Manual Material Handling Tasks Research Articles

Evaluating the Efficacy of a Passive Exoskeleton for Enhancing Ergonomics in Manufacturing

Manual material handling (MMH) significantly impacts worker health and productivity, often leading to musculoskeletal disorders (MSDs) primarily in the lower back. As a novel assistive technology, exoskeletons may serve as ergonomic tools to mitigate these work-related MSDs. It is essential to examine exoskeletons from the users’ perspectives before their widespread implementation in occupational settings. This study investigates the effectiveness of a passive back-support exoskeleton (BExo) in reducing perceived physical exertion and improving ergonomic safety in a manufacturing context. Twenty-two college students were recruited to perform MMH tasks in a controlled lab environment, both with and without the BExo, followed by completing a survey questionnaire on various aspects of the BExo. Using ANOVA, the study analyzed biomechanical exertion across various body parts and tasks. The findings indicate that the BExo substantially alleviated discomfort and physical exertion in the low back, shoulders and knees, thereby enhancing an ergonomic posture and reducing fatigue. These results underscore the potential of passive exoskeletons to boost workers’ safety and efficiency, providing valuable insights for future ergonomic strategies in industrial settings.

A comparative study of biomechanical assessments in laboratory and field settings for manual material handling tasks using extractor tools and exoskeletons.

To enhance physical capabilities of workers who regularly perform physically demanding tasks involving heavy lifting and awkward postures, various tools and occupational exoskeletons can be used. Most of the studies aiming to explore the efficiency of these tools and exoskeletons have been performed in confined and controlled laboratory spaces, which do not represent the real-world work environment. This study aimed to compare the outcome of biomechanical assessment of using a back support exoskeleton and assistive tools (Lever and Jake) in the procedure of a high demanding manual material handling task versus the results found by performing the same task in a laboratory. Ten able-bodied participants and ten able-bodied utility workers performed the same manhole removal task in-lab and in-field, respectively, with the aid of an exoskeleton and Lever and Jake tools. Muscle activity and Rapid Entire Body Assessment (REBA) scores were recorded using surface electromyography and inertial measurement units, respectively and compared between in-lab and in-field trials. The field experiments indicated significant differences (p < 0.05) in normalized muscle activity across most muscles when compared to laboratory data. These results revealed how muscle activity is affected by the controlled lab setting compared to real-world field conditions. However, REBA scores indicate similar ergonomic implications regardless of the utilization of exoskeletons or tools. These findings underscore that real-world field assessments are crucial for evaluating ergonomic risks and effects of occupational exoskeletons and tools to account for environmental factors and workers' skills in ergonomic evaluations of this nature.

Biomechanical analysis of different back-supporting exoskeletons regarding musculoskeletal loading during lifting and holding

Industrial back support exoskeletons (BSEs) are a promising approach to addressing low back pain (LBP) which still affect a significant proportion of the workforce. They aim to reduce lumbar loading, the main biomechanical risk factor for LBP, by providing external support to the lumbar spine. The aim of this study was to determine the supporting effect of one active (A1) and two passive (P1 and P2) BSEs during different manual material handling tasks. Kinematic data and back muscle activity were collected from 12 subjects during dynamic lifting and static holding of 10 kg. Mean and peak L5/S1 extension moments, L5/S1 compression forces and muscle activation were included in the analysis. During dynamic lifting all BSEs reduced peak (12–26 %) and mean (4–17 %) extension moments and peak (10–22 %) and mean (4–15 %) compression forces in the lumbar spine. The peak (13–28 %) and mean (4–32 %) activity of the back extensor muscles was reduced accordingly. In the static holding task, analogous mean reductions for P1 and P2 of L5/S1 extension moments (12–20 %), compression forces (13–23 %) and muscular activity (16–23 %) were found. A1 showed a greater reduction during static holding for extension moments (46 %), compression forces (41 %) and muscular activity (54 %). This pronounced difference in the performance of the BSEs between tasks was attributed to the actuators used by the different BSEs.

Online Ergonomic Evaluation in Realistic Manual Material Handling Task: Proof of Concept.

Work-related musculoskeletal disorders are globally one of the leading causes of work-related injuries. They significantly impact worker health and business costs. Work task ergonomic risk indices have been developed that use observational assessments to identify potential injuries, and allow safety managers to promptly intervene to mitigate the risks. However, these assessments are very subjective and difficult to perform in real time. This work provides a technique that can digitalize this process by developing an online algorithm to calculate the NIOSH index and provide additional data for ergonomic risk assessment. The method is based on the use of inertial sensors, which are easily found commercially and can be integrated into the industrial environment without any other sensing technology. This preliminary study demonstrates the effectiveness of the first version of the Online Lifting Index (On-LI) algorithm on a common industrial logistic task. The effectiveness is compared to the standard ergonomic assessment method. The results report an average error of 3.6% compared to the NIOSH parameters used to calculate the ergonomic risk and a relative error of the Lifting Index of 2.8% when compared to observational methods.

