Material Handling Tasks Research Articles

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.

Ergonomics Postural Risk Assessment and Observational Techniques in the 21st Century

Workers in various industries where materials are handled manually are at a higher risk of suffering musculoskeletal disorders (MSDs) and related body pains. These workers experience various occupational diseases due to the awkward working postures they take during tasks. Some of these work postures include sitting (for long period), lifting, body twisting, or bending. This review studied the various body postures and material handling tasks employed during daily work activities by workers. The occupational hazard in human tasks, general survey of postures in human tasks, body parts involved in human tasks, assessment of occupational risk factors and various approaches to modification have also been reviewed. The review concluded with suggestions on possible ergonomic intervention that could alleviate the risk factors caused by these awkward body postures.

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.].

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.

The effect of a passive exoskeleton on trunk co-activation during liftings at different risk levels

The new Industry 4.0 scenarios offer opportunity for both the prevention of biomechanical overload risks and the implementation of extra return-to-work initiatives for people with disabilities. Wearable assistive devices (e.g., an industrial exoskeleton) can help to lower the risk of acquiring work-related musculoskeletal diseases (WMSDs) caused by manual material handling (MMH) tasks, among other things [1]. The most prevalent and expensive musculoskeletal diseases are WLBDs, which are primarily caused by manual lifting jobs [1]. This study aims to investigate the influence of TRUExo, a passive exoskeleton for the trunk composed of a textile and a mechanical component for energy restitution, on trunk coactivation in lifting activities at various risk levels (more details cannot currently be described because the device is under patent). Twelve healthy volunteers (age: 36.33±7.66 years; BMI: 22.34±2.21 kg/m2; 6 females and 6 males) completed the lifting tasks designed using the revised NIOSH lifting equation, [2], at progressively increasing lifting indexes (from 1 to 3). The tasks were performed with and without the TRUExo exoskeleton, and the activity of the erector spinae longissimus (RESL) and rectus abdominis superior (RAS) was recorded bilaterally using a 16-channel Wi-Fi transmission surface electromyograph (Mini Wave Infinity, Cometa, Milan, Italy) system at a sampling rate of 2000 Hz. Before beginning the lifting tasks, participants completed three repetitions of a specific exercise to record the isometric maximal voluntary contractions (iMVCs) for the muscles under investigation. Then, after sEMG processing and normalization respect to iMVC values, we computed the time-varying multi-muscle co-activation function (TMCf), [3]. Our findings reveal that the mean TMCf of all patients is lower with the passive exoskeleton than without it at each LI. The mean of the TMCf reduces significantly with LI when wearing the exoskeleton, according to two-way repeated-measures ANOVA and post-hoc analysis (Fig. 1B). The results reveal that the TMCf increases in relation to the risk levels both with and without the exoskeleton, but the TMCf is lower at each LI when the subject wears the exoskeleton ( Fig. 1 , B). These findings could lend credence to the notion that a passive exoskeleton worn during a working activity could alleviate biomechanical load and, as a result, lower the likelihood of WMSD occurrence. Furthermore, our findings highlight the TMCf's sensitivity to differentiate different risk levels even when wearing an exoskeleton, implying that it might be used as an operational tool to assess biomechanical risk in situations where traditional approaches (e.g., NIOSH's method) cannot be used.

An automatic procedure to classify biomechanical risk classes associated to lifting activities according to the Revised NIOSH Lifting Equation

