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This paper is devoted to presenting the results of the computer-aided design, the multibody dynamic analysis, and the proportional-derivative control synthesis of an adaptive mechanism serving as a lifting table. More specifically, the first part of the manuscript deals with the methodological approach and the mathematical background employed in the entire research work, whereas the second part of the present paper is focused on the model development and the numerical experiments carried out in this investigation. The analytical derivations presented herein demonstrate that the desired adaptive behavior can be successfully obtained for the virtual prototype of the lift mechanism devised in this investigation. Furthermore, a detailed CAD model of the proposed design was constructed in this investigation using SOLIDWORKS. To this end, particular attention was paid to the actual assembly and disassembly of each mechanical component, in conjunction with the choice of the actuators and sensors that are necessary for the proper functioning of the lifting mechanism. Then, a three-dimensional multibody model was developed starting from the CAD model of the virtual prototype devised in this investigation. The resulting multibody model, following appropriate simplifications, was subsequently imported into the MATLAB virtual environment, thereby allowing for readily performing kinematic and dynamic simulations of the nonlinear behavior of the mechanical system under study by using the SIMSCAPE MULTIBODY computational software. By doing so, the development of an appropriate control strategy, as well as the analysis of its behavior under loading and unloading goods conditions, was carried out and tested in the case of four different scenarios considered as the case study. For this purpose, the applicative scenarios considered are: 1) an impulsive loading scenario; 2) a progressive loading scenario; 3) an impulsive unloading scenario; 4) and a progressive unloading scenario. The performance of the feedforward plus feedback control strategy devised in this study was discussed based on several computer simulations. Numerical simulations demonstrate that the desired adaptive behavior is successfully obtained for the virtual prototype of the lift table designed in this study. [ABSTRACT FROM AUTHOR]

Featured Application: This paper proposes a protocol for researchers, ergonomists, and industrial practitioners to design and carry out well-structured experiments that allow for reliable and valid comparisons between different exoskeleton designs or configurations, considering factors such as user characteristics, task demands, environmental conditions, and subjective user perceptions. This paper proposes a modular protocol for the designing of experimental studies to analyze exoskeletons used in industrial settings to support manual material handling (MMH). Despite exoskeleton technologies starting to be highly commercialized and present in workplaces, research still lacks a standardized procedure for analyzing the impact of these devices on workers. The protocol presented in this paper outlines a step-by-step procedure, including the parameters to be collected and analyzed in a research study. Moreover, the approach could be easily adapted to meet the specificity of a wide range of exoskeletons. The main novelty of the protocol is thus to support the experimental design and analysis of studies assessing the overall impact of exoskeletons on workers. To implement the protocol, the selected case study concerned a palletizing task involving the MMH of 12 cardboard boxes, weighing 10 kg. The results from physiological signals and pressure insoles show that the protocol is comprehensive and can be utilized by researchers evaluating different occupational exoskeletons for assistance during MMH (both active and passive), with modifications to specific parts based on the type of exoskeleton being assessed or the variables of interest. [ABSTRACT FROM AUTHOR]

It is not possible to directly measure the reaction forces and moments acting on the joints in the living things without damaging the integrity of the body. L5/S1 joint in the human body have a higher damage potential than other joints. In this study, it is aimed to develop a method that can calculate the net reaction forces and moments acting on the L5/S1 and upper extremity joints by using only kinematic measurement inputs, without the need for force measurement from the points where the feet contact the ground. For this purpose, 3D biomechanical model suitable for human anatomy was created. Kinematic analyzes were performed using the Denavit–Hartenberg (DH) method. Iterative Newton–Euler (NE) method was used for inverse dynamic calculations. The kinematic measurement inputs of joint movements were obtained by the subject in an experimental study involving the lifting task a certain load from the ground and lowering it to a high place. The model was verified by comparing the results obtained using the proposed method with each other and with the literature data. [ABSTRACT FROM AUTHOR]