Effect of a confidence-based weighted 3D point reconstruction for markerless motion capture with a reduced number of cameras

ABSTRACTMarkerless motion capture has been made available by the development of pose estimation algorithms that provide both an estimate of the location of body keypoints in two-dimensional images and its associated confidence. It seems relevant to use this additional information for three-dimensional (3D) point reconstruction. Yet, it has been little described, nor has its influence on 3D point reconstruction. Eight participants performed a manual material handling task, which was recorded by 10 video cameras. Each video was processed using OpenPose. Different 3D point reconstruction methods were compared: direct linear transform (DLT) and weighted DLT (wDLT), with 10 cameras and with a subset of 4 cameras. For each keypoint, confidence and position deviation from the 3D reconstructed point with 10 cameras projected in the image reference frame were assessed. Results suggest that using confidence information reduces both average and maximum 3D distance between 3D points reconstructed with 4 and 10 cameras.

Posture Risk Assessment and Workload Estimation for Material Handling by Computer Vision

Workers in material handling tasks often suffer from work-related musculoskeletal disorders (WMSDs) caused by inaccurate work postures or the lifting of excessively heavy loads. Therefore, effective ergonomic assessment of workers is needed to improve worker productivity while reducing the risk of musculoskeletal disorders. This paper proposes a noninvasive method for evaluating posture risks and load analysis in manual material handling tasks. The study focuses on three main aspects: first, using 3D pose recognition technology to extract the 3D coordinates and joint angles of the human body. Second, the REBA method was improved by using fuzzy logic theory to more effectively capture the slow transition features of continuous movement by humans without abruptly altering risk scores, as well as to increase the accuracy and consistency of posture risk evaluation. Third, joint torque and workloads were estimated using biomechanical calculations by integrating pressure insoles and 3D joint coordinate data. Experiments show that this method can effectively evaluate posture risks and workloads in manual material handling tasks, with a correlation coefficient of 0.817 ( p &lt; 0.01 ) between fuzzy logic REBA and REBA and an error rate of 15% in estimating workloads of eight joints. This method can help reduce occupational health risks for workers and industries and improve work efficiency.

Design and evaluation of a parallel mechanism for wearable lumbar support exoskeleton.

Work-related musculoskeletal disorders (WMSDs) are a serious problem, and manual material handling (MMH) tasks remain common in most industries. Thus, a lightweight and active exoskeleton is to be needed. A facile, convenient, multifunctional, wearable lumbar support exoskeleton (WLSE) was proposed to relieve the muscular tension and fatigue especially in the way of WMSDs. Based on the screw theory and virtual power principle, the parallel structure was used as the scheme choice for selecting suitable actuators and joints. The exoskeleton, which was characterized by high adaptability and complied with human motion, included branch unit, mechanism branch units, control units and sensors. Furthermore, using surface electromyography (sEMG) signal evaluation, an experiment which contains several tests were designed to evaluate whether WLSE had effect on supporting and reliving muscular fatigue while lifting-up different weight of objects under wearing without traction (T1) and wearing with traction (T2). Data collected were analyzed statistically by the two-way ANOVA. It showed that the RMS of sEMG was obviously reduced while carrying the heavy objects with WLSE under T2, and the MF values always performed the decreasing trend in T2/T1. This paper proposed a facile, convenient, multifunctional WLSE. From the results, it was concluded that the WLSE was significantly effective in reliving the muscle tension and muscle fatigue while lifting to prevent and treat WMSDs.

Biomechanical investigation of tasks concerning manual materials handling using response surface methodology

