Material Handling Tasks Research Articles

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.

BackgroundWe conducted this experimental study to estimate a risk of a high-risk group of low back pain (LBP) membership in workers who perform the manual material handling (MMH) tasks in an actual workplace setting.MethodsThe subjects include healthy workers who were engaged in 12 MMH tasks at 6 manufacturing companies. We assessed the dynamic motion of trunk or lumbar spine using an industrial lumbar motion monitor (BioDynamics Laboratory of Ohio State University). The subjects were evaluated for the age, gender, years of working and anthropometric measurements (e.g., height, weight, shoulder height, elbow height, iliac height, leg length, trunk length, trunk circumference, iliac width, iliac depth, xiphoid width and xiphoid depth). Moreover, they were also evaluated for a risk of a high-risk group of LBP membership based on lift frequency, average twisting velocity, maximum moment, maximum sagittal flexion and maximum lateral velocity.ResultsThe subjects who were engaged in a packaging at a detergent manufacturing company are at the greatest risk of LBP (63.76%). This was followed by packaging at a leather product manufacturing company (57.06%), packaging at a non-metallic casting material manufacturing company (57.03%), manual injection at a non-metallic casting material manufacturing company (52.00%), toggling at a leather product manufacturing company (46.09%), non-metallic casting material manufacturing company (42.88%), rolling at a non-metallic mineral product manufacturing company (42.12%), shooting at a non-metallic casting material manufacturing company (40.99%), vacuum processes at a leather product manufacturing company (35.00%), looping at a general industrial machinery manufacturing company (33.93%), setting at a leather product manufacturing company (30.22%) and packaging at a general metal product manufacturing company (22.02%).ConclusionsOur approach indicates that there is a risk of a high-risk group of LBP membership in workers who perform the MMH tasks.

Instrumented Ergonomic Risk Assessment Using Wearable Inertial Measurement Units: Impact of Joint Angle Convention

The Rapid Upper Limb Assessment (RULA) is frequently used to monitor body posture for early risk prevention of work-related musculoskeletal disorders. However, RULA measurements that are based on workers’ self-report or external rater observation suffer from low repeatability. Thus, the objective of this study was to investigate the accuracy and repeatability of an inertial measurement unit (IMU) system for in-field RULA score assessment during manual material handling tasks using 3D Cardan angles and 2D projection angles against reference values obtained by a motion-capture camera system. The experimental results showed that for trunk and neck joint angles, the 2D convention had significantly (p < 0.05) smaller root-mean-square error (RMSE), while for other upper-body angles, the convention with significantly smaller RMSE depended on the angle under analysis. Also, the 3D convention showed a “moderate” agreement with the reference system, while the 2D convention showed a “substantial” agreement for two tasks and a “moderate” agreement for one task. Moreover, the intraclass correlation coefficients ranged from 0.82 to 0.94 for the 3D convention and 0.87 to 0.95 for the 2D convention for repeated trials performed by each participant. Therefore, the wearable IMU system, along with the 2D convention, could be considered as an accurate and repeatable ergonomic risk assessment tool.

A reinforcement learning model for material handling task assignment and route planning in dynamic production logistics environment

The study analyzes the application of reinforcement learning (RL) for material handling tasks in Smart Production Logistics (SPL). It presents two contributions based on empirical results of a RL model in dynamic production logistics environment from the automotive industry. Firstly, an architecture integrating the use of RL in SPL. Secondly, the study defines various elements of RL (environment, value, state, reward, and policy) relevant for training and validating models in SPL. The study provides novel insight essential for manufacturing managers and extends current understanding related to research combining artificial intelligence and SPL, granting manufacturing companies a unique competitive advantage.

