Wearable Sensors and Artificial Intelligence for Physical Ergonomics: A Systematic Review of Literature

Diagnostics, Год журнала: 2022, Номер 12(12), С. 3048 - 3048

Опубликована: Дек. 5, 2022

Physical ergonomics has established itself as a valid strategy for monitoring potential disorders related, example, to working activities. Recently, in the field of physical ergonomics, several studies have also shown improvement experimental methods ergonomic analysis, through combined use artificial intelligence, and wearable sensors. In this regard, review intends provide first account investigations carried out using these methods, considering period up 2021. The method that combines information obtained on worker sensors (IMU, accelerometer, gyroscope, etc.) or biopotential (EMG, EEG, EKG/ECG), with analysis intelligence systems (machine learning deep learning), offers interesting perspectives from both diagnostic, prognostic, preventive points view. particular, signals, recognition categorization postural biomechanical load worker, can be processed formulate algorithms applications (especially respect musculoskeletal disorders), high statistical power. For Ergonomics, but Occupational Medicine, improve knowledge limits human organism, helping definition sustainability thresholds, design environments, tools, work organization. growth prospects research area are refinement procedures detection processing signals; expansion study assisted (assistive robots, exoskeletons), categories workers suffering pathologies disabilities; well development risk assessment exceed those currently used precision agility.

Язык: Английский

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Manufacturing Operator Ergonomics: A Conceptual Digital Twin Approach to Detect Biomechanical Fatigue

IEEE Access, Год журнала: 2022, Номер 10, С. 12774 - 12791

Опубликована: Янв. 1, 2022

The primary sources of injuries in the workplace are improper manual material handling (MMH) motions, forklift collisions, slip, and fall. This research presents a Digital Twin (DT) framework to analyze fatigue humans while performing lifting MMH activity laboratory environment for purpose reducing these types injuries. proposed methodology analyzes worker's body joints detect biomechanical as factor change back, elbow, knee joint angles. Using dynamic time warping (DTW) algorithm, angles with was analyzed. variation DTW parameters evaluated using exponentially weighted moving average (EWMA) control charts further analysis. A preliminary study considering two healthy male subjects seven experiments, each under an optical motion capture system performed test model's efficiency. Our contributions twofold. First, we propose model Secondly, also shows evidence that different individuals show signs via showcases need true personalized DT operator assessment environment.

Язык: Английский

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Data-driven ergonomic risk assessment of complex hand-intensive manufacturing processes

Communications Engineering, Год журнала: 2025, Номер 4(1)

Опубликована: Март 12, 2025

Abstract Hand-intensive manufacturing processes, such as composite layup and textile draping, require significant human dexterity to accommodate task complexity. These strenuous hand motions often lead musculoskeletal disorders rehabilitation surgeries. Here we develop a data-driven ergonomic risk assessment system focused on finger activity better identify address these risks in manufacturing. This integrates multi-modal sensor testbed that captures operator upper body pose, applied force data during hand-intensive tasks. We introduce the Biometric Assessment of Complete Hand (BACH) score, which measures with greater granularity than existing scores for posture (Rapid Upper Limb Assessment, or RULA) level (HAL). Additionally, train machine learning models effectively predict RULA HAL metrics new participants, using collected at University Washington 2023. Our system, therefore, provides interpretability enabling targeted workplace optimizations corrections improve safety.

Язык: Английский

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A Computer Vision Approach for Estimating Lifting Load Contributors to Injury Risk

IEEE Transactions on Human-Machine Systems, Год журнала: 2022, Номер 52(2), С. 207 - 219

Опубликована: Фев. 23, 2022

Safety practitioners widely use the lifting index (LI) to determine workers’ risk but are hampered by difficulties of estimating load without intervention or intrusive sensors. This study proposes a computer vision method for LI across varying loads. The proposed can also predict Brog rating perceived exertion (RPE), measure associated with load. A controlled experiment was conducted demonstrate approach. Thirty participants performed 2176 tasks at three levels. These levels were and fixing other task variables (e.g., distance). combined pose estimation (OpenPose) optical flow (SelFlow) techniques extracting participants’ body motion posture features; facial expression recognition algorithm (OpenFace) built upon action unit coding system (FACS) used extract features. extracted features develop prediction models. best-performing model an integration 1-D convolutional neural network long short-term memory network. It achieved area under curve 0.890 in classifying root mean square 2.264 predicting RPE. Critical indicators identified investigating contribution through interpretable machine learning techniques. In summary, this demonstrates nonintrusive assessment discovers behavioral that changes RPE due

Язык: Английский

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Machine learning approach to determine the decision rules in ergonomic assessment of working posture in sewing machine operators

Journal of Safety Research, Год журнала: 2023, Номер 87, С. 15 - 26

Опубликована: Авг. 22, 2023

Язык: Английский

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Human reliability modeling in occupational environments toward a safe and productive operator 4.0

International Journal of Industrial Ergonomics, Год журнала: 2023, Номер 97, С. 103479 - 103479

Опубликована: Июль 8, 2023

Язык: Английский

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Prediction of Work-Related Risk Factors among Bus Drivers Using Machine Learning

International Journal of Environmental Research and Public Health, Год журнала: 2022, Номер 19(22), С. 15179 - 15179

