Comparing the results of five lifting analysis tools

The ACGIH TLV for lifting: Estimated TLVs for torso asymmetry beyond 30 degrees

BACKGROUND

The American Conference of Governmental Industrial Hygienists (ACGIH) Threshold Limit Values (TLVs) for Lifting is a manual material handling (MMH) assessment method to identify weight limits that nearly all workers may be exposed to without developing work-related low back disorders (LBD). However, this assessment method only applies to lifting with the torso within 30° asymmetry of the sagittal plane.

OBJECTIVE

Estimate TLV weight limits while lifting with torso asymmetry greater than 30° beyond the sagittal plane.

METHODS

Lifting tasks were performed from various horizontal and vertical locations, at torso asymmetry angles of 0°, 15°, 30°, 45°, 60°, 75° and 90°, using ACGIH identified TLVs. Validated MMH assessment methods (NIOSH Lifting Equation, Ohio State University LBD Risk Model) were utilized to estimate TLVs at torso asymmetries greater than 30°.

RESULTS

The current ACGIH TLVs resulted in low- to moderate-risk risk levels for torso asymmetries from 0° to 30°, and the risk incrementally increased as torso asymmetry increased to 90°. With the intention to keep the risk levels to that found at 30° torso asymmetry, lower TLV weight limits in the vertical and horizontal zones investigated were estimated for torso asymmetries from 45° to 90°. The resulting adjusted TLVs were consistent with weight limits identified for similar lifting conditions from other assessment methods that account for torso asymmetry.

CONCLUSIONS

This research found current ACGIH-defined TLVs possess less than high-risk for LBD, and provided guidance to practitioners for reduced TLVs when torso asymmetry is greater than 30° from the sagittal plane.

Comparison of ACGIH lifting threshold limit values to validated low back disorder lifting assessment methods outcomes

BACKGROUND

Work-related low back pain (LBP) increases the workforce disability and healthcare costs. This study evaluated the LBD risk level associated with handling the ACGIH TLVs in lifting tasks corresponding to various horizontal and vertical zones.

OBJECTIVE

The aim of this study was to compare the low-risk ACGIH TLV to risk outcomes from various validated lifting assessment methods, including the OSU LBD Risk Model, NIOSH Lifting Equation, and LiFFT.

METHODS

Twenty-four subjects were recruited for this study to perform various lifting conditions. The various ergonomic assessment methods were then used to obtain the risk assessment outcomes.

RESULTS

The selected assessment methods showed that the ACGIH-defined TLVs are associated with less than high-risk for LBD for all the assessed tasks. The findings showed a moderate agreement (Kendall's W = 0.477) among the various assessment methods risk outcomes. The highest correlation (ρ= 0.886) was observed between the NIOSH Lifting Equation and LiFFT methods risk assessment outcomes.

CONCLUSION

The findings showed that ACGIH-defined TLVs possesses less than high-risk for LBD. The outcomes of the selected ergonomic assessment methods moderately agree to each other.

Classification of body postures using smart workwear

Background

Despite advancing automation, employees in many industrial and service occupations still have to perform physically intensive work that may have negative effects on the health of the musculoskeletal system. For targeted preventive measures, precise knowledge of the work postures and movements performed is necessary.

Methods

Prototype smart work clothes equipped with 15 inertial sensors were used to record reference body postures of 20 subjects. These reference postures were used to create a software-based posture classifier according to the Ovako Working Posture Analysing System (OWAS) by means of an evolutionary training algorithm.

Results

A total of 111,275 posture shots were recorded and used for training the classifier. The results show that smart workwear, with the help of evolutionary trained software classifiers, is in principle capable of detecting harmful postures of its wearer. The detection rate of the evolutionary trained classifier (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\bar{a}_{ccr} = 0.35$$\end{document}a¯ccr=0.35 for the postures of the back, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\bar{a}_{ccr} = 0.64$$\end{document}a¯ccr=0.64 for the arms, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\bar{a}_{ccr} = 0.25$$\end{document}a¯ccr=0.25 for the legs) outperforms that of a TensorFlow trained classifying neural network.

Conclusions

In principle, smart workwear – as prototypically shown in this paper – can be a helpful tool for assessing an individual’s risk for work-related musculoskeletal disorders. Numerous potential sources of error have been identified that can affect the detection accuracy of software classifiers required for this purpose.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-022-05821-9.

