Handling Tasks Research Articles

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.

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.

Enhancing construction robot learning for collaborative and long-horizon tasks using generative adversarial imitation learning

The development and deployment of robots on construction sites are integral to the industrialization of construction, known as Construction 4.0. Tele-operated and pre-programmed robots have enhanced construction efficiency and safety. However, their utilization on-site remains limited due to the need for expert remote control and the lack of adaptability in dynamic environments. Reinforcement learning (RL) has emerged as a promising solution, as RL-controlled robots possess inherent self-learning abilities to adapt to diverse situations. Nevertheless, manual design of RL reward functions for complex tasks poses challenges. To address this issue, inverse reinforcement learning (IRL) methods, such as Generative Adversarial Imitation Learning (GAIL), have been proposed to learn optimal actions through expert demonstration and self-exploration, without explicitly defined reward functions. In this study, we propose an innovative approach integrating GAIL and virtual reality (VR) integrated robot control approach to control robots for long-horizon collaborative construction tasks involving multiple sub-tasks. We employ VR expert demonstrations as input for GAIL training, enabling a team of robots, including an Unmanned Ground Vehicle (UGV) and two robot arms, to interact with the designed RL environment and perform tasks such as transporting, picking, and installing window panels. Handle long-horizon collaborative construction tasks (i.e., a long sequence of several sub-tasks performed by multiple robots). For evaluation, we compare the performance of our VR-GAIL model with a prevalent and robust RL baseline model, Proximal Policy Optimization (PPO). The results demonstrate that our reward-free VR-GAIL model achieves, on average, a 4.5% higher success rate than the PPO counterpart equipped with carefully designed reward functions across all three sub-tasks and their randomized variations. Furthermore, the performance gap between GAIL and PPO widens as the task difficulty increases. These findings indicate that our approach effectively enhances RL agent performance in tackling complex construction tasks while expediting development by eliminating reward function design requirements.

Manual handling amongst physiotherapists: Analysis of intra-shift variations in pain, fatigue and movement.

Manual handling injuries amongst physiotherapists are common and the need to improve our understanding of causal influences is imperative. The objective was to determine whether intra-shift variations in manual handling task performance occurred in our cohort, which may inform mechanisms underpinning related injuries. We used motion capture, force plate dynamics and electromyography to identify variations in task performance, loading forces and muscle activity, during the performance of one static and one dynamic standardized manual handling task, pre- and post-shift, by 40 physiotherapists. Participants also rated their pain and fatigue on a visual analogue scale (VAS). Statistical analysis utilised paired samples Student's t tests. Significant differences were seen in the EMG activity in the quadriceps during the static task only. No significant differences were seen for any of the kinematic variables. Significant differences in fatigue (p < 0.005) were seen between the pre- and post-shift sessions. Notably, there were significant differences in pain between the pre- and post-shift sessions in the static (p < 0.01) and dynamic tasks (p < 0.05). This increase in pain was at a level which impacted on function. Whilst significant variations in task performance were not observed, our findings indicate that physiotherapists frequently experience task-related pain towards the end of their shift. Contemporary research indicates that frequent transient low back pain may transition to a chronic disabling condition, as such we posit that the effects of intra-shift pain, and its causative factors, should be more widely considered in a 'whole-of-job' approach to mitigating risk in this demographic.

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.

Construction and application of knowledge graph for grid dispatch fault handling based on pre-trained model

With the construction of new power systems, the power grid has become extremely large, with an increasing proportion of new energy and AC/DC hybrid connections. The dynamic characteristics and fault patterns of the power grid are complex; additionally, power grid control is difficult, operation risks are high, and the task of fault handling is arduous. Traditional power-grid fault handling relies primarily on human experience. The difference in and lack of knowledge reserve of control personnel restrict the accuracy and timeliness of fault handling. Therefore, this mode of operation is no longer suitable for the requirements of new systems. Based on the multi-source heterogeneous data of power grid dispatch, this paper proposes a joint entity–relationship extraction method for power-grid dispatch fault processing based on a pre-trained model, constructs a knowledge graph of power-grid dispatch fault processing and designs, and develops a fault-processing auxiliary decision-making system based on the knowledge graph. It was applied to study a provincial dispatch control center, and it effectively improved the accident processing ability and intelligent level of accident management and control of the power grid.

Biomechanical Consequences of Using Passive and Active Back-Support Exoskeletons during Different Manual Handling Tasks.