In the Industry 4.0 scenario, human–robot collaboration (HRC) plays a key role in factories to reduce costs, increase production, and help aged and/or sick workers maintain their job. The approaches of the ISO 11228 series commonly used for biomechanical risk assessments cannot be applied in Industry 4.0, as they do not involve interactions between workers and HRC technologies. The use of wearable sensor networks and software for biomechanical risk assessments could help us develop a more reliable idea about the effectiveness of collaborative robots (coBots) in reducing the biomechanical load for workers. The aim of the present study was to investigate some biomechanical parameters with the 3D Static Strength Prediction Program (3DSSPP) software v.7.1.3, on workers executing a practical manual material-handling task, by comparing a dual-arm coBot-assisted scenario with a no-coBot scenario. In this study, we calculated the mean and the standard deviation (SD) values from eleven participants for some 3DSSPP parameters. We considered the following parameters: the percentage of maximum voluntary contraction (%MVC), the maximum allowed static exertion time (MaxST), the low-back spine compression forces at the L4/L5 level (L4Ort), and the strength percent capable value (SPC). The advantages of introducing the coBot, according to our statistics, concerned trunk flexion (SPC from 85.8% without coBot to 95.2%; %MVC from 63.5% without coBot to 43.4%; MaxST from 33.9 s without coBot to 86.2 s), left shoulder abdo-adduction (%MVC from 46.1% without coBot to 32.6%; MaxST from 32.7 s without coBot to 65 s), and right shoulder abdo-adduction (%MVC from 43.9% without coBot to 30.0%; MaxST from 37.2 s without coBot to 70.7 s) in Phase 1, and right shoulder humeral rotation (%MVC from 68.4% without coBot to 7.4%; MaxST from 873.0 s without coBot to 125.2 s), right shoulder abdo-adduction (%MVC from 31.0% without coBot to 18.3%; MaxST from 60.3 s without coBot to 183.6 s), and right wrist flexion/extension rotation (%MVC from 50.2% without coBot to 3.0%; MaxST from 58.8 s without coBot to 1200.0 s) in Phase 2. Moreover, Phase 3, which consisted of another manual handling task, would be removed by using a coBot. In summary, using a coBot in this industrial scenario would reduce the biomechanical risk for workers, particularly for the trunk, both shoulders, and the right wrist. Finally, the 3DSSPP software could be an easy, fast, and costless tool for biomechanical risk assessments in an Industry 4.0 scenario where ISO 11228 series cannot be applied; it could be used by occupational medicine physicians and health and safety technicians, and could also help employers to justify a long-term investment. [ABSTRACT FROM AUTHOR]

This paper is devoted to presenting the results of the computer-aided design, the multibody dynamic analysis, and the proportional-derivative control synthesis of an adaptive mechanism serving as a lifting table. More specifically, the first part of the manuscript deals with the methodological approach and the mathematical background employed in the entire research work, whereas the second part of the present paper is focused on the model development and the numerical experiments carried out in this investigation. The analytical derivations presented herein demonstrate that the desired adaptive behavior can be successfully obtained for the virtual prototype of the lift mechanism devised in this investigation. Furthermore, a detailed CAD model of the proposed design was constructed in this investigation using SOLIDWORKS. To this end, particular attention was paid to the actual assembly and disassembly of each mechanical component, in conjunction with the choice of the actuators and sensors that are necessary for the proper functioning of the lifting mechanism. Then, a three-dimensional multibody model was developed starting from the CAD model of the virtual prototype devised in this investigation. The resulting multibody model, following appropriate simplifications, was subsequently imported into the MATLAB virtual environment, thereby allowing for readily performing kinematic and dynamic simulations of the nonlinear behavior of the mechanical system under study by using the SIMSCAPE MULTIBODY computational software. By doing so, the development of an appropriate control strategy, as well as the analysis of its behavior under loading and unloading goods conditions, was carried out and tested in the case of four different scenarios considered as the case study. For this purpose, the applicative scenarios considered are: 1) an impulsive loading scenario; 2) a progressive loading scenario; 3) an impulsive unloading scenario; 4) and a progressive unloading scenario. The performance of the feedforward plus feedback control strategy devised in this study was discussed based on several computer simulations. Numerical simulations demonstrate that the desired adaptive behavior is successfully obtained for the virtual prototype of the lift table designed in this study.

