Manual Lifting Tasks Research Articles

A new approach for ergonomic risk assessment integrating KEMIRA, best–worst and MCDM methods

In this study, a new three-phase ergonomic risk assessment approach was proposed for manual lifting tasks to determine which worker has the highest ergonomic risk level considering two criteria sets as lifting-related criteria and human-related criteria. In the first phase, Modified Kemeny Median Indicator Ranks Accordance (KEMIRA-M) and a novel two-dimensional best–worst method (BWM) integration were proposed for weighting ergonomic risk criteria in two sets. In this way, weighting procedure of KEMIRA-M was advanced by the proposed two-dimensional BWM in a consistent manner and subjectivity in determining the best and the worst criteria in traditional BMW was prevented by using KEMIRA-M. Thus, the weaknesses of both methods have been developed. In the second phase, the rankings of workers were determined via utilizing multi-objective optimization on the basis of simple ratio analysis, multi-objective optimization by ratio analysis (MOORA) ratio, MOORA reference point and complex proportional assessment to see how worker rankings differ despite using the same advanced weighting approach based on KEMIRA-M and two-dimensional BWM integration. Finally, to aggregate these different ranking results, technique of precise order preference was applied. In this way, different viewpoints of each ranking approach can be reflected on a single worker’s priority. The applicability of the proposed ergonomic risk assessment approach was demonstrated with a real application in tube manufacturing.

Comparative assessment of manual load lifting tasks by three methods: KIM-LHC, WISHA and Snook tables: A case study in printing industry

Introduction: today, musculoskeletal disorders are considered as a serious threat for workers, and one of the main activities, which lead to these problems, is lifting. Moreover, workers in printing industry are widely involved in inappropriate postures and lifting loads. The aim of this study was to compare the results of assessment of lifting tasks with KIM-LHC, WISHA and Snook tables in printing industry. Materials and Methods: In this cross-sectional study, a total number of 58 employees (64 tasks in total) from a printing industry staff in Tehran were evaluated with three observational methods of KIM-LHC, WISHA and Snook tables, and the final scores and necessary action levels were determined for them. Furthermore, statistical data were analyzed by SPSS (ver.22) software using Chi-square and Fisher exact tests. Results: Results showed that the prevalence of lower limbs musculoskeletal discomforts was high. Comparing the results of load lifting assessment with WISHA and KIM-LHC methods showed that there was a significant relationship between these two methods (p=0.005). Moreover, there was a significant relationship between the results of assessment through the WISHA and Snook tables methods (p=0.006), and finally the comparison of load lifting assessment results with KIM-LHC and Snook tables showed a significant relationship between these two methods (p=0.046). Conclusion: According to the results of this study, there was a high correlation between the results of these three methods, and they can be used as an alternative in printing industry for assessment of the load lifting task.

Influence of creep deformation on sub-regional lumbar spine motion during manual lifting

Prolonged or repetitive spine flexion induces creep deformation of posterior spine tissues allowing for increased intervertebral motion beyond ‘normal’ limits, which may influence sub-regional (intersegmental) spine motion during subsequent manual lifting tasks. Using spine skin-surface kinematics, intersegmental lumbar spine motion was recorded over 20 minutes of prolonged static spine flexion and a subsequent manual lifting task (2 lifts every 3 minutes, 30 minutes total) in 14 participants. Results demonstrated that mid to lower lumbar intersegmental levels (i.e. L2/L3 to L4/L5) experienced the greatest overall creep deformation and range of motion during both prolonged flexion and manual lifting; however, overall range of motion during manual lifting was unaffected. Additionally, creep deformation did not completely recover within 30 minutes. Future work should continue to investigate the influence of this residual creep, as well as how overall creep deformation impacts spine neuromuscular control and stability, and ultimately the development of low back disorders. Practitioner summary: Mid to lower lumbar spine levels (i.e. L2/L3 to L4/L5) experienced the greatest creep deformation and range of motion during both prolonged flexion and manual lifting. Repeated lifting following prolonged flexion may limit creep recovery; however, overall lifting kinematic motion remained unchanged.