In the field of occupational medicine, physical ergonomics studies, aimed at preventing work-related illnesses, have gained importance in recent years since both quantitative/semi-quantitative observational studies and instrumental methods have reported promising results. Among the instrumental methods, wearable devices capable to acquire signals such as acceleration and angular velocity, electromyography (EMG) [1] have potentially proved useful to help assessing – both automatically and more accurately – the work-related risk, which can lead to biomechanical overloading and the development of work-related musculoskeletal disorders (WRMD), especially in the case of workers involved in activities such as material handling and lifting tasks. In this context, a powerful method to monitor workers biomechanical risk is a National Institute for Occupational Safety and Health (NIOSH) quantitative formula (RNLE), which considers factors such as intensity, duration and frequency (together with other geometrical characteristics) of a lifting task [2]. The study was carried out enrolling 13 healthy subjects with no signs of musculoskeletal disorder wearing an inertial sensor (Opal, APDM Inc.) on the chest. Each subject performed a session based on two trials. Each trial consisted in 20 consecutive lifting of a plastic container. The first trial was performed in a condition with LI < 1 which corresponds to the No-risk class while the second trial was performed in a condition with LI > 1 which correspond to the Risk class. LI of 0.5 and 1.3 were derived from the RNLE by variously combining height, frequency and weight of lifting tasks. The collected acceleration and angular velocity signals along the three axes were filtered and then segmented to extract the region of interest (ROI) corresponding to the lifting by means of the Savitzky-Golay filter. For each ROI several features in the time and frequency domains were extracted and feed to machine learning algorithms to evaluate the discriminative power of these features to classify risk classes according to the RNLE. Table 1 shows the results of the machine learning (ML) analysis by means of some evaluation metrics using different ML algorithms. The ML analysis was carried out on a dataset composed of 520 instances (13 subject x 40 lifting), 114 features (19 features x 6 axes) and two classes (No-risk, risk). The goal of this research was to develop an automatic procedure - based on algorithms for digital signal processing and ML algorithms fed with appropriate time and frequency domains features extracted from sternum inertial data – able to classify lifting biomechanical risk classes according to the RNLE. The results of the ML analysis, especially Random Forest, showed high scores in evaluation metrics. The presented methodology could represent a valid integration to the conventional protocols used in ergonomics to evaluate the biomechanical risk more quickly and easily. These results are of direct practical relevance for occupational ergonomics, as they present the opportunity for automatic, economic and non-invasive (by placing an IMU on the sternum) detection of the risk associated with lifting. Future investigation on enriched dataset that will involve several scenarios and risk classes could confirm the potentiality of the proposed methodology.

Augmented reality (AR) posture training for manual material handling: iterative design, evaluation, and recommendation for AR interface

Manual material handling (MMH) tasks can pose risks of musculoskeletal disorders for workers, which makes posture training programs essential. The ultimate goal of this research was to design and evaluate an augmented reality (AR) based training platform, namely the virtual instructor application (VIA). Specifically, the usability problems revealed from the preliminary version (VIA-1) were addressed and enhanced features were implemented in the current version (VIA-2). The usability of VIA-2 was assessed by ten individuals with experience of MMH. Participants explored VIA-2 while performing a series of predefined tasks. A usability questionnaire was administered, and a brief interview was conducted. Results showed that VIA-2 had increased usability compared to VIA-1. While some visual content display issues occurred, a future minor refurnish can address these problems. Finally, the iterative design and evaluation scheme can be applied to the future design of AR-based training programs and evaluation protocols.

Inertial Motion Capture-Based Estimation of L5/S1 Moments during Manual Materials Handling.

Inertial motion capture (IMC) has gained popularity in conducting ergonomic studies in the workplace. Because of the need to measure contact forces, most of these in situ studies are limited to a kinematic analysis, such as posture or working technique analysis. This paper aims to develop and evaluate an IMC-based approach to estimate back loading during manual material handling (MMH) tasks. During various representative workplace MMH tasks performed by nine participants, this approach was evaluated by comparing the results with the ones computed from optical motion capture and a large force platform. Root mean square errors of 21 Nm and 15 Nm were obtained for flexion and asymmetric L5/S1 moments, respectively. Excellent correlations were found between both computations on indicators based on L5/S1 peak and cumulative flexion moments, while lower correlations were found on indicators based on asymmetric moments. Since no force measurement or load kinematics measurement is needed, this study shows the potential of using only the handler’s kinematics measured by IMC to estimate kinetics variables. The assessment of workplace physical exposure, including L5/S1 moments, will allow more complete ergonomics evaluation and will improve the ecological validity compared to laboratory studies, where the situations are often simplified and standardized.

What Drives Acceptance of Occupational Exoskeletons? Focus Group Insights from Workers in Food Retail and Corporate Logistics

The potential of occupational exoskeletons can only be realized if workers are willing to wear them on their bodies. As classical technology acceptance theories originate in research into information technology, they do not sufficiently cover the peculiarities of exoskeletons, and thus greater focus is needed on factors that specifically shape intentions to use them. Involving three companies from food retail and logistics, we conducted guided focus groups with 18 workers who perform material handling tasks in their daily work. Participants discussed the envisioned benefits, risks, and conditions related to the adoption of exoskeletons. Consistent with established technology acceptance models, performance-related and effort-related factors were found to be highly recognized. Participants also highlighted factors such as wellbeing, fairness, and the altered physical appearance of wearers, which was considered important in social contexts at work. Complementing established factors with new exoskeleton-specific determinants, our results are a valuable starting point for further exoskeleton user studies.