Introduction: Lifting loads in awkward postures is a main cause of low back musculoskeletal disorders. In this context, researchers have used various indicators to determine the relationship between biomechanical variables and the risk of these disorders. This study aimed to investigate the correlation between plantar pressure distribution and the values of UTAH back-compressive forces (BCF) and lifting index (LI) during symmetrical load-lifting tasks. Material and Methods: Thirteen healthy men, aged 25 to 35, took part in this study. The participants were instructed to symmetrically lift loads weighing 7.5 kg and 15 kg in 15 different postures, considering three horizontal distances (A, B, C) and five different heights (1-5). Pressure on the foot soles was recorded using 16 force-sensitive resistors (FSR) corresponding to eight anatomical areas on each foot. The BCF and LI were also calculated using the UTAH method and the NIOSH equation, respectively. Statistical analysis was performed using SPSS (version 21) software. Results: Based on the results, when the load was closest to the body (A1-A5), the highest pressure was recorded in the heel and the 4th and 5th metatarsal of both feet. In lifting a load of 15 kg in the A2, B1, B2, C1, C2 postures and lifting a load of 7.5 kg in the C2 posture, the average BCF exceeded 700 pounds. The LI was greater than 1 for specific postures (B1, B2, B4, B5, C1-C5) at 15 kg and (C1, C2, C4, C5) at 7.5 kg load-lifting. During the 7.5 kg and 15 kg load-lifting, there was a significant correlation between the plantar pressure and the values of LI and UTAH (p-values < 0.05) in most postures. Conclusion: The results showed a significant correlation between plantar pressure distribution and load-lifting postures. The study findings, which identify risk levels associated with lifting postures, lay the groundwork for future research aimed at categorizing safe and unsafe plantar pressure patterns.

The three Ground Reaction Force (GRF) components can be estimated using pressure insole sensors. In this paper, we compare the accuracy of estimating GRF components for both feet using six methods: three Deep Learning (DL) methods (Artificial Neural Network, Long Short-Term Memory, and Convolutional Neural Network) and three Supervised Machine Learning (SML) methods (Least Squares, Support Vector Regression, and Random Forest (RF)). Data were collected from nine subjects across six activities: normal and slow walking, static with and without carrying a load, and two Manual Material Handling activities. This study has two main contributions: first, the estimation of GRF components (Fx, Fy, and Fz) during the six activities, two of which have never been studied; second, the comparison of the accuracy of GRF component estimation between the six methods for each activity. RF provided the most accurate estimation for static situations, with mean RMSE values of RMSE_Fx = 1.65 N, RMSE_Fy = 1.35 N, and RMSE_Fz = 7.97 N for the mean absolute values measured by the force plate (reference) RMSE_Fx = 14.10 N, RMSE_Fy = 3.83 N, and RMSE_Fz = 397.45 N. In our study, we found that RF, an SML method, surpassed the experimented DL methods. [ABSTRACT FROM AUTHOR]

Featured Application: This study explores the integration of an occupational passive back-support exoskeleton in manufacturing, demonstrating its potential to reduce physical discomfort and physical exertion and enhance ergonomic practices. It highlights the role of the exoskeleton as a proactive tool for improving occupational health and safety in industrial settings. 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. [ABSTRACT FROM AUTHOR]

The central nervous system uses muscle co-activation for body coordination, effector movement control, and joint stabilization. However, co-activation increases compression and shear stresses on the joints. Lifting activity is one of the leading causes of work-related musculoskeletal problems worldwide, and it has been shown that when the risk level rises, lifting enhances trunk muscle co-activation at the L5/S1 level. This study aims to investigate the co-activation of lower limb muscles during liftings at various risk levels and lifting types (one-person and vs. two-person team lifting), to understand how the central nervous system governs lower limb rigidity during these tasks. The surface electromyographic signal of thirteen healthy volunteers (seven males and six females, age range: 29–48 years) was obtained over the trunk and right lower limb muscles while lifting in the sagittal plane. Then co-activation was computed according to different approaches: global, full leg, flexor, extensor, and rostro-caudal. The statistical analysis revealed a significant increase in the risk level and a decrease in the two-person on the mean and/or maximum of the co-activation in almost all the approaches. Overall, our findings imply that the central nervous system streamlines the motor regulation of lifting by increasing or reducing whole-limb rigidity within a distinct global, extensor, and rostro-caudal co-activation scheme, depending on the risk level/lifting type. [ABSTRACT FROM AUTHOR]