Lifting Activity Assessment Using Kinematic Features and Neural Networks

Work-related low-back disorders (WLBDs) can be caused by manual lifting tasks. Wearable devices used to monitor these tasks can be one possible way to assess the main risk factors for WLBDs. This study aims at analyzing the sensitivity of kinematic data to the risk level changes, and to define an instrument-based tool for risk classification by using kinematic data and artificial neural networks (ANNs). Twenty workers performed lifting tasks, designed by following the rules of the revised NIOSH lifting equation, with an increasing lifting index (LI). From the acquired kinematic data, we computed smoothness parameters together with kinetic, potential and mechanical energy. We used ANNs for mapping different set of features on LI levels to obtain an automatic risk estimation during these tasks. The results show that most of the calculated kinematic indexes are significantly affected by changes in LI and that all the lifting condition pairs can be correctly distinguished. Furthermore, using specific set of features, different topologies of ANNs can lead to a reliable classification of the biomechanical risk related to lifting tasks. In particular, the training sets and numbers of neurons in each hidden layer influence the ANNs performance, which is instead independent from the numbers of hidden layers. Reliable biomechanical risk estimation can be obtained by using training sets combining body and load kinematic features.

Development of a NIOSH based software tool for musculoskeletal disorders

Musculoskeletal disorders amongst workers performing manual lifting tasks have become a major challenge now a days. Such problems hamper productivity of any concern to a greater extent. Industrial experts and researchers have been devising ways and means to reduce such disorders and the National Institute of Occupational Safety and Health (NIOSH) agency lifting equation is one amongst such tools. NIOSH lifting equation has significantly enhanced the safety of the workers involved in manual lifting tasks. With this equation, a prior indication of musculoskeletal disorders can be obtained from the workers anthropometric details. However, till date there is no such tool available with which we can have recommendations to eliminate/reduce such disorders. In this paper an expert system on the basis of NIOSH equation has been developed to deal with the musculoskeletal problems amongst the workers involved in manual lifting tasks. The expert system is basically a computer programme developed to facilitate the use of NIOSH lifting equation. On the basis of lifting parameters, this equation computes recommended weight limit (RWL) and lifting index (LI). These outputs have been further analyzed by the programe to check existing working conditions against occupational hazards, and suggest recommendations for the safe working conditions.

Valutazione dell’attività di sollevamento utilizzando le caratteristiche estratte da sensori indossabili

The manual lifting tasks, which occur in the vast majority of workplaces can cause work-related low-back disorders (WLBDs), that are the most common musculoskeletal problems. Recently, to identify the relationship betweenWLBDs and risk factors, wearable monitoring devices-based biomechanical risk assessments have been proposed.The purpose of this study is to characterize from a biomechanical point of view, using wearable devices other liftingconditions to define, in the future, a risk classification tool that can be applied in each lifting condition. To do this,we recorded electromyographic data of workers during lifting tasks designed to have a growing lifting index (LI=1,2 and 3) by means of revised NIOSH lifting equation. Each lifting condition (LI=1 or LI=2 or LI=3) was obtainedin three different ways modifying the asymmetry angle. We acquired data by using Wi-Fi transmission surface electromyograph (sEMG). From the sEMG signals, analyses of time and frequency domains were performed within thelifitng cycle to extract maximum value, the average rectified value, the mean frequency defined as the gravity centerfrequency of the power spectrum of the signal. The results show that these sEMG data grew significantly with the LIand that all the lifting condition pairs are discriminated. We will test whether machine-learning techniques used formapping features extracted from wearable sensors on LI levels can improve the biomechanical risk estimation duringthese tasks. These findings suggest the use of kinematic and sEMG features to assess biomechanical risk associatedwith work activities can be integrated with methods already used for biomechanical risk assessment.

Valutazione dell’attività di sollevamento utilizzando le caratteristiche estratte da sensori indossabili