Shoulder moments and angles during single and combined manual material handling tasks

Shoulder musculoskeletal disorders due to manual material handling tasks are common workplace injuries. Here we investigated the difference in shoulder biomechanics (moments and angles) between a single task of removing a box from a shelf (or depositing a box on a shelf) and the equivalent part of a combined task that consisted of removing, carrying and depositing boxes; that is, a single removing [depositing] task was compared with the removing [depositing] part of a combined task. We found that the peak and cumulative shoulder moments were larger during the single-task paradigm than during the equivalent part of the combined task by 26.3 and 25.5%, respectively. The two paradigms also differed in terms of shoulder angles. It is likely that the main contributors to this overestimation were differences between the single and combined tasks in terms of the lever arm (i.e. horizontal distance), the shoulder angle, and the task duration. Practitioners’ Summary: We investigated shoulder moments during single and combined manual material handling tasks. Shoulder moments were found to be smaller during combined tasks. Practitioners should consider that analysing combined tasks using estimations based on single tasks could result in an overestimation of 26.3 and 25.5% in peak and cumulative shoulder moments, respectively. Abbrevaitions: MSDs: musculoskeletal disorders; MMH: manual material handling; LMM: linear mixed model

Manual material handling in the supermarket sector. Part 1: Joint angles and muscle activity of trapezius descendens and erector spinae longissimus

Work-related musculoskeletal disorders are highly prevalent in the supermarket sector with manual material handling being one of the most commonly identified occupational risk factors. This cross-sectional study applied inertial motion capture and electromyography (EMG) to measure full-body kinematics and muscle activity of trapezius descendens and erector spinae longissimus during 50 manual material handling tasks performed by 17 workers in two supermarkets. The handling of bread and cucumbers to high shelf heights showed the highest trapezius muscle activity (from 47% to 59% peak normalized EMG), while the handling of bananas as well as lifting milk, bread and cucumbers from low to high positions showed the highest erector spinae activity (from 59% to 71%). Twenty-two tasks involved flexing the shoulders and trunk more than 90° and 50°, respectively. Based on these results, several manual handling practices in supermarkets should be reconsidered to reduce the physical work demands.

Two-Dimensional Symmetric Box Delivery Motion Prediction and Validation: Subtask-Based Optimization Method

Box delivery is a complicated manual material handling task which needs to consider the box weight, delivering speed, stability, and location. This paper presents a subtask-based inverse dynamic optimization formulation for determining the two-dimensional (2D) symmetric optimal box delivery motion. For the subtask-based formulation, the delivery task is divided into five subtasks: lifting, the first transition step, carrying, the second transition step, and unloading. To render a complete delivering task, each subtask is formulated as a separate optimization problem with appropriate boundary conditions. For carrying and lifting subtasks, the cost function is the sum of joint torque squared. In contrast, for transition subtasks, the cost function is the combination of joint discomfort and joint torque squared. Joint angle profiles are validated through experimental results using Pearson’s correlation coefficient (r) and root-mean-square-error (RMSE). Results show that the subtask-based approach is computationally efficient for complex box delivery motion simulation. This research outcome provides a practical guidance to prevent injury risks in joint torque space for workers who deliver heavy objects in their daily jobs.

Investigation of 3D dynamic and quasistatic models for spinal moments during combined manual material handling tasks

Digital human modeling software uses biomechanical models to compute workers' risk of injury during industrial work processes. In many cases, the biomechanics are calculated using quasistatic models, which neglect the body's dynamics and therefore might be erroneous. This study investigated the differential effect of using a dynamic vs. a quasistatic model on spinal loading during combined manual material handling tasks that are prevalent in industry. An experiment was conducted involving nine male and nine female participants performing a total of 3402 cycles of a box-conveying task (removing, carrying and depositing) for different box masses and shelf heights. Using motion capture data, the peak and cumulative moments acting on the L5/S1 joint were calculated using 3D dynamic and quasistatic models. This revealed that neglecting the dynamic movements (i.e., using a quasistatic model) results in an on average underestimation of 19.7% in the peak spinal moment and 3.6% in the cumulative moment that in some cases exceeds the maximal limit for the compression forces acting on the lower back.

