Related references
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Summary: High movement velocities are a significant risk factor for work-related musculoskeletal disorders. Ergonomists typically use two methods to calculate angular movement velocities of the upper arms. However, neither method accurately captures the full extent of upper arm angular velocity. A new method called gyroscope vector magnitude (GVM) is proposed, which captures angular velocities around all motion axes and more accurately represents the true angular velocities of the upper arm. Optical motion capture data demonstrates that the previous methods capture only 89% and 77% relative to the proposed method.
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Howard Chen et al.
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IEEE EMBEDDED SYSTEMS LETTERS
(2023)
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Amir Nourmohammadi et al.
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Victor C. H. Chan et al.
Summary: This scoping review analyzed 130 primary research studies on the applications of machine learning techniques for preventing work-related musculoskeletal disorders, revealing that ML techniques mainly contribute to the development of interventions. This study provides insights into the breadth of ML techniques in primary WMSD prevention and helps identify directions for future research and development.
APPLIED ERGONOMICS
(2022)
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Han Cui et al.
Summary: This paper presents a novel human posture estimation system using mmWave radars, which can detect people with arbitrary postures in indoor environments at close distances and estimate the posture by localizing the key joints.
IEEE SENSORS JOURNAL
(2022)
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Yuqian Lu et al.
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IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
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Enrique Coronado et al.
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Sandra Grabowska et al.
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Guoyang Zhou et al.
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IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS
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IEEE ROBOTICS AND AUTOMATION LETTERS
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J. Oyekan et al.
Summary: High value manufacturing systems still rely heavily on manual ergonomically intensive activities, especially in industries like aerospace where workers are subjected to awkward forces and postures for long periods of time. The integration of wearable sensor systems and cognitive architecture can track human ergonomics in real time and provide real-time feedback to workers when ergonomic rules are violated.
JOURNAL OF MANUFACTURING SYSTEMS
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Mark C. Schall et al.
Summary: The study compared the levels of posture and movement speed exposure between manufacturing workers primarily performing cyclic and non-cyclic tasks, and explored exposure variance patterns within and between workers in each group. Results showed significantly higher movement speeds for workers performing predominantly cyclic tasks and greater exposure variability within the non-cyclic group.
APPLIED ERGONOMICS
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Li Li et al.
Summary: This study successfully predicted lifting postures using generative models, demonstrating reasonable accuracy and validity in posture prediction. The predicted postures can support biomechanical analysis and ergonomics assessment to reduce the risk of low back injuries.
IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS
(2021)
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Computer Science, Artificial Intelligence
Xuan Wang et al.
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IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS
(2021)
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Haeseok Jeong et al.
Summary: A novel sensor-embedded smart chair system has been developed to monitor and classify a worker's sitting postures in real time. By utilizing seat cushion pressure sensors and seatback distance sensors, the system achieves significantly superior classification performance compared to benchmark systems. The low-cost system could be utilized for various applications, including the development of a real-time posture feedback system for preventing sitting-related musculoskeletal disorders.
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Kamel Aouaidjia et al.
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