The manual lifting tasks, which occur in the vast majority of workplaces can cause work-related low-back disorders (WLBDs), that are the most common musculoskeletal problems. Recently, to identify the relationship between WLBDs and risk factors, wearable monitoring devices-based biomechanical risk assessments have been proposed. The purpose of this study is to characterize from a biomechanical point of view, using wearable devices other lifting conditions to define, in the future, a risk classification tool that can be applied in each lifting condition. To do this, we recorded electromyographic data of workers during lifting tasks designed to have a growing lifting index (LI=1,2 and 3) by means of revised NIOSH lifting equation. Each lifting condition (LI=1 or LI=2 or LI=3) was obtained in three different ways modifying the asymmetry angle. We acquired data by using Wi-Fi transmission surface electromyograph (sEMG). From the sEMG signals, analyses of time and frequency domains were performed within the lifitng cycle to extract maximum value, the average rectified value, the mean frequency defined as the gravity center frequency of the power spectrum of the signal. The results show that these sEMG data grew significantly with the LI and that all the lifting condition pairs are discriminated. We will test whether machine-learning techniques used for mapping features extracted from wearable sensors on LI levels can improve the biomechanical risk estimation during these tasks. These findings suggest the use of kinematic and sEMG features to assess biomechanical risk associate with work activities can be integrated with methods already used for biomechanical risk assessment.

Understanding Anthropometric Characteristics Associated With Performance in Manual Lifting Tasks.

Beck, B, Middleton, KJ, Billing, DC, Caldwell, JN, and Carstairs, GL. Understanding anthropometric characteristics associated with performance in manual lifting tasks. J Strength Cond Res 33(3): 755-761, 2019-Manual lifting is an essential military job task and is commonly linked to occupational injury. Methods to reduce injury risk focus on ensuring that employees have the requisite physical capacity to safely conduct critical job tasks. The aim of this study was to investigate which anthropometric characteristics are associated with lifting performance to inform targeted training programs for job-critical lifting tasks. Sixty-three (42 men and 21 women) participants conducted 3 maximal lifts to a platform (pack lift to 1.5 m, box lift to 1.3 m and box lift to 1.5 m). A dual-energy x-ray absorptiometry scan was used to quantify anthropometric characteristics (body region-specific lean mass and fat mass). Although anthropometric measures were strongly associated with each other, multivariable linear regression revealed that a significant proportion of the total variation in lifting performance in each of the 3 tasks was explained by upper-arm lean mass (pack lift: β = 5.42, p < 0.001; box lift 1.3 m: β = 5.64, p < 0.001; box lift 1.5 m: β = 7.00, p < 0.001). Leg lean mass also significantly contributed to the variation of pack lift performance (β = 0.93, p = 0.01). When controlling for key anthropometric characteristics in these 3 tasks, separate analyses showed no significant effect of sex or stature on lift performance. These results suggest that the perceived limitations of stature and sex may be overcome by targeted training programs to improve specific physical characteristics associated with lifting performance.

Estimation of exerted force in manual lifting task using wearable device

Estimation of exerted force in manual lifting task using wearable device

Riesgo lumbar en la manipulación de barras de hierro en una empresa siderúrgica

Sr. Editor. El dolor lumbar por manipulación de carga constituye un problema de salud ocupacional que trae como consecuencia la reducción de la movilidad, y discapacidad1,2,3. Así, le comunicamos acerca de un estudio descriptivo y transversal que tuvo como objetivo determinar el nivel de riesgo lumbar en el conteo de barras de hierro, que realizamos el año 2015 en el almacén de la ciudad de Pisco de la empresa siderúrgica Aceros Arequipa. Participaron 40 trabajadores que tuvieron como actividad diaria el conteo de barras de hierro en posición parada y flexión, levantando barras de 30 kg. Se utilizó el método INSHT (Instituto Nacional de Seguridad e Higiene en el Trabajo de España)1 en la determinación del nivel de riesgo lumbar con los datos: peso de la carga, postura, desplazamiento vertical, giro del tronco, agarre de la carga, frecuencia, distancia transportada, según la ecuación de NIOSH que permite calcular un índice de levantamiento que proprciona el nivel de riesgo asociado a una tarea de levantamiento manual (cociente entre peso de carga levantada y límite de peso recomendado).

