still common in the German workforce and shows a significant association to LBP. Prevention policies for avoiding MHL remain vital.

Project description:Efficacy of classical manual material handling (MMH) training interventions on back pain prevention at the workplace has been called into question. The way that observation (self-observation or hetero-observation) is used in other areas to create feedback addressed to modify motor activities can justify innovative components for these interventions. However, their implementation and evaluation cannot be done without tackling the methodological challenge of developing a reliable observational instrument to measure manual handling practice during the training process. The aims of this study were: (1) justify and develop an hetero-observation (H-O) instrument to assess changes in the worker behavioral patterns with a level of analysis convenient to derive a parallel version for the systematic self-observation (S-O) during training on MMH; (2) provide evidence on the inter-rater reliability of the H-O instrument; (3) provide evidence on the usability of the S-O instrument and its perceived usefulness; and (4) provide evidence on the benefits that can be derived with the use of the H-O instrument to create feedback based on T-pattern and polar coordinate analysis. A mixed method approach mainly grounded on systematic observation was used. A convenience sample composed by blue-collar workers participated in the study. Based on literature review and expert opinion, the H-O instrument proposed was composed by six dimensions (feet, knee joints, back, elbow joints, load position, and interaction between back tilt and displacement) plus a structural dimension which defined MMH phases. The inter-rater reliability of this instrument was almost perfect for all dimensions using a tolerance level of 2 s (the range of time-unit kappa was from 0.93 to 0.97 and the range of event-based kappa was from 0.82 to 0.9). The usability and usefulness of the S-O instrument was highly valued by workers. Regarding the way to use hetero-observations to create feedback, the paper shows the great potential of T-pattern and polar coordinate analysis. The observational instruments developed combined with these techniques make it possible to characterize the body positions adopted during manual handling performance, and this is crucial to create feedback on performance instead of only feedback on results.

Dataset Information

A Promising Wearable Solution for the Practical and Accurate Monitoring of Low Back Loading in Manual Material Handling.

Publications

A Promising Wearable Solution for the Practical and Accurate Monitoring of Low Back Loading in Manual Material Handling.

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