Variable stiffness soft pneumatic grippers augmented with active vacuum adhesion

Soft pneumatic grippers (SPGs), made of highly stretchable elastomer materials with internal fluidic channels, are a popular soft robotic gripping technology, and have been configured as soft bending fingers for passively compliant grasping applications. It is challenging, however, for current SPGs to grip both flat, concave, and convex surfaces. Controllable adhesion augmented SPGs are promising solutions. We aim to develop a composite gripper structure that could enable SPGs with the capability to lift flat, concave, and convex surfaces using pneumatic inputs only. To this end, we present PneuIVAVS, a layer-jamming induced active vacuum adhesion (VA) augmented and variable stiffness (VS) fiber-reinforced SPG. This integrated PneuIVAVS design has enabled the gripper to generate a VA force without the requirement that all suction units should work. Also, a VA force analytical model and a shape-locking model of the PneuIVAVS design were developed and experimentally validated. In addition, the PneuIVAVS gripper presented good performance on gripping both flat, concave and convex objects with a range of contacting areas. According to the load capacity test, the gripper could grasp a maximum of approximately 470 g, 683 g, and 268 g when handling flat, concave, and convex surfaces respectively. Besides, the gripper could grasp curved surfaces with a minimum radius of curvature between 30 and 40 mm. The PneuIVAVS concept, design, and results in this work have potential to promote the applications of SPGs and VA grippers in various material handling and robotics tasks.

A forecasting framework for predicting perceived fatigue: Using time series methods to forecast ratings of perceived exertion with features from wearable sensors

Advancements in sensing and network technologies have increased the amount of data being collected to monitor the worker conditions. In this study, we consider the use of time series methods to forecast physical fatigue using subjective ratings of perceived exertion (RPE) and gait data from wearable sensors captured during a simulated in-lab manual material handling task (Lab Study 1) and a fatiguing squatting with intermittent walking cycle (Lab Study 2). To determine whether time series models can accurately forecast individual response and for how many time periods ahead, five models were compared: naïve method, autoregression (AR), autoregressive integrated moving average (ARIMA), vector autoregression (VAR), and the vector error correction model (VECM). For forecasts of three or more time periods ahead, the VECM model that incorporates historical RPE and wearable sensor data outperformed the other models with median mean absolute error (MAE) <1.24 and median MAE <1.22 across all participants for Lab Study 1 and Lab Study 2, respectively. These results suggest that wearable sensor data can support forecasting a worker’s condition and the forecasts obtained are as good as current state-of-the-art models using multiple sensors for current time prediction.

Improving sustainability in combined manual material handling through enhanced lot-sizing models

Lot-sizing models play an important role in optimizing the performance of internal logistics systems that involve a large amount of manual material handling (MMH) tasks. The performance of MMH tasks should be assessed using multiple criteria rather than being merely cost-oriented, considering the fact that the lot size affects the workload and can result in work-related musculoskeletal disorders (WMSDs). This study aims to integrate biomechanical and physiological guidelines into a cost-based performance optimization model of MMH tasks. In this study, we consider the impact of lot size on lifting frequency, which is reflected in the Composite Lifting Index (CLI), an extension of the National Institute for Occupational Safety and Health (NIOSH). In addition, the energy expenditure rates of operators are estimated by applying an established physiological method. The energy expenditure results are used to calculate rest allowance during a handling process that help keep workload levels acceptable. To make the model more general, we consider different manual material shipping scenarios and intermodal replenishment cases. Finally, a sustainable lot-sizing model is applied in a two-stage intermodal replenishment case and several numerical experiments to determine the influence of item weight, speed, distance, and opportunity cost. The results indicate that the proposed model can ease the ergonomic strain of operators without compromising economic efficiency.

Cardiorespiratory and Thermoregulatory Parameters Are Good Surrogates for Measuring Physical Fatigue during a Simulated Construction Task