Comparisons with subjective and objective indexes of lifting risk among different combinations of lifting weight and frequency

It is evident that the lifting risk increases with both the lifting weight and frequency. However, little is known whether or not different combinations of lifting weight and frequency cause different risks even lifting the same total weight over a period of time. Therefore, the purpose of this study was to investigate the effects of lifting weight and frequency combinations on the lifting risk in the same amount of weight lifted over a period of time. Oxygen consumption, heart rate, and NIOSH’s (US National Institute for Occupational Safety and Health) Lifting Index (LI) were measured and considered as objective risk indexes. The rating of perceived exertion (RPE) was collected as subjective risk index. Sixteen young adults participated in the experiment. Each participant performed four lifting trials with different combinations of lifting weight and frequency, i.e. 12 kg & 2 lift/min, 8 kg & 3 lift/min, 6 kg & 4 lift/min, and 4 kg & 6 lift/min. Each lifting trial last 16 min. Each participant’s oxygen consumption and heart rate were measured during the lifting trial. After each trial, the participant’s RPE and LI were collected. The results indicated the combination of weight and frequency had a significant effect on RPE and LI, but had little effect on oxygen consumption and heart rate. The larger lifting weight accompanied with lower frequency (12 kg & 2 lift/min) had a significant greater RPE and LI than the other three lifting trials. This study provided useful information to determine proper risk predictors for manual lifting tasks with different combinations of object weight and lifting frequency.

Estimation of physical workload of the low-back based on exposure variation analysis during a full working day among male blue-collar workers. Cross-sectional workplace study

This study aims to quantify physical workload of the low-back using exposure variation analysis (EVA) during a full working day among blue-collar workers with manual lifting tasks. One hundred and ten male employees (39 warehouse workers, 27 operators, 24 postal workers and 20 slaughterhouse workers) with manual lifting tasks from 12 workplaces participated. The workers performed standardized box lifts using 5, 10, 20 and 30 kg before and after a working day. Muscular activity of the low-back was measured throughout the working day using surface electromyography (sEMG). Corresponding sEMG-values for 0–30 kg lifts were identified using linear regression. EVA at exposure levels corresponding to “lifting periods” of [1-5, 5-10, 10-20, 20-30 and >30] kg in time intervals [0–0.5, 0.5–1, 1-2, 2-5, 5-10, >10] sec was computed. Back inclination was measured using tri-axial accelerometers. Compared to the other job groups, the operators’ low-back muscles were exposed to more short duration “lifting periods” with varying loads and more frequent medium duration high load “lifting periods”, respectively. The operators also worked more with their back inclined (>30°, >60°, and >90°) than the remaining job groups. Nonetheless, more than 41% of the workers performed heavy “lifting periods” that exceeded Danish lifting guidelines. This EVA demonstrates that almost half of the blue-collar workers were exposed to heavy low-back loading which puts them at risk of developing musculoskeletal disorders and low-back injury. Operators are, in particular, exposed to more short duration and medium duration “lifting periods” with varying load compared to warehouse-, postal- and slaughterhouse workers.

Hand Grip Strength and Gender: Allometric Normalization in Older Adults and Implications for the NIOSH Lifting Equation.

BackgroundMany countries are experiencing an aging workforce with women workers making up a growing proportion. Workplaces often require employees to complete lifting tasks that require the ability of the hand to grasp an implement (coupling). The National Institute for Occupational Safety and Health (NIOSH) has developed an equation for manual lifting tasks hoping to minimize the potential for a workplace back-injury related to a lifting task. The NIOSH lifting equation relies upon stress variables including a coupling factor. However, little is known regarding grip strength as related to the NIOSH lifting equation coupling factor. The purpose of this study was to investigate differences in grip strength due to gender in older adults.MethodsThe participant’s (68–88 years) maximal grip (MG) strength measures were collected for each hand with a hand grip dynamometer (kg). MG scores were converted to Newtons (N), normalized to body mass, and allometrically scaled. Measures of MG were than compared between genders with an independent t-test.ResultsThe hand grip measures of MG (kg) [male: 30.3 ± 5.6, female: 10.6 ± 3.3], MG (kg) / body mass (kg) [male: 0.35 ± 0.06, female: 0.16 ± 0.04], MG (N) / (body mass (kg))0.67 [male: 15.1 ± 2.5, female: 6.2 ± 1.7], and MG (N) / (body height (m))1.84 [male: 103.6 ± 18.6, female: 42.6 ± 10.6] were all significantly lower (p < 0.0001) for women than men. Regardless of how grip strength was reported, there is a strong difference in grip strength between genders in this sample. The NIOSH lifting equation does not account for varying grip strength due to aging and gender differences.ConclusionsIt is recommended that grip strength variability be accounted for in the coupling factor of the NIOSH lifting equation.