When performing lifting tasks at work, the Lifting Index (LI) is widely used to prevent work-related low-back disorders, but it presents criticalities pertaining to measurement accuracy and precision. Wearable sensor networks, such as sensorized insoles and inertial measurement units, could improve biomechanical risk assessment by enabling the computation of an adaptive LI (aLI) that changes over time in relation to the actual method of carrying out lifting. This study aims to illustrate the concepts and mathematics underlying aLI computation and compare aLI calculations in real-time using wearable sensors and force platforms with the LI estimated with the standard method used by ergonomists and occupational health and safety technicians. To reach this aim, 10 participants performed six lifting tasks under two risk conditions. The results show us that the aLI value rapidly converges towards the reference value in all tasks, suggesting a promising use of adaptive algorithms and instrumental tools for biomechanical risk assessment. [ABSTRACT FROM AUTHOR]

In the Industry 4.0 scenario, human–robot collaboration (HRC) plays a key role in factories to reduce costs, increase production, and help aged and/or sick workers maintain their job. The approaches of the ISO 11228 series commonly used for biomechanical risk assessments cannot be applied in Industry 4.0, as they do not involve interactions between workers and HRC technologies. The use of wearable sensor networks and software for biomechanical risk assessments could help us develop a more reliable idea about the effectiveness of collaborative robots (coBots) in reducing the biomechanical load for workers. The aim of the present study was to investigate some biomechanical parameters with the 3D Static Strength Prediction Program (3DSSPP) software v.7.1.3, on workers executing a practical manual material-handling task, by comparing a dual-arm coBot-assisted scenario with a no-coBot scenario. In this study, we calculated the mean and the standard deviation (SD) values from eleven participants for some 3DSSPP parameters. We considered the following parameters: the percentage of maximum voluntary contraction (%MVC), the maximum allowed static exertion time (MaxST), the low-back spine compression forces at the L4/L5 level (L4Ort), and the strength percent capable value (SPC). The advantages of introducing the coBot, according to our statistics, concerned trunk flexion (SPC from 85.8% without coBot to 95.2%; %MVC from 63.5% without coBot to 43.4%; MaxST from 33.9 s without coBot to 86.2 s), left shoulder abdo-adduction (%MVC from 46.1% without coBot to 32.6%; MaxST from 32.7 s without coBot to 65 s), and right shoulder abdo-adduction (%MVC from 43.9% without coBot to 30.0%; MaxST from 37.2 s without coBot to 70.7 s) in Phase 1, and right shoulder humeral rotation (%MVC from 68.4% without coBot to 7.4%; MaxST from 873.0 s without coBot to 125.2 s), right shoulder abdo-adduction (%MVC from 31.0% without coBot to 18.3%; MaxST from 60.3 s without coBot to 183.6 s), and right wrist flexion/extension rotation (%MVC from 50.2% without coBot to 3.0%; MaxST from 58.8 s without coBot to 1200.0 s) in Phase 2. Moreover, Phase 3, which consisted of another manual handling task, would be removed by using a coBot. In summary, using a coBot in this industrial scenario would reduce the biomechanical risk for workers, particularly for the trunk, both shoulders, and the right wrist. Finally, the 3DSSPP software could be an easy, fast, and costless tool for biomechanical risk assessments in an Industry 4.0 scenario where ISO 11228 series cannot be applied; it could be used by occupational medicine physicians and health and safety technicians, and could also help employers to justify a long-term investment.

