Handling Tasks Research Articles

Classifying diverse manual material handling tasks using a single wearable sensor

The use of inertial measurement units (IMUs) for monitoring and classifying physical activities has received substantial attention in recent years, both in occupational and non-occupational contexts. However, a “user-friendly” approach is needed to promote this approach to quantify physical demands in actual workplaces. We explored the use of a single IMU for extracting information about different manual material handling (MMH) tasks (i.e., specific type of task performed, and associated duration and frequency), using a bidirectional long short-term memory network for classification. Classification performance using single IMUs placed on several body parts was compared with performance using multiple IMU configurations (2, 3, and 17 IMUs). Overall, the use of a single sensor led to satisfactory results (e.g., median accuracy >97%) in classifying MMH tasks and estimating task duration and frequency. Limited benefits were obtained using additional sensors, and several sensor locations yielded similar outcomes. Classification performance, though, was relatively inferior for push/pull vs. other tasks.

Wrapping technique and wrapping height interact to modify physical exposures during manual pallet wrapping

Occupational tasks often involve musculoskeletal demands that contribute to injury risk. In pallet wrapping tasks, 36% of workplace claims involve over exertion and repetitive exposures (Workplace Safety and Prevention Services, 2012). Tools that modify how the wrap is handled by workers have been introduced to help mitigate over exertion and extreme postures wherever possible. A novel device has been introduced that places the required tension on the roll to mitigate these factors. However, the effect of reducing the necessary tension on the roll during pallet wrapping in limiting muscular demand and extreme postures is unknown. Fourteen healthy university aged participants completed 12 wrapping trials on a simulated pallet (2 repetitions of 2 wrapping techniques (device, hand wrapping) at 3 wrapping heights (low, medium, high)). Surface electromyography (sEMG) was measured on 6 shoulder and 2 low back muscles; anterior and middle deltoids, biceps brachii, infraspinatus, supraspinatus, upper trapezius, and erector spinae (T8 & L3). Kinematic data were collected for the torso and upper extremity and global to torso, and torso to upper arm angles were computed. Repeated measures ANOVAs were performed for the following experimental factors: 1) technique used (device or hand wrapping) and 2) the wrapping height (low, medium, high) for each muscle (8), angle (5), rating of perceived discomfort (1) and rating of perceived exertion (1). Pallet wrapping without a device required greater trunk flexion at lower heights and thoracohumeral elevation at higher wrapping heights as compared to using the device. Muscular activation increased when using the device, specifically at the higher and lower heights. Posture and muscular demands during pallet wrapping tasks are sensitive to both wrapping technique and wrapping height. Ergonomics interventions such as this device may mitigate postural risks associated with manual material handling tasks.

Multi-objective workflow optimization strategy (MOWOS) for cloud computing

Workflow scheduling involves mapping large tasks onto cloud resources to improve scheduling efficiency. This has attracted the interest of many researchers, who devoted their time and resources to improve the performance of scheduling in cloud computing. However, scientific workflows are big data applications, hence the executions are expensive and time consuming. In order to address this issue, we have extended our previous work ”Cost Optimised Heuristic Algorithm (COHA)” and presented a novel workflow scheduling algorithm named Multi-Objective Workflow Optimization Strategy (MOWOS) to jointly reduce execution cost and execution makespan. MOWOS employs tasks splitting mechanism to split large tasks into sub-tasks to reduce their scheduling length. Moreover, two new algorithms called MaxVM selection and MinVM selection are presented in MOWOS for task allocations. The design purpose of MOWOS is to enable all tasks to successfully meet their deadlines at a reduced time and budget. We have carefully tested the performance of MOWOS with a list of workflow inputs. The simulation results have demonstrated that MOWOS can effectively perform VM allocation and deployment, and well handle incoming streaming tasks with a random arriving rate. The performance of the proposed algorithm increases significantly in large and extra-large workflow tasks than in small and medium workflow tasks when compared to the state-of-art work. It can greatly reduce cost by 8%, minimize makespan by 10% and improve resource utilization by 53%, while also allowing all tasks to meet their deadlines.