Cardiorespiratory (e.g., heart rate and breathing rate) and thermoregulatory (e.g., local skin temperature and electrodermal activity) responses are controlled by the sympathetic nervous system. To cope with increased physical workload, the sympathetic system upregulates its activity to generate greater sympathetic responses (i.e., increased heart rate and respiratory rate). Therefore, physiological measures may have the potential to evaluate changes in physical condition (including fatigue) during functional tasks. This study aimed to quantify physical fatigue using wearable cardiorespiratory and thermoregulatory sensors during a simulated construction task. Twenty-five healthy individuals (mean age, 31.8 ± 1.8 years) were recruited. Participants were instructed to perform 30 min of a simulated manual material handling task in a laboratory. The experimental setup comprised a station A, a 10-metre walking platform, and a station B. Each participant was asked to pick up a 15 kg ergonomically-designed wooden box from station A and then carried it along the platform and dropped it at station B. The task was repeated from B to A and then A to B until the participants perceived a fatigue level > 15 out of 20 on the Borg-20 scale. Heart rate, breathing rate, local skin temperature, and electrodermal activity at the wrist were measured by wearable sensors and the perceived physical fatigue was assessed using the Borg-20 scale at baseline, 15 min, and 30 min from the baseline. There were significant increases in the heart rate (mean changes: 50 ± 13.3 beats/min), breathing rate (mean changes: 9.8 ± 4.1 breaths), local skin temperature (mean changes: 3.4 ± 1.9 °C), electrodermal activity at the right wrist (mean changes: 7.1 ± 3.8 µS/cm), and subjective physical fatigue (mean changes: 8.8 ± 0.6 levels) at the end of the simulated construction task (p < 0.05). Heart rate and breathing rate at 15 and 30 min were significantly correlated with the corresponding subjective Borg scores (p < 0.01). Local skin temperature at 30 min was significantly correlated with the corresponding Borg scores (p < 0.05). However, electrodermal activity at the right wrist was not associated with Borg scores at any time points. The results implied cardiorespiratory parameters and local skin temperature were good surrogates for measuring physical fatigue. Conversely, electrodermal activity at the right wrist was unrelated to physical fatigue. Future field studies should investigate the sensitivity of various cardiorespiratory and thermoregulatory parameters for real time physical fatigue monitoring in construction sites.

Motion control of a cable-suspended robot using image recognition with coordinate transformation

This study will concern the implementation of a vision-based controller for cable-suspended robots, and especially for the pick-and-place function. This work will develop a novel algorithm that combines cable-suspended robot coordinate transformation with image recognition to manipulate the suspended gripper to the desired position for material-handling tasks. Two webcams sense the position of the end-effector; one will be used to calculate the horizontal planar coordinate of the target, and the other will be used to determine the vertical height of the target. Accordingly, the two-dimensional spatial image can be converted into three-dimensional image coordinates. Based on the sensed information, the robot can convert the position of the target to required cable lengths and then drive the servomotor to retract (or release) the cables to move the suspended gripper to the desired position. Various experiments will be conducted to verify the proposed methodology. The framework that is developed herein is easily implemented for real-time control and is suitable for inclusion in mechatronic kits in a university control course. The large variety of ideas that are addressed in this work apply to cranes, aerostats, and other large-scale manufacturing equipment that requires cable-driven robots.

SPACE MAPPING OF HIP AND WRISTS MOTIONS FOR DIFFERENT TRANSFER DISTANCES IN MANUAL MATERIAL HANDLING TASK

Manual material transfer tasks are common in occupational settings. Repetitive lifting tasks usually involve twisting and bending which are associated with occupational lower back injuries. One of the approaches to reduce bending and twisting is to separate the distance between lifting origin and destination, which will encourage lifters to step and turn entire bodies. However, adding lifting distances is likely to affect space usages and requirements. A study was conducted to investigate how the transfer distances influence space usage during the lifting task. Raw data of hip and hand wrists motion of 26 male subjects during transfer in 4 different distances were captured using X-Sens motion capture system. MVN Studio software was used to process and extract positional data. Tabulated space mapping revealed limited hip movement and semicircular shaped hand motions for short transfer distances. The pattern changes into a more stretched-curve shape as the distance increases. Overall, it was observed that shorter transfer distance caused participants to adopt more twisting and less bending postures, while further transfer distances resulted in more bending and less twisting. This study may provide industrial practitioners with information to design a space requirement for manual material transfer tasks. ABSTRAK: Kerja-kerja pemindahan barang secara manual adalah biasa dalam persekitaran kerja. Kerja-kerja mengangkat barang yang kebiasaannya melibatkan badan membengkok dan berpusing boleh menyebabkan kecederaan tulang belakang. Salah satu cara bagi mengurangkan risiko ini adalah dengan memisahkan jarak antara tempat asal dan tempat tuju pemindahan barang. Pemindah barang digalakkan untuk melangkah dan memusingkan seluruh badan. Namun, cara ini menyebabkan penggunaan ruang yang banyak. Satu kajian telah dijalankan bagi mengkaji bagaimana jarak pemindahan barang mempengaruhi penggunaan ruang ketika kerja-kerja pemindahan. Data asal pergerakan pinggul dan pergelangan tangan daripada 26 subjek lelaki ketika pemindahan barang pada 4 jarak berbeza diperolehi menggunakan sistem rakaman gerakan X-Sens. Perisian MVN Studio digunakan bagi memproses dan mengekstrak data ini. Ruang pemetaan berjadual mendedahkan pergerakan pinggul yang terhad dan pergerakan tangan berbentuk separa bulat pada jarak pemindahan terdekat. Corak ini berubah kepada bentuk lengkung memanjang apabila jarak bertambah. Keseluruhannya, jarak pindahan yang kurang menyebabkan para peserta lebih terdedah kepada postur memusingkan badan berbanding membengkok, sementara jarak yang jauh menyebabkan peserta lebih membengkok berbanding memusingkan badan. Kajian ini memberikan maklumat untuk penggiat industri mereka cipta keperluan ruang bagi kerja-kerja pemindahan barang secara manual.