Manual material handling in the supermarket sector. Part 2: Knee, spine and shoulder joint reaction forces

Manual material handling is common in supermarkets and may be a contributing factor to the high prevalence of work-related musculoskeletal disorders, particularly to the lower back. This cross-sectional study applied state-of-the-art musculoskeletal models driven by kinematic data obtained in two supermarkets to estimate joint reaction forces in the knees, shoulders and lumbar spine under dynamic lifting conditions. Based on 1479 lifts from 15 workers, 8 tasks for which the compression or shear forces in the L5-S1 joint exceeded well-known biomechanical tolerance limits were identified. High shoulder forces were associated with lifting relatively heavy merchandise to high shelves, while the weight of the handled merchandise was the main predictor of high knee forces. The study addressed well-known limitations associated with traditional lifting analysis tools and was the first to present a detailed analysis of the biomechanical loads during manual material handling tasks in the supermarket sector based on field measurements.

Impact of the seated height to stature ratio on torso segment parameters

Ergonomic modelling programmes such as the Three Dimensional Static Strength Prediction Programme (3DSSPP) are valuable tools for assessing strength capabilities and risk assessment. These tools rely on accurate, representative inputs in the form of body segment parameters (BSPs). The upcoming version of 3DSSPP will employ BSPs for the torso, split into thoracic, lumbar and pelvis segments in order to more precisely determine spinal forces and injury risks. This study determines the impacts of age, body mass index and the estimated seated height to stature ratio (SHS) on these full and split torso parameters in a sample of working American adults. The results show that all of these metrics have significant relationships with the BSPs of interest, indicating that they must be accounted for when determining these parameters. A sensitivity analysis performed in 3DSSPP demonstrates that varying the parameters inputs will have large effects on L5/S1 compression force calculations. Practitioner Summary: Current anthropometric data sets for ergonomic applications do not account for wide ranges of age, BMI and overall body shape on segment parameter calculations. This study quantifies the associations of age, BMI and the seated height to stature ratio on full and split torso segment parameters. Abbreviations: 3DSSPP: Three-Dimensional Static Strength Prediction Program; BMI: body mass index; BSP: body segment parameter; BW: body weight; COM: centre of mass; DXA: dual energy x-ray absorptiometry; R_G: radius of gyration; SHS: seated height to stature ratio; SL: segment length.

The incorporation of virtual ergonomics to improve the occupational safety condition in a factory

Proper manual material handling (MMH) is the important step leading to the occupational safety of the workers on the shop floor as well as the productivity improvement of the manufacturing process. The objectives of this study are the application of different risk assessment methods, the redesign of the workstation to reduce the occupational risk and the utilization of software package to validate the proposed interventions. As a result, an assembly line of a product is selected as the case study to validate the proposed agenda. Afterwards, four lifting assessment methods, i.e. NIOSH lift equation, Snook Psychophysical Table, OSU Lift guidelines and ACGIH/TLV, are used to assess the hazard risk in the assembly line. After these methods are performed, the results are introduced to recommend the newly designed working conditions, i.e. postures, movements and the barriers. To validate the improved design, new configurations are simulated by the virtual ergonomic program and the ergonomic analysis is performed. The important results, e.g. low back compression and percent of population capable, are calculated by the software to determine the appropriate values which are used as the guidelines for a safe working condition. Moreover, the manufacturing process is also simulated to improve that the ergonomic redesign of the shop floor environment and another consequence of the implementation leads to the significant increase of the productivity.

Job Hazard Analyses for Musculoskeletal Disorder Risk Factors in Pressing Operations of Dry-cleaning Establishments

Job hazard analyses were conducted to assess exposure to musculoskeletal disorder (MSD) risk factors in seven workers of three dry-cleaning establishments. In accordance with the Washington State Ergonomics Rule, the analyses were performed in two separate steps: (1) observation and checklist approaches were made to identify a "caution zone job" in the seven workers' pressing operations across the three shops; and (2) detailed posture and motion analyses were undertaken to determine a "MSD hazard" in one worker's operation using a video technique. One "caution zone job" was identified and it was the pressing operation job in which five physical risk factors were found in the pressing operations. The detailed analyses confirmed that one "MSD hazard", i.e., awkward posture in shoulders, was prevalent in the pressing operations of the three dry-cleaning facilities. It would be desirable to reduce MSD risk factors including awkward shoulder posture in the dry-cleaning industry.

Comparative evaluation of six quantitative lifting tools to estimate spine loads during static activities

Different lifting analysis tools are commonly used to assess spinal loads and risk of injury. Distinct musculoskeletal models with various degrees of accuracy are employed in these tools affecting thus their relative accuracy in practical applications. The present study aims to compare predictions of six tools (HCBCF, LSBM, 3DSSPP, AnyBody, simple polynomial, and regression models) for the L4-L5 and L5-S1 compression and shear loads in twenty-six static activities with and without hand load. Significantly different spinal loads but relatively similar patterns for the compression (R(2) > 0.87) were computed. Regression models and AnyBody predicted intradiscal pressures in closer agreement with available in vivo measurements (RMSE ≈ 0.12 MPa). Due to the differences in predicted spinal loads, the estimated risk of injury alters depending on the tool used. Each tool is evaluated to identify its shortcomings and preferred application domains.