This paper proposes a modular protocol for the designing of experimental studies to analyze exoskeletons used in industrial settings to support manual material handling (MMH). Despite exoskeleton technologies starting to be highly commercialized and present in workplaces, research still lacks a standardized procedure for analyzing the impact of these devices on workers. The protocol presented in this paper outlines a step-by-step procedure, including the parameters to be collected and analyzed in a research study. Moreover, the approach could be easily adapted to meet the specificity of a wide range of exoskeletons. The main novelty of the protocol is thus to support the experimental design and analysis of studies assessing the overall impact of exoskeletons on workers. To implement the protocol, the selected case study concerned a palletizing task involving the MMH of 12 cardboard boxes, weighing 10 kg. The results from physiological signals and pressure insoles show that the protocol is comprehensive and can be utilized by researchers evaluating different occupational exoskeletons for assistance during MMH (both active and passive), with modifications to specific parts based on the type of exoskeleton being assessed or the variables of interest.

This study aims at evaluating upper limb muscle coordination and activation in workers performing an actual use-case manual material handling (MMH). The study relies on the comparison of the workers' muscular activity while they perform the task, with and without the help of a dual-arm cobot (BAZAR). Eleven participants performed the task and the flexors and extensors muscles of the shoulder, elbow, wrist, and trunk joints were recorded using bipolar electromyography. The results showed that, when the particular MMH was carried out with BAZAR, both upper limb and trunk muscular co-activation and activation were decreased. Therefore, technologies that enable human-robot collaboration (HRC), which share a workspace with employees, relieve employees of external loads and enhance the effectiveness and calibre of task completion. Additionally, these technologies improve the worker's coordination, lessen the physical effort required to interact with the robot, and have a favourable impact on his or her physiological motor strategy. Practitioner summary: Upper limb and trunk muscle co-activation and activation is reduced when a specific manual material handling was performed with a cobot than without it. By improving coordination, reducing physical effort, and changing motor strategy, cobots could be proposed as an ergonomic intervention to lower workers' biomechanical risk in industry. [ABSTRACT FROM AUTHOR]

The three Ground Reaction Force (GRF) components can be estimated using pressure insole sensors. In this paper, we compare the accuracy of estimating GRF components for both feet using six methods: three Deep Learning (DL) methods (Artificial Neural Network, Long Short-Term Memory, and Convolutional Neural Network) and three Supervised Machine Learning (SML) methods (Least Squares, Support Vector Regression, and Random Forest (RF)). Data were collected from nine subjects across six activities: normal and slow walking, static with and without carrying a load, and two Manual Material Handling activities. This study has two main contributions: first, the estimation of GRF components (Fx, Fy, and Fz) during the six activities, two of which have never been studied; second, the comparison of the accuracy of GRF component estimation between the six methods for each activity. RF provided the most accurate estimation for static situations, with mean RMSE values of RMSE_Fx = 1.65 N, RMSE_Fy = 1.35 N, and RMSE_Fz = 7.97 N for the mean absolute values measured by the force plate (reference) RMSE_Fx = 14.10 N, RMSE_Fy = 3.83 N, and RMSE_Fz = 397.45 N. In our study, we found that RF, an SML method, surpassed the experimented DL methods.

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.

BACKGROUND: Indian farmers have musculoskeletal disorders as agriculture is a physically demanding occupation. OBJECTIVES: The main aim of this study is to identify ergonomic and psychosocial risk factors associated with musculoskeletal disorders (MSDs) among different groups of farmers. The other objective is to investigate how MSDs affect farmers' daily lives and interfere with their activities. METHODS: Farmers were evaluated for the repetitiveness of work and MSDs using the standard Nordic questionnaire, ergonomic assessment tool (REBA), and ART tool. RESULTS: Low back portions were the most affected body parts, followed by the knees (OR = 0.352; 95% CI = 0.280–0.598), shoulder (OR = 0.657; 95% CI = 0.405–1.066), neck (OR = 0.510; 95% CI = 0.350–0.743), ankle or foot (OR = 0.815; 95% CI = 0.556–1.196), and upper back region (OR = 0.681; 95% CI = 0.463–1.002). The REBA method found that most of the postures in farming are very dangerous. The ART tool showed that most of the activities are repetitive. Working long hours (90%) and monotony at work (87.5%) are the main psychosocial factors, followed by pressure to finish within a given timeframe (71.4%) and injuries related to MSDs. CONCLUSION: Farmers suffer from musculoskeletal disorders in various body parts (lower back, knee, neck, shoulder, and upper back) due to squatting, stooping, and other constrained working postures during agricultural activities. rolonged working hours, repetitive movements, and MMH are ergonomic risk factors forFurthermore, p MSDs. [ABSTRACT FROM AUTHOR]