Effects of Manual Material Handling Workload on Measures of Fall Risk

OCCUPATIONAL APPLICATIONS We found, contrary to expectations, that performing a fatiguing simulated heavy manual material handling (MMH) task did not adversely affect the risk of trip-induced falls when compared to a less-fatiguing light MMH task. However, when considering these MMH tasks together rather than in comparison, our results provide evidence for adverse effects of fatigue on both gait and the ability to recover balance after tripping. The current results provide additional evidence that physical fatigue increases fall risk, start to clarify the mechanisms by which this increase occurs, and can help in developing and evaluating fall prevention strategies targeting these mechanisms.

Activation of pelvic floor, lumbar and abdominal musculature during a simulated manual material handling task: a cross-sectional study

ABSTRACT Pelvic floor muscles act synergistically with the abdominal and lumbar muscles contributing to spine and pelvic control. These muscles are activated during activities that increase intra-abdominal pressure, such as manual material handling. The aim of our study was to assess the electrical activity of the lumbar, abdominal and pelvic floor muscles during manual material handling with different loads. This is a cross-sectional study with sixteen nulliparous continent women aged between 18 and 35 years. An electromyographic system was used to evaluate the activation of the multifidus, erector spinal (iliocostal) and abdominal rectus muscles bilaterally (Trigno Wireless®, DelSys®, Boston, USA) and another for the pelvic floor muscles (Thought Technology Ltd, Canadá). Electromyographic data were collected during manual handling of three loads: light (1.5 kg), medium (4.5 kg) and heavy (11.3 kg). Repeated measures ANOVA was applied to compare the activation among loads at a 5% level of significance (α = 0.05). There was a significant increase in the activation of the lumbar and abdominal musculature as the load increases. No difference among loads was found for the pelvic floor muscle activation. Pelvic floor muscles did not increase their activation in function of the load, as occur for the lumbar and abdominal muscles in nulliparous continent women. These findings need to be confirmed for incontinent woman, since it could have clinical implications for designing both occupational tasks and pelvic floor rehabilitation.

Measuring Biomechanical Risk in Lifting Load Tasks Through Wearable System and Machine-Learning Approach.

Ergonomics evaluation through measurements of biomechanical parameters in real time has a great potential in reducing non-fatal occupational injuries, such as work-related musculoskeletal disorders. Assuming a correct posture guarantees the avoidance of high stress on the back and on the lower extremities, while an incorrect posture increases spinal stress. Here, we propose a solution for the recognition of postural patterns through wearable sensors and machine-learning algorithms fed with kinematic data. Twenty-six healthy subjects equipped with eight wireless inertial measurement units (IMUs) performed manual material handling tasks, such as lifting and releasing small loads, with two postural patterns: correctly and incorrectly. Measurements of kinematic parameters, such as the range of motion of lower limb and lumbosacral joints, along with the displacement of the trunk with respect to the pelvis, were estimated from IMU measurements through a biomechanical model. Statistical differences were found for all kinematic parameters between the correct and the incorrect postures (p < 0.01). Moreover, with the weight increase of load in the lifting task, changes in hip and trunk kinematics were observed (p < 0.01). To automatically identify the two postures, a supervised machine-learning algorithm, a support vector machine, was trained, and an accuracy of 99.4% (specificity of 100%) was reached by using the measurements of all kinematic parameters as features. Meanwhile, an accuracy of 76.9% (specificity of 76.9%) was reached by using the measurements of kinematic parameters related to the trunk body segment.