Comparison of concepts in easy-to-use methods for MSD risk assessment

This article presents a comparative analysis of easy-to-use methods for assessing musculoskeletal load and the risk for developing musculoskeletal disorders. In all such methods, assessment of load consists in defining input data, the procedure and the system of assessment. This article shows what assessment steps the methods have in common; it also shows how those methods differ in each step. In addition, the methods are grouped according to their characteristic features. The conclusion is that the concepts of assessing risk in different methods can be used to develop solutions leading to a comprehensive method appropriate for all work tasks and all parts of the body. However, studies are necessary to verify the accepted premises and to introduce some standardization that would make consolidation possible.

The NIOSH lifting equation and low-back pain, Part 1: Association with low-back pain in the backworks prospective cohort study

OBJECTIVE

The aim of this study was to evaluate relationships between the revised NIOSH lifting equation (RNLE) and risk of low-back pain (LBP).

BACKGROUND

The RNLE is commonly used to quantify job physical stressors to the low back from lifting and/or lowering of loads. There is no prospective study on the relationship between RNLE and LBP that includes accounting for relevant covariates.

METHOD

A cohort of 258 incident-eligible workers from 30 diverse facilities was followed for up to 4.5 years. Job physical exposures were individually measured. Worker demographics, medical history, psychosocial factors, hobbies, and current LBP were obtained at baseline. The cohort was followed monthly to ascertain development of LBP and quarterly to determine changes in job physical exposure. The relationship between LBP and peak lifting index (PLI) and peak composite lifting index (PCLI) were tested in multivariate models using proportional hazards regression.

RESULTS

Point and lifetime prevalences of LBP at baseline were 7.1% and 75.1%, respectively. During follow-up, there were 123 incident LBP cases. Factors predicting development of LBP included job physical exposure (PLI and PCLI), history of LBP, psychosocial factors, and housework. In adjusted models, risk (hazard ratio [HR]) increased per-unit increase in PLI and PCLI (p = .05 and .02; maximum HR = 4.3 and 4.2, respectively). PLI suggested a continuous increase in risk with an increase in PLI, whereas the PCLI showed elevated, but somewhat reduced, risk at higher exposures.

CONCLUSION

Job physical stressors are associated with increased risk of LBP. Data suggest that the PLI and PCLI are useful metrics for estimating exposure to job physical stressors.

Comparison of risk assessment procedures used in OCRA and ULRA methods

The aim of this study was to analyse the convergence of two methods by comparing exposure and the assessed risk of developing musculoskeletal disorders at 18 repetitive task workstations. The already established occupational repetitive actions (OCRA) and the recently developed upper limb risk assessment (ULRA) produce correlated results (R = 0.84, p = 0.0001). A discussion of the factors that influence the values of the OCRA index and ULRA's repetitive task indicator shows that both similarities and differences in the results produced by the two methods can arise from the concepts that underlie them. The assessment procedure and mathematical calculations that the basic parameters are subjected to are crucial to the results of risk assessment. The way the basic parameters are defined influences the assessment of exposure and risk assessment to a lesser degree. The analysis also proved that not always do great differences in load indicator values result in differences in risk zones.

PRACTITIONER SUMMARY

We focused on comparing methods that, even though based on different concepts, serve the same purpose. The results proved that different methods with different assumptions can produce similar assessment of upper limb load; sharp criteria in risk assessment are not the best solution.

A neural network model for predicting postures during non-repetitive manual materials handling tasks

Posture prediction can be useful in facilitating the design and evaluation processes for manual materials handling tasks. This study evaluates the ability of artificial neural network models to predict initial and final lifting postures in 2-D and 3-D scenarios. Descriptors for the participant and condition of interest were input to the models; outputs consisted of posture-defining joint angles. Models were trained with subsets of an existing posture database before predictions were generated. Trained models predictions were then evaluated using the remaining data, which included conditions not presented during training. Prediction errors were consistent across these data subsets, suggesting the models generalised well to novel conditions. The models generally predicted whole-body postures with per-joint errors in the 5 degrees -20 degrees range, though some errors were larger, particularly for 3-D conditions. These models provided reasonably accurate predictions, even outperforming some computational approaches previously proposed for similar purposes. Suggestions for future refinement of such models are presented. The models in this investigation provide a means to predict initial and final postures in commonly occurring manual materials handling tasks. In addition, the model structures provide information about potential lifting strategies that may be used by individuals with particular anthropometry or strength characteristics.