Investigation of precision task demands on workers' muscular activities, heart rate, and perceived exertion rating.

Manual material handling (MMH) tasks significantly contribute to musculoskeletal disorders in manual workers. In this study, we assessed the physical factors of precision lifting tasks that affect muscular activities (electromyography, EMGs), heart rate, and perceived exertion rating in ten healthy male workers aged 25-35 years, while considering the safety aspect of the common types of safety footwear. The independent variables that were assessed are as follows: 1) lifting method (precise and inexact), 2) lifting frequency (one and four liftings/min), and 3) type of safety footwear worn by the worker (light, medium, or heavy). The response variables data, represented by EMG signals, for four muscular activities (biceps brachii, deltoid, trapezius, and erector spinae), heart rate, and perceived exertion were analyzed using a three-factor within-subjects design. The results showed that wearing heavy safety shoes increases the effort used with precise lifting methods in trapezius and erector spinae muscular activities. We also observed that the heart rate and perceived exertion increased rapidly at four lifts/min compared to one lift/min, regardless of the lifting method. The advantages of choosing appropriate safety footwear must be carefully assessed before replacing the conventional working safety shoes.

The Validity and Inter-Rater Reliability of a Video-Based Posture-Matching Tool to Estimate Cumulative Loads on the Lower Back.

Background: Low back pain (LBP) is known as one of the most common work-related musculoskeletal disorders. Spinal cumulative loads (CLs) during manual material handling (MMH) tasks are the main risk factors for LBP. However, there is no valid and reliable quantitative lifting analysis tool available for quantifying CLs among Iranian workers performing MMH tasks.Objective: This study aimed to investigate the validity and inter-rater reliability of a posture-matching load assessment tool (PLAT) for estimating the L5-S1 static cumulative compression (CC) and shear (CS) loads based on predictive regression equations.Material and Methods: This experimental study was conducted among six participants performing four lifting tasks, each comprised of five trials during which their posture was recorded via a motion capture (Vicon) and simultaneously a three-camera system located at three different angles (0°, 45°, and 90°) to the sagittal plane.Results: There were no significant differences between the two CLs estimated by PLAT from the three-camera system and the gold-standard Vicon. In addition, ten raters estimated CLs of the tasks using PLAT in three sessions. The calculated intra-class correlation coefficients for the estimated CLs within each task revealed excellent inter-rater reliability (> 0.75), except for CS in the first and third tasks, which were good (0.6 to 0.75). Conclusion: The proposed posture-matching approach provides a valid and reliable ergonomic assessment tool suitable for assessing spinal CLs during various lifting activities.

Assessment of a Passive Lumbar Exoskeleton in Material Manual Handling Tasks under Laboratory Conditions.

Manual material handling tasks in industry cause work-related musculoskeletal disorders. Exoskeletons are being introduced to reduce the risk of musculoskeletal injuries. This study investigated the effect of using a passive lumbar exoskeleton in terms of moderate ergonomic risk. Eight participants were monitored by electromyogram (EMG) and motion capture (MoCap) while performing tasks with and without the lumbar exoskeleton. The results showed a significant reduction in the root mean square (VRMS) for all muscles tracked: erector spinae (), semitendinosus (), gluteus (), and quadriceps (). The classic fatigue parameters showed a significant reduction in the case of the semitendinosus: zero-crossing rate, mean frequency, and median frequency. In addition, the logarithm of the normalized Dimitrov’s index showed reductions of 11.5, 8, and 14% in erector spinae, semitendinosus, and gluteus, respectively. The calculation of range of motion in the relevant joints demonstrated significant differences, but in almost all cases, the differences were smaller than . The findings of the study indicate that the passive exoskeleton reduces muscle activity and introduces some changes of strategies for motion. Thus, EMG and MoCap appear to be appropriate measurements for designing an exoskeleton assessment procedure.

Design of a Fuzzy Logic Evaluation to Determine the Ergonomic Risk Level of Manual Material Handling Tasks.