In typical manual material handling, the variations in walking pattern are decided by various factors, such as load being handled, frequency of handling, walking surface, etc. Traditional gait analysis protocols commonly evaluate individual factor within specified ranges associated with particular activities or pathologies. However, existing literature underscores the concurrent impact of multiple factors on gait. This study identifies five pivotal factors—walking speed, surface slope, load carried, carrying method, and footwear—as contributors to gait alterations. To address risk factors in manual material handling activities, we propose a unique design-of-experiment-based approach for multi-task gait analysis. Unraveling the relationship between manual handling attributes and human gait holds paramount importance in formulating effective intervention strategies. We optimized the five input factors across a cohort of 15 healthy male participants by employing a face-centered central composite design experimentation. A total of 29 input factor combinations were tested, yielding a comprehensive dataset encompassing 18 kinematic gait parameters (such as cadence, step length etc., measured using inertial measurement system), the isolated impacts of factors, and the interplay of two-factor interactions with corresponding responses. The results illuminate the optimal scenarios of input factors that enhance individual gait performance—these include wearing appropriate footwear, employing a backpack for load carriage, and maintaining a moderate walking pace on a medium slope with minimal load. The study identifies walking speed and load magnitude as primary influencers of gait mechanics, followed by the chosen carrying method. In consequence, the insights gained advocate for the refinement of manual material handling tasks based on the outcomes, effectively mitigating the risk of musculoskeletal disorders by suggesting the interventions for posture correction.

The influence of transfer distance and pace of work on foot positioning strategies and low back loading in a manual material handling task

Foot positioning strategies are a key parameter in manual materials handling. The objective of this study was to assess the effects of transfer distance, pace and foot positioning on low back loading. Sixteen handlers performed a free handling task with conditions of pace (self-selected and 25% faster), distance (1.5 m, 1.0 m and 0.5 m), height (near-ground and above-waist levels), and mass (10 kg and 20 kg). A linear mixed model was used to evaluate the effects of mass, distance, pace and height on foot positioning occurrences. A non-parametric test (nparLD) was used to evaluate the effects of Pace × Foot positioning and Distance × Foot positioning on L5/S1 sagittal and asymmetrical moments. Positioning the feet aside from the lifting pallet was more prevalent when transferring at shorter transfer distances, high lifting height, and faster pace. When the feet were oriented aside the lifting area, asymmetrical peak moments were slightly higher when transferring at short distance. Facing the lifting pallet may ensure load symmetry, but orienting the feet aside may help to adapt to fast pace or short transfer distance.

Effects of using passive back- and arm-support exoskeletons for cart pushing and pulling

Though studies have shown both BSEs and ASEs are effective in reducing physical demands for various manual material handling tasks, limited evidence is currently available on their effects in pushing and pulling tasks. We evaluated the impacts of using a passive back-support (BSE) and an arm-support exoskeleton (ASE) on trunk kinematics, and the back and arm musculature for pushing and pulling of a moderately loaded (100 kg) cart. Fourteen volunteers performed cart the tasks, each of whom was randomly assigned one of the exoskeletons (BSE or ASE). Wearing the BSE substantially reduced lumbar muscle activity during both pushing (up to ~39%) and pulling (up to ~35%) compared to not wearing EXOs, while wearing an ASE had no significant impacts. For the anterior deltoid muscle, neither BSE nor ASE had beneficial impacts. Findings from the current study help to understand the effects of BSEs and ASEs in pushing and pulling tasks.

Effect of a Simultaneous Mental Arithmetic Task to a MMH Lifting Task on Muscle Activity in the Trunk and Shoulders

Introduction: This study investigated the effects of concurrent mental demands on material handling tasks. Methods: Eleven subjects performed a material handling task with and without cognitive loads while lifting a 5 kg box to three different destination heights. The cognitive load was created by having participants perform a concurrent math task while lifting the box. The study measured the muscle activity in trunk and shoulder muscles. Results: The study found that, in select muscles, the cognitive load conditions led to longer lift times and increased cumulative muscle activity, but lower peak muscle activity. Conclusion: These findings suggest that lifting tasks with a concurrent cognitive load could lead to greater cumulative muscle activity, increasing the risk of muscular fatigue during manual material handling tasks.

Comparison of soft vs. rigid back support exoskeletons through psychophysical and biomechanical approaches during load handling

As occupational exoskeletons are approaching real world implementation, perceptions about use and their effectiveness may differ across diverse age/gender groups, and device design. A soft and rigid type back-support exoskeleton (EXO) were evaluated in terms of selfrated maximum acceptable load (MAL), usability/social perceptions, and biomechanical outcomes during simulated manual material handling tasks. Thirty-six female and male participants from two age groups (18- 30 and 45-60 years) completed repetitive lifting and lowering tasks. Overall, both EXOs significantly improved participants’ MAL by ~6-7%, reduced peak trunk angle by 3-5 degrees, and reduced peak trunk extensor muscle activity by 9 - 13% . Participants generally reported both the EXOs as being useful, easy to use, and safe. This first systematic consideration of diverse age groups using a psychophysics approach in the context of passive occupational EXO use in laboratory settings contributes to bridging the gap between lab and field studies in exoskeleton implementations.