The implementation of policies and the impact of handling Covid-19 originating from the World Health Organization's Point of View

The purpose of this article is to describe the capacity and role of WHO in dealing with the spread of COVID-19 and to discuss in-depth articles at the stages of policy implementation that specifically discuss the WHO protocol that is applied in dealing with the spread of Covid-19. The implementation of the policy to handle the spread of COVID-19 considers various aspects not only health but also the economic and social impacts resulting from government decision making. In addition, response measures are implemented through the Acceleration of COVID-19 Handling task which is part of the formation of structures, structure is one of the aspects that are considered in Edward III's implementation theory besides communication, resources and disposition because the presence of a structure helps implement policies has a significant effect. on policy implementation. The support of all elements of the government, the private sector in compliance with government and WHO instructions, especially regarding social distancing and physical distancing, will have a big impact in preventing the spread of COVID-19.

PENGARUH KARAKTERISTIK INDIVIDU DAN KOMUNIKASI INTERNAL TERHADAP KINERJA KARYAWAN

The decline on employee performance is due to they are not able to reach targets as required by company. They must be decided by the company because they have poor performance such as sales, who did not reach the target during the trial period. Employees who are not conscientious, employees who are not alert in  working, for example handling tasks that cannot be done in a predetermined time. This research took a sample of 42 respondents by using the total sampling technique. The data collection technique is done through observation and questionnaires. The data analysis technique is multiple linear regressions. Then also wll tested, such as validity test, reliability test, classical assumption test followed by hypothesis testing.The results showed that there was an effect of individual characteristics (X1) on employee performance (Y) at PT. Genta Lampung Makmur Bengkulu, it was to 0,000, thus Ho was rejected and Ha was accepted. The significance value of variable X has an effect on employee performance (Y). Thenalso, the effect of internal communication (X2) on employee performance, with its value 0.031, thus Ho is rejected and Ha is accepted. From these significance values obtained, the variable X2 has an effect on employee performance (Y). Individual characteristics and internal communication simultaneously have an effect on employee performance with a value of F_sig <α where F_sig is 0,000. Keywords: Individual Characteristics, Internal Communication, Performance.

Automated Liquid Handling for Microplate Assays: a Simplified User Interface for the Hamilton Microlab STAR

Automated liquid handling stations such as the Hamilton Microlab range can be implemented to greatly enhance throughput of cell-based and cell-free assays. To facilitate utilisation past the comprehensive programming interface of the Hamilton Method Editor this paper presents a user interface (UI) that runs within Hamilton Venus and allows for a user to control and store programs for plate-to-plate transfers and serial dilutions in 96 well plate format. The interface allows for rapid control of aspiration and dispensing height, trituration, control of tip rack selection, and includes a tip washing program that can reduce the number of disposable tips utilised. The simple program interface allows the Hamilton Microlab to be used as a readily deployable microtitre plate handler, particularly for applications such as aliquoting cells for seeding, diluting a number of plates in medium, or transferring drug dilution arrays to multiple plates. This paper also discusses various optimisations to increase accuracy and rate of low volume liquid transfer. While complex liquid handling tasks such as high-throughput drug screening requires established core facilities, standalone liquid handlers with simplified interfaces can be utilised for smaller-scale research applications and educational purposes.

Manual material handling in the supermarket sector. Part 2: Knee, spine and shoulder joint reaction forces

Manual material handling is common in supermarkets and may be a contributing factor to the high prevalence of work-related musculoskeletal disorders, particularly to the lower back. This cross-sectional study applied state-of-the-art musculoskeletal models driven by kinematic data obtained in two supermarkets to estimate joint reaction forces in the knees, shoulders and lumbar spine under dynamic lifting conditions. Based on 1479 lifts from 15 workers, 8 tasks for which the compression or shear forces in the L5-S1 joint exceeded well-known biomechanical tolerance limits were identified. High shoulder forces were associated with lifting relatively heavy merchandise to high shelves, while the weight of the handled merchandise was the main predictor of high knee forces. The study addressed well-known limitations associated with traditional lifting analysis tools and was the first to present a detailed analysis of the biomechanical loads during manual material handling tasks in the supermarket sector based on field measurements.