In this work, we propose a fuzzy inference as a decision support system built in the MATLAB Fuzzy Logic Designer for evaluating manual material handling risk conditions. The input variables for the fuzzy decision were: (1) the total time duration of the manual material handling in one shift of 450 min, with 3 h considered the maximal exposition time; (2) 25 kg as a maximal mass reference which should never be exceeded; (3) the repetitiveness of the manual material handling task through the shift considering as the maximal frequency of four lifts per min. Results of 135 earlier direct ergonomic evaluations made using the method proposed by the ISO 11228-1 were used as validator results, and called “expected results”. The experimentation intended to simulate an ergonomic evaluation in different boundary conditions of work and verify if the fuzzy interface could correctly replicate the results of the ergonomic evaluations. As validation, the list with the 135 expected results was compared against the evaluation made by the fuzzy logic interface, called “Work_Conditions”. From the comparison, only three evaluations (0.02%) differed with respect to the expected results. Consequently, it is concluded that the fuzzy interface can be used as a tool for automating the determination of manual material handling ergonomic risk levels, with great precision.

Application of the predicted biomechanics approach in assessing work-related musculoskeletal discomfort among female brickfield workers in West Bengal, India.

Manual brick manufacturing units in West Bengal, India are mostly unorganized sectors employing many migrant female workers in manual material handling tasks as brick moulders and brick carriers. The study estimated the biomechanical postural stress developed due to manual load handling, carriage and brick transportation. Body Part Discomfort Scale was used to assess the subjective pain felt by 18 adult female brick moulders and 12 brick carriers during work. Video recordings were taken during the actual field work and the images were analyzed for two-dimensional prediction kinetics and kinematics. Results show that the horizontal distance of the load from the body was 59.95±3.74 cm during the initial lifting of bricks by the brick carriers and so their biomechanical trunk angle inclination was significantly detrimental. The bending moment at the lumbosacral disc was much higher during the squatting posture while initial brick lifting. Moreover, the trunk and the neck pain of the brick carriers were caused by carrying 18% of body weight load. The brick carriers were the more vulnerable group in terms of work-related musculoskeletal disorders. Therefore, ergonomic interventions such as relay brick carrying and pole supported balanced brick carrying, may be implemented to reduce the prevalence of pain. This study is the first of its kind that validates the extent or the prevalence of work-related musculoskeletal disorders among the female brickfield workers through approaches of predicted biomechanics.

Heart rate variability based physical exertion monitoring for manual material handling tasks

Physical exertion monitoring has been strongly emphasized to avert the ill-effects of physically demanding nature of many industries such as construction. Recently, several sensors-based approaches have been suggested as an alternative to traditional subjective feedback-based methods. Although the proposed sensor-based approaches have laid the foundation for automated physical exertion monitoring, they require multiple on-body and/or off-body sensors to collect psychological, physiological, acceleration/posture or weather-related data. As such, multiple on-body sensors may instigate irritation and discomfort whereas other off-body sensors require additional resources for handling and managing them. To address these limitations, taking a minimalistic approach, this study explored the use of heart rate variability (HRV) metrics which could be computed from a single electrocardiogram or optical sensor (often found in fitness wrist bands and smart watches). For this purpose, manual material handling experiments were conducted while state-of-the-art HRV features were used to perform physical exertion monitoring with ensemble classifiers and artificial neural network (ANN) based regression analysis. The results indicate that ensemble classifiers achieved accuracies from 64.2% to 81.2%, depending on the number of levels in which physical exertion data was divided, whereas ANN regression achieved the least root mean square error of 1.651. Given the wide availability of HRV sensors in fitness bands and wrist watches, this study highlights the usability and limitations of HRV based physical exertion monitoring which could help make informed decisions related to its adoption in physically demanding industries such as construction.

A Case Study of Ergonomics Prevention Program in a Machine Manufacture Industry

Musculoskeletal Disorders (MSDs) is the most potential risks in human production and economic systems. In this research, ergonomics guidelines and enacted relevant regulations announced by other countries to effectively facilitate ergonomics prevention programs were discussed. A machine manufacture industry as a case study has been ergonomically evaluated by Musculoskeletal Disorders Checklist (MSDs) and Nordic Musculoskeletal Questionnaire (NMQ), which was used to recognize the dominating risk factors. According to the result of observation and preliminary assessment, low back pain related to the high frequency of manual material handling tasks was the critical issue in this case study. The conclusion revealed that the proposed improvement such as engineering redesign, training, health management, and administration, would be implemented into the main axle assembly area, customer service department, and Z-axis assembly area. The further improvement interventions would help organization to carry out the ergonomics prevention program.

