Biomechanical Risk Classification in Repetitive Lifting Using Multi-Sensor Electromyography Data, Revised National Institute for Occupational Safety and Health Lifting Equation, and Deep Learning

Biosensors, Год журнала: 2025, Номер 15(2), С. 84 - 84

Опубликована: Фев. 1, 2025

Repetitive lifting tasks in occupational settings often result shoulder injuries, impacting both health and productivity. Accurately assessing the biomechanical risk of these remains a significant challenge ergonomics, particularly within manufacturing environments. Traditional assessment methods frequently rely on subjective reports limited observations, which can introduce bias yield incomplete evaluations. This study addresses limitations by generating utilizing comprehensive dataset containing detailed time-series electromyography (EMG) data from 25 participants. Using high-precision wearable sensors, EMG were collected eight muscles as participants performed repetitive tasks. For each task, index was calculated using revised National Institute for Occupational Safety Health (NIOSH) equation (RNLE). Participants completed cycles low-risk high-risk four-minute period, allowing muscle performance under realistic working conditions. extensive dataset, comprising over 7 million points sampled at approximately 1259 Hz, leveraged to develop deep learning models classify risk. To provide actionable insights practical ergonomics assessments, statistical features extracted raw data. Three models, Convolutional Neural Networks (CNNs), Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), employed analyze predict level. The CNN model achieved highest performance, with precision 98.92% recall 98.57%, proving its effectiveness real-time assessments. These findings underscore importance aligning architectures characteristics optimize management. By integrating sensors this enables precise, real-time, dynamic significantly enhancing workplace safety protocols. approach has potential improve planning reduce incidence severity work-related musculoskeletal disorders, ultimately promoting better outcomes across various settings.

Язык: Английский

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Wearable Sensors in Industrial Ergonomics: Enhancing Safety and Productivity in Industry 4.0

Sensors, Год журнала: 2025, Номер 25(5), С. 1526 - 1526

Опубликована: Фев. 28, 2025

The fourth industrial revolution has transformed ergonomics through the adoption of wearable technologies to enhance workplace safety and well-being. This study conducts a comprehensive scoping review, structured according PRISMA guidelines, examining how devices are revolutionizing ergonomic practices within Industry 4.0. After analyzing 1319 articles from major databases including SpringerLink, MDPI, Scopus, IEEEXplore, 36 relevant studies were selected for detailed analysis. review specifically focuses on improve worker comfort safety, promoting more productive work environments. findings reveal that have significantly impacted conditions in settings, with artificial intelligence integration showing highest presence analyzed applications. Over past years, technology implementations demonstrated 38% improvement optimizing compared traditional approaches.

Язык: Английский

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Integrating exoskeletons in the construction sector: a systematic review of empirical evaluation tools and future directions

Engineering Construction & Architectural Management, Год журнала: 2024, Номер unknown

Опубликована: Дек. 30, 2024

Purpose This study aims to evaluate and synthesize literature on exoskeleton implementation in the construction industry understand their current applications, existing research approaches identify critical areas for future investigation. Through a comprehensive analysis of empirical studies, seeks establish clear roadmap advancing adoption work. Design/methodology/approach conducts systematic review following Preferred Reporting Items Systematic Reviews Meta-Analysis (PRISMA) guidelines. By searching relevant databases applying predefined inclusion criteria, focused studies that effectiveness acceptance exoskeletons construction. Both objective parameters (EMG data, Kinematic analysis, heart rate) subjective (user comfort, perceived exertion, usability surveys) were analyzed assess how impactful are among workers. Findings The identified 236 publications, which 36 included, revealing several insights: (1) A significant reliance conducted controlled environments, accounting 77.78% studies. (2) limited representation actual workers, mainly non-construction worker volunteers, may affect practical relevance findings. (3) gap exists standardized evaluation protocols, with researchers using varying assessment methods hinder cross-study comparisons. (4) Predominantly short-term nature These findings highlight need more real-world testing, frameworks longitudinal Originality/value contributes original insights into deployment sector, particularly highlighting industry's direct, situ engagement It suggests should prioritize long-term, onsite achieve understanding exoskeletons’ impacts, thus supporting development targeted intervention strategies reducing work-related musculoskeletal disorders (WMSDs)

Язык: Английский

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Recent Advances in Ergonomic Studies on Material Handling: Mitigating Musculoskeletal Risks and Enhancing Worker Safety

Deleted Journal, Год журнала: 2024, Номер 6, С. 52 - 64

Опубликована: Дек. 31, 2024

Manual material handling (MMH) tasks are a significant contributor to work-related musculoskeletal disorders (WMSDs), particularly in industries where repetitive motions, awkward postures, and excessive loads common. Recent advances ergonomic interventions aim mitigate these risks, enhancing worker safety reducing the incidence of injuries. The integration automation technologies, such as robotic assistants human-machine interfaces, has proven effective human involvement monotonous tasks, thereby alleviating physical strain. Additionally, passive back-support exoskeletons have emerged promising tools provide mechanical support during heavy lifting, bending, movements, effectively risks. Technological innovations, including wearable sensors AI-driven tools, further improved assessments by providing real-time monitoring feedback on workers’ posture movements. These advancements allow for timely adjustments preventive measures, ensuring safer more efficient working environment. However, challenges remain regarding long-term effects user acceptance other interventions. Studies also highlight importance risk assessments, utilizing Rapid Entire Body Assessment (REBA) fuzzy logic models identify high-risk tasks.

Язык: Английский

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