The central nervous system uses muscle co-activation for body coordination, effector movement control, and joint stabilization. However, co-activation increases compression and shear stresses on the joints. Lifting activity is one of the leading causes of work-related musculoskeletal problems worldwide, and it has been shown that when the risk level rises, lifting enhances trunk muscle co-activation at the L5/S1 level. This study aims to investigate the co-activation of lower limb muscles during liftings at various risk levels and lifting types (one-person and vs. two-person team lifting), to understand how the central nervous system governs lower limb rigidity during these tasks. The surface electromyographic signal of thirteen healthy volunteers (seven males and six females, age range: 29–48 years) was obtained over the trunk and right lower limb muscles while lifting in the sagittal plane. Then co-activation was computed according to different approaches: global, full leg, flexor, extensor, and rostro-caudal. The statistical analysis revealed a significant increase in the risk level and a decrease in the two-person on the mean and/or maximum of the co-activation in almost all the approaches. Overall, our findings imply that the central nervous system streamlines the motor regulation of lifting by increasing or reducing whole-limb rigidity within a distinct global, extensor, and rostro-caudal co-activation scheme, depending on the risk level/lifting type.

When performing lifting tasks at work, the Lifting Index (LI) is widely used to prevent work-related low-back disorders, but it presents criticalities pertaining to measurement accuracy and precision. Wearable sensor networks, such as sensorized insoles and inertial measurement units, could improve biomechanical risk assessment by enabling the computation of an adaptive LI (aLI) that changes over time in relation to the actual method of carrying out lifting. This study aims to illustrate the concepts and mathematics underlying aLI computation and compare aLI calculations in real-time using wearable sensors and force platforms with the LI estimated with the standard method used by ergonomists and occupational health and safety technicians. To reach this aim, 10 participants performed six lifting tasks under two risk conditions. The results show us that the aLI value rapidly converges towards the reference value in all tasks, suggesting a promising use of adaptive algorithms and instrumental tools for biomechanical risk assessment.

Introduction: In India, physical manual activities in asymmetrical postures overtax the human musculoskeletal system, which may exceed workers' physical limitations. Thus the purpose of this study was to examine the physical stresses experienced by the subject, based on subjective and biomechanical loading estimates while lifting weights to various heights, in an asymmetric direction and propose the safe limit for manual lifting. Methods: A laboratory experiment was conducted utilizing twelve male subjects in the age group of 20 to 25 years who lifted 5 different weights between 10 to 20 kg from below the knee to various lifting heights (below the knee to ear level). The lifting task was performed in three asymmetric angles (45, 90, and 135-degree) using free-style lifting techniques. An ANOVA technique was used to analyze the influence of three parameters (Lifting weight, lifting height and asymmetric angle) on two responses; subjective estimates and biomechanical loading. The subjective estimate was obtained using workload assessment by body discomfort chart. The biomechanical loading (loading rate) was estimated from ground reaction force data, obtained from the force plate. Results: Both the responses; subjective estimates and biomechanical loading followed a consistent pattern in predicting physical stress. The result revealed that lifting weights with higher destination heights and asymmetry angles increased the physiological workload and discomfort. Experiments have shown that the loading rate is reduced by 8 to 10% for each increase in the 45-degree angle of asymmetry. Conclusion: In general, safe lifting of 15 kg weight up to ear level and 15 kg weight up to shoulder level are recommended for 45- and 90-degree asymmetry respectively to prevent any chronic injuries. A maximum of 12.5 kg lifting weight up to shoulder level is also proposed.