A Promising Wearable Solution for the Practical and Accurate Monitoring of Low Back Loading in Manual Material Handling.

(1) Background: Low back disorders are a leading cause of missed work and physical disability in manual material handling due to repetitive lumbar loading and overexertion. Ergonomic assessments are often performed to understand and mitigate the risk of musculoskeletal overexertion injuries. Wearable sensor solutions for monitoring low back loading have the potential to improve the quality, quantity, and efficiency of ergonomic assessments and to expand opportunities for the personalized, continuous monitoring of overexertion injury risk. However, existing wearable solutions using a single inertial measurement unit (IMU) are limited in how accurately they can estimate back loading when objects of varying mass are handled, and alternative solutions in the scientific literature require so many distributed sensors that they are impractical for widespread workplace implementation. We therefore explored new ways to accurately monitor low back loading using a small number of wearable sensors. (2) Methods: We synchronously collected data from laboratory instrumentation and wearable sensors to analyze 10 individuals each performing about 400 different material handling tasks. We explored dozens of candidate solutions that used IMUs on various body locations and/or pressure insoles. (3) Results: We found that the two key sensors for accurately monitoring low back loading are a trunk IMU and pressure insoles. Using signals from these two sensors together with a Gradient Boosted Decision Tree algorithm has the potential to provide a practical (relatively few sensors), accurate (up to r2 = 0.89), and automated way (using wearables) to monitor time series lumbar moments across a broad range of material handling tasks. The trunk IMU could be replaced by thigh IMUs, or a pelvis IMU, without sacrificing much accuracy, but there was no practical substitute for the pressure insoles. The key to realizing accurate lumbar load estimates with this approach in the real world will be optimizing force estimates from pressure insoles. (4) Conclusions: Here, we present a promising wearable solution for the practical, automated, and accurate monitoring of low back loading during manual material handling.

A pilot study to assess a risk of a high-risk group of low back pain membership in workers who perform the manual material handling tasks.

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

Reliability oriented multi-AGVs online scheduling and path planning problem of automated sorting warehouse system

The automated sorting warehouses are operated by multiple AGVs simultaneously, which can quickly sort a large number of packages. How to solve the problems of multi-AGVs scheduling, path planning and conflict resolution is the key to the reliable and rapid operation of the warehouse system. In order to improve the reliability and sorting efficiency of the system, the objective of this paper is to minimize the maximum handling completion time. Firstly, a multi-AGVs path planning algorithm based on Grid Blocking Degree (GBD) is proposed. This algorithm can plan a conflict-free handling path for each handling task of each AGV, and update the path dynamically according to the real-time traffic congestion. Furthermore, aiming at the comprehensive optimization of scheduling and path planning, a scheduling algorithm based on priority rules is proposed to determine the handling task for AGVs. The data experiments compare the difference of the solution effect of different priority rules, and verify the effectiveness of the algorithm proposed in this paper.