The aim of this study was to assess, for both men and women, the consequences of using different back-support exoskeletons during various manual material tasks (MMH) on the activity of back muscles and trunk kinematics. Fifteen men and fourteen women performed MMH involving a 15 kg load (a static task, a symmetric lifting task, and an asymmetric lifting task). Four exoskeleton conditions were tested: without equipment (CON) and with three exoskeletons passive (P-EXO), and active (A-EXO1 and A-EXO2)). The electromyographic activity of the lower trapezius (TZ), latissimus dorsi (LD), erector spinae (ES), gluteus maximus (GM), and biceps femoris (BF) muscles was recorded. Trunk kinematics were evaluated to provide average thoracic, lumbar, and hip angles. The use of the P-EXO decreased the activity of LD, GM, and BF from -12 to -27% (p < 0.01) compared to CON, mostly during the static task. The A-EXO1 and A-EXO2 reduced the muscle activity of all studied muscles from -7 to -62% (p < 0.01) compared to CON and from -10 to -52% (p < 0.005) compared to the P-EXO, independently of the modalities of the experimental tasks. A statistical interaction between the sex and exoskeleton was only observed in a few rare conditions. Occupational back-support exoskeletons can reduce trunk extensor muscle activity compared to no equipment being used. However, these reductions were modulated by the exoskeleton technology (passive vs. active), design (weight and anthropomorphism), and the modalities of the task performed (static vs. dynamic). Our results also showed that the active exoskeletons could modify the trunk kinematics.

Instructional Management During Covid-19

This study aims to describe learning management and the inhibiting and supporting factors for learning during the Covid-19 pandemic. It is descriptive qualitative research. Data collection techniques using observation, interviews, and documentation. Data collection techniques use observation, interviews, and documentation. Data analysis techniques are data collection, data reduction, data display, and conclusions. The results of the study show that learning management during the Covid-19 pandemic at SMP Negeri 01 Semendawai, Suku Ill included planning, implementation, and evaluation. The learning planning is the preparation of Limited Face-to-face Learning, namely health protocol facilities, Covid-19 task force, Limited PTM supporting documents, preparation of learning tools Special Conditions Curriculum. The Learning implementation refers to the policy of the Head of Education and Culture Office of East OKU Regency regarding technical instructions Implementation of Limited PTM for the 2020/2021 school year. The learning evaluation is carried out normally according to the pandemic situation, namely the assessment of learning outcomes by teachers, education units, and the government. The inhibiting factor for learning is students’ enthusiasm for learning is low, the signal is not stable, and not all students have a smartphone. The supporting factors for learning are the enthusiasm of teachers, parents, and students to learn face-to-face, school discipline, good cooperation between school members, health protocol facilities, a Covid-19 handling task force, and the use of smartphones.

Trajectory tracking control of an unmanned aerial vehicle with deep reinforcement learning for tasks inside the EAST

The robotic arms inside the EAST (Experimental Advanced Superconducting Tokamak) are bulky and slow, making them unable to efficiently complete remote handling tasks such as inspection and grasping. Miniature intelligent UAVs have the potential to assist in remote handling tasks. A key challenge is to achieve autonomous flight along a set trajectory within the EAST's vacuum vessel. This paper presents an autonomous UAV system with deep reinforcement learning for this purpose. The autonomous flight of a quadrotor UAV within the EAST was simulated using OpenAI Gym-style environment. To verify that the trained policy is transferable, we experimentally verified the trajectory tracking of UAVs along specific trajectories in real scenarios. The results show that our autonomous UAV system can complete trajectory-tracking flight tasks inside the EAST vacuum vessel.

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.

Grid-Map-Based Path Planning and Task Assignment for Multi-Type AGVs in a Distribution Warehouse

In an intelligent distribution warehouse, latent AGVs are used for horizontal handling, and forklift AGVs are used for horizontal or vertical handling. Studying the path planning and task assignment problem when the two types of AGVs are mixed can help improve the warehouse operation efficiency and reduce the warehouse operation cost. This paper proposes a two-stage optimization method to solve this problem. In the first stage, the warehouse plan layout is transformed into a raster map, and the shortest path between any two points of the warehouse without conflict with fixed obstacles is planned and stored using the A* algorithm combined with circular rules, and the planned shortest path is called directly in the subsequent stages. In the second stage, to minimize the task completion time and AGV energy consumption, a genetic algorithm combining penalty functions is used to assign horizontal handling tasks to submerged AGVs or forklift AGVs and vertical handling tasks to forklift AGVs. The experimental results show that the method can meet the 24 h operation requirements of an intelligent distribution warehouse and realize the path planning and task assignment of forklift AGVs and latent AGVs. And furthermore, the number of AGVs arranged in the warehouse can be further reduced.