Seeking Human Help to Manage Plan Failure Risks in Semi-Autonomous Mobile Manipulation

AbstractWe present a framework for identifying, communicating, and addressing risk in shared-autonomy mobile manipulator applications. This framework is centered on the capacity of the mobile manipulator to sense its environment, interpret complex and cluttered scenes, and estimate the probability of actions and configuration states that may result in task failures, such as collision (i.e., identifying “risk”). If the threshold for acceptable risk is exceeded, a remote operator is notified and presented with timely, actionable information in which the person can quickly assess the situation and provide guidance for the robot. This framework is demonstrated with a use case in which a mobile manipulator performs machine tending and material handling tasks.

The impact of using wearable devices on the operator during manual material handling tasks

There has been a recent advent in the use of wearable devices in the workplace as these technologies offer an opportunity to track product movement through the warehousing and material handling process. This study examines the use of a wand scanner and two wearable devices (ring scanner and smart glasses) on forty healthy participants to study comfort, discomfort and performance while executing shipment putting tasks. The impact of wearables was investigated using the Comfort Rating Scales and Body Discomfort Scale. Participants identified the ring and wand scanner as the most comfortable device. Participants became more uncomfortable using the smart glasses over time during the completion of the task. While using the glasses, the participants experienced discomfort in the eye, neck, and shoulder. This research shows that there is a need for additional improvement with smart glasses before incorporating this technology into the workplace. Companies contemplating incorporating smart glasses to improve productivity can use the results of this study to identify important features needed to optimize this device. Likewise, companies interested in shifting away from handheld scanning technologies to wearable solutions can consider ring scanners and similarly donned devices as performance enhancing solutions. Relevance to industryThis study investigates productivity, comfort, and discomfort of standard material handling tasks through the comparison of multiple scanning devices including a commonly used wand scanner and two wearable solutions (smart glasses and a ring scanner). Ring scanner of most productive with smart glasses being both the least productive and comfortable.

Different Phases in Manual Materials Handling Have Different Performance Criteria: Evidence From Multi-Objective Optimization.

A manual material handling task involves the phases of reaching, lifting, unloading, and standing up (RLUS). Understanding the mechanisms of manual material handling is important for occupational health and the development of assist devices. Predictive models are becoming popular in exploring which performance criterion is appropriate in the lifting phase. However, limited attempts have been performed on the other phases. The associated performance criterion for predicting other phases is unknown. In this study, an optimization model for predicting RLUS has been developed with the multi-objective optimization method. Two performance criteria (minimum dynamic effort and maximum balance) were studied to explore their importance in each phase. The result shows that maximum balance leads to joint angle errors 27.6% and 40.9% smaller than minimum dynamic effort in reaching and unloading phases, but 40.4% and 65.9% larger in lifting and standing up phases. When the two performance criteria are combined, the maximum balance could help improve the predicting accuracy in the reaching, lifting, and unloading phases. These findings suggest that people prefer different performance criteria in different phases. This study helps understand the differences in motion strategies in manual materials handling (MMH), which would be used to develop a more accurate predictive model.

Risk events recognition using smartphone and machine learning in construction workers' material handling tasks

PurposeThe outbreak of the pandemic makes it more difficult to manage the safety or health of construction workers in infrastructure construction. Risk events in construction workers' material handling tasks are highly relevant to workers' work-related musculoskeletal disorders. However, there are still many problems to be resolved in recognizing risk events accurately. The purpose of this research is to propose an automatic and non-invasive recognition method for construction workers in material handling tasks during the pandemic based on smartphone and machine learning.Design/methodology/approachThis research proposes a method to recognize and classify four different risk events by collecting specific acceleration and angular velocity patterns through built-in sensors of smartphones. The events were simulated with anterior handling and shoulder handling methods in the laboratory. After data segmentation and feature extraction, five different machine learning methods are used to recognize risk events and the classification performances are compared.FindingsThe classification result of the shoulder handling method was slightly better than the anterior handling method. By comparing the accuracy of five different classifiers, cross-validation results showed that the classification accuracy of the random forest algorithm was the highest (76.71% in anterior handling method and 80.13% in shoulder handling method) when the window size was 0.64 s.Originality/valueLess attention has been paid to the risk events in workers' material handling tasks in previous studies, and most events are recorded by manual observation methods. This study provided a simple and objective way to judge the risk events in manual material handling tasks of construction workers based on smartphones, which can be used as a non-invasive way for managers to improve health and labor productivity during the pandemic.