Color-Coding of Microchip RT-PCR Test System for SARS-CoV-2 Detection

An RT-PCR based microchip test system for the detection of SARS-CoV-2 offers pre-loaded and lyophilized reagents in the microchip. However, the 30- and 48-microwell formats of the microchip being miniaturized and performing 1.2 μl reaction, seek visual attention during sample addition. Therefore, adding colorants as color indicator in the lyophilized matrix in the microchips or adding to sample or master mix can impart not only user-friendliness to the task of liquid handling but also precision, and color-codes for easy identification of multiple kits in the layout of the microchip without compromising PCR data quality. A panel of colorants was screened for their background intensity, spectral inertness towards detection channels of AriaDNATM analyzer, interference with the reporter dyes (FAM, Cy5 and ROX), and visibility of optimal concentration in the microwell. The concentration of the colorant displaying insignificant impact on the quality of the amplification (Ct, fluorescence, and sensitivity) in comparison to no-colorant control was chosen for inclusion in the test kit. Tartrazine, Acid Red, Brilliant Blue and FAST Green colorants lyophilized with the reagents in the SARS-CoV-2 microchips were found to be stable and suitable. Storage of microchips with Fast Green colorant was tested at 40°C, 22°C, 4°C, and -20°C for 70 days and was found to be suitable and compatible with different master mixes available as liquid or lyophilized. Additionally, the microchips pre-loaded with lyophilized reagents in the presence and absence of two colorants Tartrazine and Fast Green were validated with clinical samples of SARS-COV-2. No significant impact of these colorants both intra- and inter-microchips was observed on the Ct and intensity of amplification for the tested samples in comparison to no-colorant control. The data suggested that the tested colorants can be used to color the sample, or the master mix or PCR mix for user-friendly liquid handling in empty microchips. For the microchip with pre-loaded and lyophilized reagents, the colorant can be added to lyophilized mixture for precision liquid handling and color-coding of lyophilized kits in the microchips. The manufacturing quality of the lyophilized microchips can also improve with colorant loaded reagent mix.

A method for cutting force estimation through joint current signals in robotic machining

Industrial robots have been playing a key role in many sectors for several decades, enabling higher automation levels. Lately, they have upscaled their role in the manufacturing industry. Apart from handling and assembly tasks, robots are being directly involved in manufacturing processes, due to their large working envelopes and high flexibility. Process monitoring is a crucial task since it enables real-time characterization and control. Especially in the case of robots, cutting forces cause deflection of the arm and subsequently dimensional errors. As a result, estimating these forces is a significant task. This is the main shortcoming of robots in machining and should be compensated in real-time. However, utilizing costly dynamometers for this purpose is often not feasible on an industrial level. This work proposes a method for extracting cutting forces from the joint current signals, which are already monitored by the robot controller, to enable cost-effective and non-invasive force monitoring.

Trajectory Tracking and Vibration Control of Flexible Beams in Multi-Axis System

In this paper, an X-Y position stage simulates flexible loads completing handling or detection tasks in industrial applications are studied. The platform is driven by permanent magnet synchronous motors (PMSM) servo system, which has flexible beams with different natural vibration frequencies on the X-axis and Y-axis. The controller is designed to suppress the vibration of the flexible beams with different frequencies and reducing the contour error. The Euler-Bernoulli beam theory is adopted to obtain the mode shapes and natural frequencies of the flexible beam with clamped and endpoint mass boundary conditions. Analysis shows that the first-order vibration mode contributes the most to the displacement of the flexible beam. The trajectory motion profile based on time-shifted cosine jerk central angle interpolation (CJCI) contains prefilter to suppress low vibration frequencies in the two beams, which does not cause distortion of the actual contour. The linear phase lead iterative learning control (ILC) combined with the contour of CJCI is proposed to reduce the tracking errors of each axis, thereby reducing the overall contour error. The analysis and design process of ILC in frequency domains, which is applied to alleviate noise and disturbance effects. Comparison experiments confirm the effectiveness of the proposed method.