Multi-agent planning and coordination for automated aircraft ground handling

Inspired by the vision of fully autonomous airside operations at Schiphol airport, this study aims to contribute to the short-term goal of automated aircraft ground handling. In this research, we design and evaluate a multi-agent system for planning of automated ground handling. There are two main components in the system: task allocation optimization and multi-agent path planning. To allocate tasks to ground support equipment (GSE) vehicles, an auction mechanism inspired by temporal sequential single item (TeSSI) auction is proposed. Ground handling tasks scheduling for GSE vehicles is modeled as several single-vehicle pickup and delivery optimization problems (SPDP), and the values of the objective functions are used to generate bids for GSE vehicle agents in the auction. Prioritized safe interval path planning for large agents (LA-SIPP) is used to plan collision-free paths for GSE vehicle agents in the model to execute tasks. The aim is to increase the success rates of allocating tasks and finding collision free paths without causing flight delays, given the limited resources such as a small number of available GSE vehicles, time windows constraints and conflicting interests of different agents. Due to the results, even for the instances with frequent flights and the most limited resources, the success rates of allocation and path planning were higher than 81% and 98%, respectively. Furthermore, periodic task allocation and path planning of the ground handling tasks for flights in three aircraft stands during a planning time window of the day, as well as replanning in case of disruptions were performed in a short CPU time. There is a lack of research dealing with the complete process of ground handling, since existing studies concerning the automation of ground handling operations involve fleet assignment or task scheduling models without an integration of detailed path planning. Our main contribution is to present a framework that combines task allocation and path planning for automation of ground handling operations and provides solutions using a multi-agent perspective.

Research on risk assessment model of subjective and objective integration in manual handling work

AbstractThe main hazards in the process of manual handling work are triggered by human factors and ergonomics. This study is intended to implement a valid approach to quantitatively evaluate the risk level during manual handling work. A risk assessment model for manual handling workers was proposed based on subjective and objective correlation. A simulation experiment of manual handling process was developed and an ergonomics evaluation method was carried out. The 33‐point human joint model of BlazePose neural network and the Rapid Upper Limb Analysis (RULA) method were utilized to determine the risk posture and risk index of the manual handling workers. This study brought together hemodynamic parameters and the score of Borg Rating of Perceived Exertion (RPE) scale and Physical Resources Scale (PRS) to obtain the final comprehensive index of work risk. The results showed that the risk indexes from the three stages of the experiment obtained by RULA method were 3, 4, and 7. And the scores of comprehensive indexes obtained by the risk assessment model were 1.841, 1.900, and 1.987, suggesting that the evaluation model based on subjective and objective correlation had the same ability to determine the risk levels of different handling tasks. Therefore, the risk assessment model proposed in this study verified the effectiveness of the comprehensive evaluation index integrating hemodynamic parameters and subjective evaluation scores.

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.

The IFMIF-DONES remote handling control system: Experimental setup for OPC UA integration

The devices used to carry out Remote Handling (RH) manipulation tasks in radiation environments address requirements that are significantly different from common robotic and industrial systems due to the lack of repetitive operations and incompletely specified control actions. This imposes the need of control with human-in-the-loop operations. These RH systems are used on facilities such PRIDE, CERN, ESS, ITER or IFMIF-DONES, the reference used for this work.For the RH system is crucial to provide high availability, robustness against radiation, haptic devices for teleoperation and dexterous operation, and smooth coordination and integration with the centralized control room. To achieve this purpose is necessary to find the best approach towards a standard control framework capable of providing a standard set of functionalities, tools, interfaces, communications, and data formats to the different types of mechatronic devices that are usually considered for Remote Handling tasks. This previous phase of homogenization is not considered in most facilities, which leads towards a costly integration process during the commissioning phase of the facility.In this paper, an approach to the IFMIF-DONES RH Control framework with strong standard support based on protocols such as OPC UA has been described and validated through an experimental setup. This test bench includes a set of physical devices (PLC, conveyor belt and computers) and a set of OPC UA compatible software tools, configured and operable from any node of the University of Granada network. This proof-of-concept mockup provides flexibility to modify the dimension and complexity of the setup by using new virtual or physical devices connected to a unique backbone. Besides, it will be used to test different aspects such as control schemes, failure injection, network modeling, predictive maintenance studies, operator training on simulated/real scenarios, usability or ergonomics of the user interfaces before the deployment. In this contribution, the results are described and illustrated using a conveyor belt set-up, a small but representative reference used to validate the RH control concepts here proposed.

Effects of Slide Sheet Use and Bed Position on Muscle Activities in the Low Back and Extremities: A Pilot Experimental Simulation Study.