Biomechanical and physiological responses to allowable load handling limits in Iranian female workers

AbstractIn 2017, load limit values for manual material handling (MMH) tasks were confirmed to protect workers from low back disorders without prior validity investigation for Iranian workers. The present study investigated the applicability and feasibility of the Washington State Department of Labor and Industries (WISHA) lifting method as an occupational exposure limit (OEL) for the MMH tasks in a sample of young Iranian female workers using biomechanical and physiological criteria. A laboratory study was conducted with 10 female participants performing lifting tasks as described in the Iranian OEL for the MMH tasks based on the WISHA checklist. Each participant completed 21 lifting task combinations that varied in height and reach with the maximal allowable load. To test the applicability of the WISHA method, the maximum aerobic capacity and the equivalent heart rate (EHR) were estimated for 10 young Iranian female workers using an ergometer and Astrand protocol. Moreover, spinal loads for each lifting task were calculated using a static biomechanical model. From a physiological point of view, approximately 38% of the lifting tasks resulted in an increase in the physical workload. For 33% of the lifting trials, the estimated compression loads exceeded 3400 N. The findings revealed that the weight and lift height of the loads affected the lifting capacity of the participants. It seems that the allowable weight limits for manual lifting are not matched to the biomechanical and physiological capacities of the Iranian women. Therefore, the Iranian guideline for manual lifting is recommended to be revised based on the physiological and biomechanical capacities of the Iranian women.

Ergonomic assessment for designing manual material handling tasks at a food warehouse in India: A case study

AbstractManual handling of heavy grain bags is a commonplace activity across agriculture produce supply chain. In the present research, a manual material handling involving lifting, lowering, and carrying grain bags along the paths of variable characteristics is critically analyzed to explore the risk associated with it. The humans engaged in this activity are found to be facing discomfort, suffering from pain and musculoskeletal disorders, and undergoing medication for the same. This study proposes and demonstrates a structured ergonomic evaluation methodology to not only assess the level of discomfort/pain but also evaluate risk factors such as load handled, method of handling, frequency of handling, awkward postures, path characteristics, and so on. It employs a systematic multimethod approach consisting of Nordic Musculoskeletal Questionnaire and Borg Rating of Perceived Exertion, Ovako Working posture Assessment System, NIOSH Lifting Equation, Rapid Entire Body Assessment, and Key Indicator Method. The study reveals that about 94% of the sample population suffered from moderate to severe discomfort in ankle, knee, and lower back. Eight basic activities responsible for risky postures are identified. Frequency of handling, weight being handled, back bending/twisting, poor coupling, and walking surface are observed as major contributors to musculoskeletal discomfort. Carrying on shoulder is found to be riskier than carrying on back; however, the risk is found to be significantly lowered by carrying on backpack. The present study identifies the need for task redesign, proper gripping arrangements, auxiliary devices for lifting/carrying, and improvement in the path characteristics.

Closed-Chain Inverse Dynamics for the Biomechanical Analysis of Manual Material Handling Tasks through a Deep Learning Assisted Wearable Sensor Network.

Despite the automatization of many industrial and logistics processes, human workers are still often involved in the manual handling of loads. These activities lead to many work-related disorders that reduce the quality of life and the productivity of aged workers. A biomechanical analysis of such activities is the basis for a detailed estimation of the biomechanical overload, thus enabling focused prevention actions. Thanks to wearable sensor networks, it is now possible to analyze human biomechanics by an inverse dynamics approach in ecological conditions. The purposes of this study are the conceptualization, formulation, and implementation of a deep learning-assisted fully wearable sensor system for an online evaluation of the biomechanical effort that an operator exerts during a manual material handling task. In this paper, we show a novel, computationally efficient algorithm, implemented in ROS, to analyze the biomechanics of the human musculoskeletal systems by an inverse dynamics approach. We also propose a method for estimating the load and its distribution, relying on an egocentric camera and deep learning-based object recognition. This method is suitable for objects of known weight, as is often the case in logistics. Kinematic data, along with foot contact information, are provided by a fully wearable sensor network composed of inertial measurement units. The results show good accuracy and robustness of the system for object detection and grasp recognition, thus providing reliable load estimation for a high-impact field such as logistics. The outcome of the biomechanical analysis is consistent with the literature. However, improvements in gait segmentation are necessary to reduce discontinuities in the estimated lower limb articular wrenches.