Management of heat stress and metabolic cost is vital for preventing any work-related disorders. In this paper, we integrated rest time formulations for heat strain and metabolic cost to develop a new lot sizing model for preventing heat exposure and work-related musculoskeletal disorders. The effects of heat strain and rest allowance on the total cost of the production supply process were investigated. The problem studied in this paper was the handling of the raw materials placed in boxes by manual material handling in order to supply the material requirement of a production line placed in a production area. For the realisation of the material handling transactions between the raw material warehouse and the production line, Electric Pallet Jack (EPJ) was used. The study covers the investigation of picking, storing, and carrying motions for the manual handling of these materials. The result of the analysis has shown that 8.5% savings were achieved by using the heat strain and rest time in comparison to the total cost of this part of the production line supply process with the ISO 7243 maximum metabolic work limit. Consequentially, the analysis results showed that the developed method demonstrated the viability of lot sizing model optimisation with multiple objectives and complex constraints with regards to the metabolic cost and heat strain.

Objective: Spine kinematics, kinetics, and trunk muscle activities were evaluated during different stages of a fatigue-induced symmetric lifting task over time. Background: Due to neuromuscular adaptations, postural behaviors of workers during lifting tasks are affected by fatigue. Comprehensive aspects of these adaptations remain to be investigated. Method: Eighteen volunteers repeatedly lifted a box until perceived exhaustion. Body center of mass (CoM), trunk and box kinematics, and feet center of pressure (CoP) were estimated by a motion capture system and force-plate. Electromyographic (EMG) signals of trunk/abdominal muscles were assessed using linear and nonlinear approaches. The L5-S1 compressive force (Fc) was predicted via a biomechanical model. A two-way multivariate analysis of variance (MANOVA) was performed to examine the effects of five blocks of lifting cycle (C1 to C5) and lifting trial (T1 to T5), as independent variables, on kinematic, kinetic, and EMG-related measures. Results: Significant effects of lifting trial blocks were found for CoM and CoP shift in the anterior–posterior direction (respectively p <.001 and p =.014), trunk angle (p =.004), vertical box displacement (p <.001), and Fc (p =.005). EMG parameters indicated muscular fatigue with the extent of changes being muscle-specific. Conclusion: Results emphasized variations in most kinematics/kinetics, and EMG-based indices, which further provided insight into the lifting behavior adaptations under dynamic fatiguing conditions. Application: Movement and muscle-related variables, to a large extent, determine the magnitude of spinal loading, which is associated with low back pain. [ABSTRACT FROM AUTHOR]

Lifting is a main task for manual material handling (MMH), and it is also associated with lower back pain. There are many studies in the literature on predicting lifting strategies, optimizing lifting motions, and reducing lower back injury risks. This survey focuses on optimization-based biomechanical lifting models for MMH. The models can be classified as two-dimensional and three-dimensional models, as well as skeletal and musculoskeletal models. The optimization formulations for lifting simulations with various cost functions and constraints are reviewed. The corresponding equations of motion and sensitivity analysis are briefly summarized. Different optimization algorithms are utilized to solve the lifting optimization problem, such as sequential quadratic programming, genetic algorithm, and particle swarm optimization. Finally, the applications of the optimization-based lifting models to digital human modeling which refers to modeling and simulation of humans in a virtual environment, back injury prevention, and ergonomic safety design are summarized. [ABSTRACT FROM AUTHOR]

BACKGROUND: Musculoskeletal risks (MSRs) are a major concern among construction warehouse workers due to the lifting, carrying and lowering of heavy loads. OBJECTIVE: The objective of this study was to reduce MSRs among warehouse workers in the construction industry using virtual modelling and analysis of activities. METHODS: A preliminary study was carried out using the Standard Nordic Questionnaire. Biomechanical analysis, Ovako Working posture Assessment System (OWAS) and Rapid Upper Limb Assessment (RULA) were used to analyse the material handling activities. Virtual modelling was used develop the manikins with autoclaved aerated concrete (AAC) blocks at different carriage modes and stacking heights for the analysis. RESULTS: The preliminary study results revealed a higher prevalence of risk at their lower back (73.24%) among the construction warehouse workers. Biomechanical analysis showed a higher risk at L4-L5 joint of lumbar spine during overhead and bending postures for stacking the blocks. Posture analysis results of OWAS indicated a lower risk in overhead carriage mode. Detailed analysis with RULA confirms this result. Mean compressive force values at stacking heights were showed a significant difference (p < 0.05) in 8, 13, 18 and 21 kg AAC blocks. However, stacking height with a range of 120–140 cm was found as safer to the workers by considering all block sizes. CONCLUSION: An ergonomic intervention based on safer stacking heights was developed to reduce MSRs to an acceptable range. It improves productivity of handling the AAC blocks by reducing the cycle time. The intervention can be adapted for handling of similar materials in the construction industry. [ABSTRACT FROM AUTHOR]