Deep Learning Approach for Negation Handling in Sentiment Analysis

Negation handling is an important sub-task in Sentiment Analysis. Negation plays a significant role in written text. Negation terms in sentence often changes the polarity of entire sentence from positive to negative or vice versa, resulting in the opposite meaning of the sentence than what is observed by the machine learning based linguistic model. As automatic opinion mining has become very important in this digital era, proper handling of negation term is the need of the hour. In any natural language negations can be formulated both explicitly or implicitly while their use is very much domain-specific. Existing negation handling techniques follow rule-based approach and mainly used in medical domain. Due to the complex syntactic structure of negation, it is hard to build general purpose machine learning based negation handling model on user review or conversational text data. In this paper, we investigate negation components i.e., cue and scope in a sentence which determine the polarity shift in sentence. We propose LSTM based deep neural network model for negation handling task where the model automatically learns the negation features from labeled input training dataset. We used <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ConanDoyle</i> story corpus for model training and testing, which is pre-annotated with negation information. The proposed model first identify negation cues in each sentence and then using bidirectional LSTM extracts the relationship between cue and other words to identify scope of the cue in sentences. We derived word level features for model training to determine correct polarity of the sentence. Result shows that the LSTM based nonlinear language models perform comparatively better than the traditional state of the art SVM, HMM or CRF based models. BiLSTM achieved best result, F1 measures 93.34%, outperform traditional rule based model in negation handling task.

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

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

Polemic of Mudik in Indonesia: How People Deal with Government Regulation Related to Covid-19

Indonesia has a variety of cultures and traditions that develop in society; one of them is mudik, or the tradition of returning to hometown after a month of fasting during Ramadan to celebrate Eid al-Fitr. This tradition is an example of Indonesia's religious, cultural system related to the migration movement from one area to another. For many people, especially Muslims, mudik (homecoming) is considered an important event. However, since Covid-19 has been occurring globally, the government decided to ban this traditional annual activity, and it causes pros and cons to society. This research is intended to analyze society’s responses toward government regulation about the prohibition of mudik (homecoming) in 2021 due to Covid-19. Researchers used a quantitative descriptive method, namely utilizing data obtained through an online simple random sampling survey conducted on several Muslims and supported by literature studies. The result shows 64% of the respondents decided to follow the government regulation by not returning home this year. Based on the result, the existence of this (Addendum) Circular Letter of the Covid-19 Handling Task Force No.13, 2021 became a preventive way to restrain society from returning home for Eid al-Fitr this year.

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

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

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

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

Back loading estimation during team handling: Is the use of only motion data sufficient?

Analyzing back loading during team manual handling tasks requires the measurement of external contacts and is thus limited to standardized tasks. This paper evaluates the possibility of estimating L5/S1 joint moments based solely on motion data. Ten subjects constituted five two-person teams and handling tasks were analyzed with four different box configurations. Three prediction methods for estimating L5/S1 joint moments were evaluated by comparing them to a gold standard using force platforms: one used only motion data, another used motion data and the traction/compression force applied to the box and one used motion data and the ground reaction forces of one team member. The three prediction methods were based on a contact model with an optimization-based method. Using only motion data did not allow an accurate estimate due to the traction/compression force applied by each team member, which affected L5/S1 joint moments. Back loading can be estimated using motion data and the measurement of the traction/compression force with relatively small errors, comparable to the uncertainty levels reported in other studies. The traction/compression force can be obtained directly with a force measurement unit built into the object to be moved or indirectly by using force platforms on which one of the two handlers stands during the handling task. The use of the proposed prediction methods allows team manual handling tasks to be analyzed in various realistic contexts, with team members who have different anthropometric measurements and with different box characteristics.

A Human-Like Dual-Forklift Collaborative Mechanism for Container Handling

At present, container handling facilities are huge in size, low in automation, and complicated to operate, which are not suitable for applications in the limited spaces. Inspired by humans’ flexible collaboration when carrying a heavy and large object, in this article we devise a novel dual-forklift collaborative mechanism to address this problem. Specifically, we design a turn lock and hydraulic lifting structure to enable a rigid connection between each forklift and the target container, deploy a four-wheel steering structure in each forklift for omnidirectional motion, and construct a novel vision system by leveraging both RGB camera and time-of-flight depth camera. Furthermore, to provide visual feedback to the controller such that the dual forklift can connect, lift, and handle the container successfully, we devise a coarse-to-fine container detection and corner junction location model. Experiments demonstrate that our model outperforms representative models in terms of both precision and efficiency. Besides, the designed dual-forklift system can accomplish the handling tasks at narrow spaces, confirming its practical value in industrial applications.