Опубликована: Ноя. 17, 2022

A recent development in ergonomics research is using machine learning techniques for risk assessment and injury prevention. Bus drivers are more likely than other workers to suffer musculoskeletal diseases because of the nature their jobs working conditions (WMSDs). The basic idea this study forecast important work-related variables linked WMSDs bus approaches. total 400 full-time male from east west zone depots Bengaluru Metropolitan Transport Corporation (BMTC), which based Bengaluru, south India, took part study. In total, 92.5% participants responded questionnaire. Modified Nordic Musculoskeletal Questionnaire was used gather data on symptoms WMSD during past 12 months (MNMQ). Machine including decision tree, random forest, naïve Bayes were factors related WMSDs. It discovered that characteristics statistically significant. 66.75% subjects reported having Various classifiers derive simulation results frequency pain systems throughout last with variables. With 100% accuracy, tree forest algorithms produce same results. Naïve yields 93.28% accuracy. study, through a questionnaire survey analysis, several health identified among drivers. Risk such as involvement physical activities, frequent posture change, exposure vibration, egress ingress, on-duty breaks, seat adaptability issues have highest influence due From it recommended get involved adopt healthy lifestyle, maintain proper while driving. For any transport organization/company, design driver cabins ergonomically mitigate

Язык: Английский

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MOCAP and AI-Based Automated Physical Demand Analysis for Workplace Safety

Journal of Construction Engineering and Management, Год журнала: 2024, Номер 150(7)

Опубликована: Апрель 24, 2024

Worker safety and productivity the factors that affect them, such as ergonomics, are essential aspects of construction projects. The application ergonomics identification connections between workers assigned tasks have led to a decrease in worker injuries discomfort, beneficial effects on productivity, reduction project costs. Nevertheless, area often subjected awkward body postures repetitive motions cause musculoskeletal disorders, turn leading delays production. As systematic widely used procedure generates final document or form, physical demand analysis (PDA) assesses health engaged manufacturing activities proactively evaluates ergonomic risks. However, gather necessary information, traditional PDA methods require ergonomists spend significant time observing interviewing workers. To increase speed accuracy PDA, this study focuses developing framework automatically fill posture-based form address physiological task demands. In contrast observation-based approach, proposed uses motion capture (MOCAP) system rule-based expert obtain joint angles segment positions different work situations, convert measurements objective their frequencies, then populate forms. is tested validated both laboratory on-site environments by comparing generated forms with filled out ergonomists. results indicate MOCAP-/AI-based automated successfully improves performance terms accuracy, consistency, consumption. Ultimately, can aid design job goal promoting health, safety, workplace.

Язык: Английский

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Streamlining operations management by classifying methods and concepts of Lean and Ergonomics within a sociotechnical framework

Operations Management Research, Год журнала: 2024, Номер unknown

Опубликована: Июнь 12, 2024

Abstract Companies have implemented Lean to increase efficiency and competitiveness. However, the importance of Ergonomics is often neglected, resulting in ergonomic problems lower profitability acceptance . This study presents a comprehensive approach Operations Production Management (OPM) considering sociotechnical synergies. For , literature-based main methodologies categories are defined. These methodologies/categories used as search-term combinations further literature search. divided into “Production worker” (PW), “Physical environment” (PE), “Industry 4.0 technology” (i4.0), “Company culture” (CC), “Manufacturing methods” (MM) based on metric, system (STS) concept. makes it possible determine percentage participation articles by STS category. The differences can be seen PE (Lean: 10%; Ergonomics: 24%) i4.0 29%; 15%). for PW 18%; 21%), CC 19%; 20%), MM 26%; there similarities between OPM user should manage PW, CC, factors equally with objective same. measures, professional separation Lean/OPM Ergonomics/Occupational Medicine does not make sense. Concerning i4.0, danger that human factor (especially innovation-oriented) will unjustly neglected too much emphasis placed supposedly human-free technology.

Язык: Английский

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A Dataset of Human Motion and Muscular Activities in Manual Material Handling Tasks for Biomechanical and Ergonomic Analyses

IEEE Sensors Journal, Год журнала: 2021, Номер 21(21), С. 24731 - 24739

Опубликована: Сен. 15, 2021

Manual Material Handling (MMH) activities represent a large portion of the workers' tasks in tertiary sector. The ability to monitor, model, and predict human behaviours are crucial both design productive human-robot collaboration an efficient physical exposure assessment system that can prevent Work-related Musculoskeletal Disorders (WMSDs), with ultimate goal improving quality life. combined use wearable sensors machine learning (ML) techniques fulfil these purposes. Inertial Measurement Units (IMUs) surface Electromyography (sEMG) allow collecting kinematic data muscular activity information be used for biomechanical analyses, ergonomic risk assessment, as input ML algorithms aimed at joint torque/load estimation, Human Activity Recognition (HAR). latter needs amount annotated training samples, publicly available datasets is way forward. Nowadays, majority them concern Activities Daily Life (ADLs) and, including only data, have limited applications. This paper presents fully labelled dataset working include full-body kinematics from 17 IMUs upper limbs sEMG 16 channels. Fourteen subjects participated experiment performed laboratory settings overall 18.6 hours recordings. divided into two sets. first includes lifting, lowering, carrying objects, MMH suitable HAR. second isokinetic arm movements, mainly targeting load torque estimation.

Язык: Английский

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