The lobster-harvesting industry has a high occupational injury incidence compared to land-based industries. Participatory ergonomics methods were used to partner with lobstermen to develop and implement ergonomic improvements. The model included training in ergonomics principles, a forum for ergonomics discussions, and a sequence of meetings planned to focus on problem identification, intervention, evaluation, and dissemination of findings. One crew initiated 3 specific actions: the introduction of a conveyor belt to assist material handling at a local lobstering pier, the installation of a star block at the space for hauling traps onto the boat, and the initiation of a design process for improved mechanical assistance for hauling traps onto the boat. The other crew took action to reduce force and postural exposures by creating a short video intended to disseminate ergonomic ideas to other lobstermen. The influence of important cultural norms was observed for future research and development in the community. [ABSTRACT FROM AUTHOR]

Chronic low back pain due to manual lifting continues to be one of the significant common public health challenges in modern societies despite increased automation. While there are extensive studies on the biomechanics of lifting as associated with LBP, the role of unstable and time-varying dynamic loads, quite common in industrial lifting and daily life, remains elusive. The present study aimed to investigate the response of trunk muscles in subjects with chronic non-specific low back pain (CNLBP) while holding unstable dynamic loads. Twelve male patients with CNLBP and twelve healthy controls participated in this cross-sectional study. The subjects held static and dynamic loads in neutral positions. Normalized EMG data of the trunk muscles were captured and analyzed by repeated-measures ANOVA test. The low back pain group demonstrated significantly higher activation levels of the internal and external abdominal oblique muscles while holding dynamic loads (p < 0.05). Our results suggest that the neuromusculoskeletal system in low back patients holding dynamic loads may invoke a motor control strategy that significantly increases muscle co-activation leading to higher joint stiffness at the expense of higher compressive loads on the lumbar spine. Importantly, the type of load plays a critical role in terms of external perturbations that may lead to spinal injury in CNLBP patients and must, therefore, be considered in the risk prevention and assessment of lifting and other manual material handling tasks. [ABSTRACT FROM AUTHOR]

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 ( 8 % ), semitendinosus ( 14 % ), gluteus ( 5 % ), and quadriceps ( 10.2 % ). The classic fatigue parameters showed a significant reduction in the case of the semitendinosus: 1.7 % zero-crossing rate, 0.9 % mean frequency, and 1.12 % 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 10 % . 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. [ABSTRACT FROM AUTHOR]

Introduction: Manual lifting operations continue to play a key role in the industrial and service sectors, inflicting physical strain on the musculoskeletal system, despite advances in automation. As a result, an experiment is carried out to assess the impact of two lifting task parameters; weight and height, based on the estimation of subjective responses and biomechanical loading, while lifting the weight symmetrically in the sagittal plane. Also to recommend the safe limit for manual lifting tasks. Methods: Twelve volunteer male students in the age group of 21 to 26 years performed lifting tasks from floor to 5 different heights (below the knee to ear level), with 5 different weights (10 to 20 kg) using free-style lifting techniques. The load pan with no handle was used for lifting weight, which is typically adopted in the Indian building construction field. The subjective estimate was obtained using workload assessment by body discomfort chart. The biomechanical loading (loading rate) for each lifted weight and height was collected using a force platform. Results: The results showed that heavier weights produced higher stresses than lower weights. The loading rate was found to be almost similar at waist or knee level. The loading rate was observed to be linearly increasing after waist level. The overall workload rating seems to be a good correlate with the mean loading rate to some extent. Conclusion: It is proposed to keep the maximum acceptable lifting weight from floor to knee, up to the ear level is 15 kg, to prevent any musculoskeletal or chronic injury.