Low-friction slide sheets (SS) are designed to reduce compression loads on the body during manual handling tasks, such as boosting patients. Using SS has been shown to decrease muscle activity in the lower back and upper extremities. However, it is unclear if this effect varies with different bed positions. To investigate this, we studied the effects of SS use, bed height, and their combination on muscle activity during a simulated patient boost. Thirty-three Japanese undergraduate students (age 21.0 ± 1.1 years; 14 men, 19 women) participated. Participants were asked to boost a dummy figure on the bed three times each using four conditions. During the repositioning task, electromyography of eight muscles of the lower back and upper and lower extremities, hip and knee joint flexion angles, pelvic forward tilt angle, and position of the center of mass based on the posterior superior iliac spine were evaluated. Electrophysiological activities of muscles of lower back and upper extremities were significantly lower with SS than without it in both bed positions (30% and 40% of body height); the reduction in muscle activities with SS use was 20% to 40%. Lowering the bed did not affect the SS effect magnitude on reducing muscle activities, although postural changes, including hip and knee joint flexion, were observed. SS reduced muscle activities in the back, upper, and lower extremities when the bed was in the low position, and this effect persisted at a bed height of ≥30% of the participant's height.

Analisis Fatwa MUI Tentang Tes Swab Untuk Mendeteksi Covid-19 Saat Berpuasa Perspektif Pandangan Ulama dan Relevansinya

Circular Letter of the Covid-19 Handling Task Force Number 11 of 2022 concerning Provisions for the Travel of Domestic Persons During the Covid-19 Pandemic Period is the basis for revoking the obligation for Rapid antigen tests for people who have received full doses of vaccines and booster vaccines, but are required to comply with health protocols. People who receive the first dose of the vaccine and who have not received the vaccine for reasons of special health conditions must show negative results from the RT-PCR test or rapid antigen test. This regulation also applies to travellers who are fasting. One prohibition that must be avoided while fasting is putting something into the body. In response, the Indonesian Ulama Council issued fatwa number 23 of 2021 concerning the Swab Test to Detect Covid-19 While Fasting, which states that the swab test does not break fast. So it is necessary to study how the views of the Ulama on this fatwa and its relevance to the situation when this fatwa was decided. This study uses the method of literature review. The approach used is the ushul fiqh approach, and the theory used is istihsan. The primary legal material used is the MUI Fatwa Number 23 of 2021, and the secondary legal material used is books and journal articles. The purpose of this study was to find out the views of the Ulama on the results of the MUI's Ijtihad in establishing Fatwa Number 23 of 2021 so that there is no doubt when carrying out a swab test while fasting and whether this MUI fatwa is relevant to the situation at the time this fatwa was decided. The study's results show that Ulama's views on the results of the MUI's Ijtihad in establishing Fatwa Number 23 of 2021 are divided into two opinions. Most contemporary scholars say that the law on swab testing during fasting does not break the fast. According to the classical ulama, the Hambali and Shafi'i ulama declared it null and void. The Maliki school of thought believes there is a gap for the swab not to break the fast because it is a dry and solid object that does not reach the stomach. The Hanafi school believes that the swab is not invalidated because it does not enter the body perfectly. MUI's decision-making in establishing Fatwa No. 23 of 2021 is considered relevant to the situation when this fatwa was issued. This fatwa is an effort to emphasize the spread of Covid-19.

SUPPORT VECTOR MACHINE (SVM) ALGORITHM FOR STUDENT SENTIMENT ANALYSIS OF ONLINE LECTURES

Covid-19 was first discovered in Wuhan City, Hubei Province, China at the end of December 2019. According to the WHO (World Health Organization) as of October 13 2020, the number of positive confirmed cases of Covid-19 reached 38,103,332 cases, while in Indonesia the number of cases exposed to Covid-19 reached 268.85 cases and is likely to increase every day (Covid-19 Handling Task Force, 2020). The formulation of the problem that will be raised from this research is to measure the level of accuracy obtained from the results of classifying sentiments of distance learning during the Covid-19 pandemic using the Support Vector Machine (SVM) method and measuring the impact of implementing online lectures during the Covid-19 pandemic. The data used in this research is in the form of public responses regarding distance learning policies implemented during the Covid-19 pandemic, taken from January to March 2022. The data obtained will then be divided into training data as much as 80% of the the total data and test data is 20% of the total data. Based on the results of testing the previous Support Vector Machine classification model, the accuracy value for the entire system can be calculated at 70.8%. Based on the results of testing the previous Support Vector Machine classification model, the accuracy value for the entire system can be calculated at 70.8%.

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