A Review of Recent Advances in Automated Guided Vehicle Technologies: Integration Challenges and Research Areas for 5G-Based Smart Manufacturing Applications

In industrial environments, over several decades, Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs) have served to improve efficiencies of intralogistics and material handling tasks. However, for system integrators, the choice and effective deployment of improved, suitable and reliable communication and control technologies for these unmanned vehicles remains a very challenging task. Specifics of communication for AGVs and AMRs imposes stringent performance requirements on latency and reliability of communication links which many existing wireless technologies struggle to satisfy. In this paper, a review of latest AGVs and AMRs research results in the past decade is presented. The review encompasses results from different past and present research domains of AGVs. In addition, performance requirements of communication networks in terms of their latencies and reliabilities when they are deployed for AGVs and AMRs coordination, control and fleet management in smart manufacturing environments are discussed. Integration challenges and limitations of present state-of-the-art AGV and AMR technologies when those technologies are used for facilitating AGV-based smart manufacturing and factory of the future applications are also thoroughly discussed. The paper also present a thorough discussion of areas in need of further research regarding the application of 5G networks for AGVs and AMRs fleet management in smart manufacturing environments. In addition, novel integration ideas by which tactile Internet, 5G network slicing and virtual reality applications can be used to facilitate AGV and AMR based factory of the future (FoF) and smart manufacturing applications were motivated.

Optimization of Waste Smoke Recovery Scheduling Strategy Based on Multi AGV

AGV is an important material handling equipment for flexible, lean and intelligent production of smart factory. Because of the high cost of AGV car and the congestion of vehicles in the system, this chapter studies the waste smoke recovery path planning and material handling task scheduling strategy of multi AGV system to improve the waste smoke recovery efficiency of AGV.

Workforce development: understanding task-level job demands-resources, burnout, and performance in unskilled construction workers

This study examines how task demands and personal resources affect unskilled construction worker productivity and safety performance. It extends the job demands-resources (JD-R) burnout model to show how job characteristics interact with burnout to influence performance. A modified model was designed to measure burnout, with exhaustion and disengagement among unskilled construction workers taken into consideration. An observational study was conducted in a laboratory environment to test the research hypotheses and assess the prediction accuracies of outcome constructs. Twenty-two subjects participated in multiple experiments designed to expose them to varying levels of task-demands and to record their personal resources as they performed common construction material-handling tasks. Specifically, both surveys and physiological measurements using wearable sensors were used to operationalize the model constructs. Moreover, partial least squares structural equation modeling was applied to analyze data collected at the task and individual levels. Exhaustion and disengagement exhibited different relationships with productivity and safety performance outcomes as measured by unit rate productivity and ergonomic behavior, respectively. Subjects with high burnout and high engagement showed high productivity but low safety performance. Thus, exhausted workers stand a greater chance of failing to comply with safety. As the sample and the task performed in the experiment do not cover the experience and trade of all construction workers, our findings are limited in their application to entry-level and unskilled workers, whose work is mainly manual material-handling tasks.

Mapping Center Of Pressure during Standing Reach Tasks

Postural stability and balance during manual material handling and industrial tasks are fundamental to ergonomic assessment of workplace tasks. Previous research has determined that accurate prediction of a person’s balance maintenance strategy is one of the most important parameters affecting the accuracy of posture prediction algorithms. Digital human modeling has the potential to provide designers with accurate tools to represent human posture, but currently available software typically lacks empirically-derived models of center of pressure (CoP) excursion. This paper presents an overview of a study that systematically quantified CoP excursion behavior through a series of standing reach tasks for participants with a wide range of body size. CoP excursion was greatly affected by foot placement and target location. The overall goal of this research is to develop an empirical model of center of pressure (CoP) excursion that can be integrated into human figure modeling software to improve prediction of standing postures typically observed in industrial tasks.