Performance and effectiveness of a passive back-support exoskeleton in manual material handling tasks in the construction industry

Performance and effectiveness of a passive back-support exoskeleton in manual material handling tasks in the construction industry

Estimating lumbar spine loading when using back-support exoskeletons in lifting tasks

Low-back pain (LBP) continues as the leading cause of work-related musculoskeletal disorders, and the high LBP burden is attributed largely to physical risk factors prevalent in manual material handling tasks. Industrial back-support exoskeletons (BSEs) are a promising ergonomic intervention to help control/prevent exposures to such risk factors. While earlier research has demonstrated beneficial effects of BSEs in terms of reductions in superficial back muscle activity, limited evidence is available regarding the impacts of these devices on spine loads. We evaluated the effects of two passive BSEs (BackX™ AC and Laevo™ V2.5) on lumbosacral compression and shear forces during repetitive lifting using an optimization-based model. Eighteen participants (gender-balanced) completed four minutes of repetitive lifting in nine different conditions, involving symmetric and asymmetric postures when using the BSEs (along with no BSE as a control condition). Using both BSEs reduced estimated peak compression and anteroposterior shear forces (by ∼8–15%). Such reductions, however, were task-specific and depended on the BSE design. Laevo™ use reduced mediolateral shear forces during asymmetric lifting (by ∼35%). We also found that reductions in composite measures of trunk muscle activity may not correspond well with changes in spine forces when using a BSE. These results can help guide the proper selection and application of BSEs during repetitive lifting tasks. Future work is recommended to explore the viability of different biomechanical models to assess changes in spine mechanical loads when using BSEs and whether reasonable estimates would be obtained using such models.

Retraction Notice to: A pilot study to assess a risk of a high-risk group of low back pain membership in workers who perform the manual material handling tasks (Ann Occup Environ Med 2021;33:e34).

[This retracts the article e34 in vol. 33, PMID: 35024151.].

Fatigue and Recovery of Muscles for Pulling Tasks.

Manual materials handling (MMH) contributes to musculoskeletal disorders (MSDs) in the workplace. The development and recovery of muscle fatigue are essential in work/rest arrangements for MMH tasks. A pulling experiment, including a muscle fatigue test and a muscle fatigue recovery test, was conducted. In the muscle fatigue test, the participant performed a pulling task on a treadmill with a walking velocity of 1 km/h until they could no longer do so. The load was either 30 or 45 kg. The maximum endurance time (MET) was recorded. The pull strength (PS) of the participant both before and after the pulling task was measured. The subjective ratings of muscle fatigue after the pulling task were recorded. In the muscle fatigue recovery test, the participant took a rest after performing the pulling task. The participants reported their subjective ratings of muscle fatigue on the CR-10 scale after taking a rest for a time period t, where t = 1, 2,…, 6 min. The PS of the participant was then measured again. It was found that the load significantly affected the MET for pulling tasks. The load was insignificant to the decrease of the PS, but was significant to the decrease rate (PS decrease per min) of the PS. The PS decrease rate for the 45 kg condition (30.8 ± 16.5 N/min) was significantly higher (p < 0.05) than that of the 30 kg condition (15.4 ± 5.5 N/min). The recovery time significantly affected the PS and CR-10. Two MET models were established to explore the development of muscle fatigue in pulling tasks. A PS model was constructed to describe the recovery of muscle force. A CR-10 model was proposed to show the subjective ratings of recovery. These models are beneficial for determining the work/rest allowance for pulling tasks.

A novel approach to quantify the assistive torque profiles generated by passive back-support exoskeletons

Industrial exoskeletons are a promising ergonomic intervention to reduce the risk of work-related musculoskeletal disorders by providing external physical support to workers. Passive exoskeletons, having no power supplies, are of particular interest given their predominance in the commercial market. Understanding the mechanical behavior of the torque generation mechanisms embedded in passive exoskeletons is, however, essential to determine the efficacy of these devices in reducing physical loads (e.g., in manual material handling tasks). We introduce a novel approach using a computerized dynamometer to quantify the assistive torque profiles of two passive back-support exoskeletons (BSEs) at different support settings and in both static and dynamic conditions. The feasibility of this approach was examined using both human subjects and a mannequin. Clear differences in assistive torque magnitudes were evident between the two BSEs, and both devices generated more assistive torques during trunk/hip flexion than extension. Assistive torques obtained from human subjects were often within similar ranges as those from the mannequin, though values were more comparable over a narrow range of flexion/extension angles due to practical limitations with the dynamometer and human subjects. Characterizing exoskeleton assistive torque profiles can help in better understanding how to select a torque profile for given task requirements and user anthropometry, and aid in predicting the potential impacts of exoskeleton use by incorporating measured torque profiles in a musculoskeletal modeling system. Future work is recommended to assess this approach for other occupational exoskeletons.