Material Handling Tasks Research Articles

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.

Gait Learning for Hexapod Robot Facing Rough Terrain Based on Dueling-DQN Algorithm

In the handling of dangerous goods in explosive environments, robots are increasingly being used instead of human operators. Robots designed for operation in explosive environments are mostly equipped with tracked structures, which, due to their limited terrain adaptability, struggle to movement rugged landscapes. Hexapod robots, with their excellent maneuverability and adaptability, possess advantages in completing hazardous material handling tasks in such rugged terrains. One current challenge lies in enabling hexapod robots to autonomously adjust their gaits to cope with rugged terrain. This paper proposes a gait learning method based on the Dueling Deep Q-Network (Dueling-DQN) algorithm to address the gait adjustment problem of hexapod robots in sloped, terraced, and rugged terrain. The method combines lidar data and reinforcement learning to extract features from the lidar data to determine terrain types and foot coordinates. Finally, the Dueling-DQN algorithm and redundant phase strategy are employed to facilitate the motion of hexapod robots in these three types of rugged terrain. Simulation and prototype experiments are conducted to evaluate the Dueling-DQN algorithm's performance in terms of rewards and stability margins for the three types of terrain. During algorithm training on sloped, terraced, and rugged terrain, stable rewards and positive stability margins are achieved after approximately 490 iterations. The effectiveness and feasibility of the proposed method are further validated through Gazebo simulation and prototype experiments. In the context of movement in rugged terrain within explosive environments, the gait learning method based on the Dueling-DQN algorithm offers valuable insights into the control of hexapod robots.

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.

Trajectory Tracking Control of Fast Parallel SCARA Robots with Fuzzy Adaptive Iterative Learning Control for Repetitive Pick-and-Place Operations

Aiming at enhanced suppression of external disturbances and high-precision trajectory tracking of parallel SCARA robot dedicating to fast pick-and-place operations, this work presents the integrated control design of iterative learning algorithm, adaptive control and fuzzy rules, namely, fuzzy adaptive iterative learning control, for such type of robots. A step-design approach is adopted to ensure the adaptability of the designed control law, which is reflected in two aspects: ① the feedback gain of the controller is adjusted by the fuzzy rules; ② the adaptive unknown parameters are obtained by means of iterative learning estimation to suppress the uncertainties and external disturbances. The stability of the designed controller is analyzed and proved by the Lyapunov theory, and the effectiveness is verified by observing the tracking errors in joint space along with the testing pick path, in comparison with different iterative learning based algorithms. After the first-iteration learning, the motion errors of the four actuated joints can be reduced by 56.5%, 45.8%, 46.4% and 39.8%, respectively, and after 15 iterations of learning control, the final angular errors by the designed control law converge to 0.7×10−4 degree maximally. The varying maximum, root-mean-squared and mean angular displacement errors of the actuation joints can converge to zero values with the increasing iterations rapidly, which shows the robustness, effectiveness and advantages of the designed control law. The designed control law can be generalized to high-speed parallel pick-and-place robot to ensure high-precision trajectory tracking for high-quality material handling tasks.

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.

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.

Exploring the Addition of Torso Flexion to the LIFFT Analysis Tool

The Lifting Fatigue Failure Tool (LiFFT) is an ergonomic assessment tool based on fatigue failure theory that uses the lower back load moment to evaluate the risk associated with multi-task jobs involving manual lifting. The current LiFFT tool does not account for the moment associated with flexing the lifter's torso. This study explores the incorporation of torso flexion into the LiFFT model while maintaining the relative simplicity of the original LiFFT tool. Automotive manufacturing workers (n=607) performing various tasks were included in the study. Non-manual material handling (MMH) tasks with no MMH load moment were considered "zero" risk. The moment associated with trunk flexion was considered if a worker flexed at the torso during non-MMH assembly tasks. The torso moment from bending was computed using the "average" worker height and weight for the data set used in this study. The proposed model yielded higher odds ratios than the original model.

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.

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.

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.

Energy Efficiency of AGV-Drone Joint In-Plant Supply of Production Lines

Energy efficiency plays an increasingly important role not only in supply chains, but also in in-plant supply systems. Manufacturing companies are increasingly using energy-efficient material handling equipment to solve their in-plant material handling tasks. A new example of this effort is the use of drones for in-plant transportation of small components. Within the frame of this article, a new AGV-drone joint in-plant supply model is described. The joint service of AGV-based milkrun trolleys and drones makes it possible to optimize the in-plant supply in production lines. This article discusses the mathematical description of AGV-drone joint in-plant supply solutions. The numerical analysis of the different AGV-drone joint in-plant supply solutions shows that this new approach can lead to an energy consumption reduction of about 30%, which also has a significant impact on GHG emission.

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

IMPROVING HUMAN-ROBOT PHYSICAL INTERACTION COMFORT IN MATERIAL HANDLING TASKS USING A SMART PLATFORM

The use of mobile platforms can help employees automate manual processes and streamline operations to save time and perform their tasks safely and accurately. A power-assisted vehicle to move weight around the place – solution: inexpensive, easy to apply, reliable, safe. It can adjust to various tasks, operators’ gait, loads up to 500 kg. It is a relatively inexpensive, easy-to-apply, reliable, and safe solution for moving weight. The motivation of the study is to increase efficiency and reduce physical strain on the operator in material handling tasks and to promote the implementation of this smart platform. Artificial intelligence learning methods are applied to adapt to individual operator’s experience, resulting in a personalized and more comfortable interaction with the help of Q-learning algorithm with 256 learning outcomes in adjusting controller settings: damping, mass, stiffness.