A framework for evaluating muscle activity during repetitive manual material handling in construction manufacturing

Workers in construction sites are exposed to highly labor-intensive tasks. Ergonomic principles, in addition to engineering considerations, should thus be included in the design of workstations in order to minimize the risk of injury. The objective of this paper is to propose a framework to assess muscle force and muscle fatigue development due to manual lifting tasks using surface electromyography (sEMG) and human body modelling. Muscle forces are calculated using the human body model and compared qualitatively to sEMG muscle activities. The results show that sEMG is capable of visualizing muscle activity. However, sEMG application in identifying muscle fatigue development is limited to bulkier and superficial muscle bundles in low fat areas. The proposed human body model, which is driven by kinematic motion capture data, predicts muscle forces during the entire task maneuver. The predicted muscle forces from the human body model are compared with sEMG data from corresponding muscles as well as data available in the literature. In future research, the developed model will be used to determine optimal task maneuvers that minimize muscle forces with the ultimate goal of preventing muscle injuries in workstations.

The Adequacy of NIOSH Equation to to Determine the Rate of Stresses Exerted on the Back Based on Compressive Force Estimation

Background: Manual handling may cause workers to deal with various risk factors. Workers performing such tasks repeatedly for a long time are more prone to bodily injuries and physical problems such as back pain. Objectives: The current study aimed to assess the compressive loads estimation and analysis of forces exerted on the workers’ back in manual load lifting tasks using the national institute for occupational safety and health (NIOSH) equation and compare them with the recommended weight limit (RWL). Methods: It was a cross-sectional study in a laboratory setting on 15 healthy male workers in 2015. The participants were required to randomly perform 25 tasks with four iterations. The lifting index (LI) was calculated by NIOSH equation in simple tasks and compressive loads on the low back (L4/L5) for each task were calculated using the 3DSSPP. Data were analyzed using SPSS version 16. Results: The results showed that the highest compressive force exerted on the back was equal to 4002 N and the lowest force 1425 N. Moreover, 76% of the tasks were reported to have the highest compressive force greater than the recommended limit; 72% of the tasks had an LI 1 - 3 and 28% of them had an LI less than 1. There was a weak direct correlation between compressive force and RWL. Conclusions: The NIOSH equation and compressive force estimation function were partially similar to identify high-risk tasks, however, by the compressive force estimation, biomechanical analysis of tasks can be done better since it is quantitative and can determine the exact amount of forces exerted on the back.

Panel Discussion In Honor of Dr Tom Waters The NIOSH Lifting Equation - Part I

The NIOSH Lifting Equation, specifically the 1991 Revised Lifting Equation or RLE (Waters et al., 1993) is used worldwide by occupational ergonomists, industrial engineers and safety specialists to assess and (re)design manual lifting tasks and industrial systems that involve either single or repetitive lifting. Since the initial publication of the RLE, there have been a number of published studies on its usability, effectiveness and validation. The purpose of this panel discussion will be to critically review these RLE-related studies and examine implications of applying the RLE in various occupational settings and working populations. The first of four presentations will summarize recent epidemiological studies that have quantified exposure-response relationships between the RLE and incidence of low-back pain-related care and medication use issues, and discuss the RLE implications for job surveillance, intervention, and design approaches. The second presentation discusses practical relevance of the RLE and issues surrounding its sensitivity and specificity in correctly identifying hazardous lifting tasks. The third presentation will critically examine the conservativeness of the recommended weight limit derived from RLE by comparing the output of the RLE with the output from the specific biomechanical, psychophysical and physiological criteria utilized in its initial development of the lifting equation. Although application of the RLE has exponentially grown since its inception, the final presentation explores its feasibility in applying it to a non-US work force. The primary audience for this session will be occupational ergonomists who utilize the NIOSH equation for job assessment and (re)design, although the discussion should be of interest to researchers as well. The overall goal of this Part 1 symposium is to provide RLE users with some practical suggestions and recommendations.

Panel Discussion In Honor of Dr Tom Waters The NIOSH Lifting Equation - Part II

With the introduction of the NIOSH Lifting Equation, specifically after the publication of the Revised Lifting Equation (RLE) (Waters et al., 1993), occupational health and safety professionals across the world have successfully utilized the RLE to evaluate the risks associated with lifting and lowering tasks in the workplace. Although the RLE takes into consideration of various job task variables to determine recommended weight limits for a variety of task combinations, a number of articles and peer reviewed publications have appeared with a notion of either extending or modifying the RLE to address manual handling situations that the original equation was not able to assess comprehensively. The purpose of this panel discussion will be to provide an overview to ergonomics practitioners and researchers of these extensions and beyond, thus exploiting the full potential of this lifting equation. The first presentation will discuss the Variable Lift Index (VLI) for highly variable manual lifting tasks, whereas the second presentation describes the Sequential Lift Index (SLI) in assessing a sequence of lifting tasks that workers perform while they rotate to different tasks over a workday. In addition to the inherent variability in lifting tasks assumed by the RLE, we often encounter materials handling scenarios associated with one-handed lifting, team lifting, lifting of people (e.g., patient handling), lifting while seated or kneeling, lifting on improper frictional surfaces, lifting unstable loads, or lifting for more than 8 hours. To this effect, the third presentation examines the wider application of the RLE by adding new multipliers to the equation. Extending further on quantifying typical lifting task-related variables and associated risk on the lower back, the final presentation explores the fatigue failure process experienced by the lumbar spine when performing multiple and varied lifting tasks. This innovative approach is nascent in ergonomics literature, especially in ergonomics risk assessment, and has great potential in injury prevention at the workplace.

Differences in muscular activity between obese and non-obese workers during manual lifting

The prevalence of obesity is increasing throughout the workforce. Manual lifting tasks are common and can produce significant muscle loading. This study compared muscular activity between obese and non-obese subjects, using surface Electromyography (EMG), during manual lifting. Six different lifting tasks (with 5, 10 and 15 kg loads in free and constrained styles) were performed by 14 participants with different obesity levels. EMG data normalization was based on the percentage of Maximum Contraction during each Task (MCT). Muscle Activation Times (AT) before each task were also evaluated. The study suggests that obesity can increase MCT and delay muscle AT. These findings reinforce the need to develop further studies focused on obesity as a risk factor for the development of musculoskeletal disorders.

Evaluation of Lumbar Spine Load by Computational Method in Order to Acknowledge Low-back Disorders as Occupational Diseases.

The aim of the study was to develop a computational module for the prediction of compressive force on the L4/L5 disc suitable for use in field settings. The value of compressive force is intended to be used as a proxy measure of the mechanical burden of low-back when performing work activities. The compressive force predicted by the module in a particular worker should be compared with the NIOSH limit value of 3,400 N for the assessment of lumbar spine load during manual lifting tasks. Exceeding the limit will be considered as the fulfilment of "hygienic criterion" that should be met to acknowledge low-back disorder as an occupational disease. To develop the computational module we used the ergonomic software TECNOMATIX Classic Jack taking into account the anthropometric parameters of a worker and ergonomic parameters of his/her work activity. We calculated compressive forces on the L4/L5 disc in about 1,300 simulated combinations of various factors influencing compressive force. Parameters which turned out to be crucial for the compression of L4/L5 disc were included in the computational algorithm. Our study was primarily aimed at the assessment of lumbar disorders as occupational diseases. Moreover, the study can contribute to the recommendation of preventive measures to decrease health risks in occupations associated with the overload of low-back region. The graphic maps generated by the computational module enable a fast and exact analysis of particular job.

Effect of pelvic forward tilt on low back compressive and shear forces during a manual lifting task.

[Purpose] To examine the effect of an instruction to increase pelvic forward tilt on low back load during a manual lifting task in the squat and stoop postures. [Subjects] Ten healthy males who provided informed consent were the subjects. [Methods] Kinetic and kinematic data were captured using a 3-dimensional motion analysis system and force plates. Low back compressive and shear forces were chosen as indicators of low back load. The subjects lifted an object that weighed 11.3 kg, under the following 4 conditions: squat posture, stoop posture, and these lifting postures along with an instruction to increase pelvic forward tilt. [Results] In the squat posture, the instruction to increase pelvic forward tilt reduced the low back compression and shear forces. [Conclusion] The present results suggest that a manual lifting task in the squat posture in combination with an instruction to increase pelvic forward tilt can decrease low back compression and shear forces, and therefore, might be an effective preventive method for low back pain in work settings.