Abstract
This paper wants to stress the importance of human movement monitoring to prevent musculoskeletal disorders by proposing the WGD—Working Gesture Dataset, a publicly available dataset of assembly line working gestures that aims to be used for worker’s kinematic analysis. It contains kinematic data acquired from healthy subjects performing assembly line working activities using an optoelectronic motion capture system. The acquired data were used to extract quantitative indicators to assess how the working tasks were performed and to detect useful information to estimate the exposure to the factors that may contribute to the onset of musculoskeletal disorders. The obtained results demonstrate that the proposed indicators can be exploited to early detect incorrect gestures and postures and, consequently to prevent work-related disorders. The approach is general and independent on the adopted motion analysis system. It wants to provide indications for safely performing working activities. For example, the proposed WGD can also be used to evaluate the kinematics of workers in real working environments thanks to the adoption of unobtrusive measuring systems, such as wearable sensors through the extracted indicators and thresholds.
Highlights
The analysis of human motor behaviour is being looked into by the scientific community, as it is transversely in touch with several research fields
Understanding and replicating the human motion behaviour is a common objective among researchers of vary disciplines such as biomechanics, ergonomics, action recognition and computer vision applications up to robotics
The introduction of a benchmark for the correct postures that people should have during their working activities could help identify kinematics quantities that can be linked to musculoskeletal overloads
Summary
The analysis of human motor behaviour is being looked into by the scientific community, as it is transversely in touch with several research fields. Understanding and replicating the human motion behaviour is a common objective among researchers of vary disciplines such as biomechanics, ergonomics, action recognition and computer vision applications up to robotics. The obtained information can be used for different purposes, such as performance and motor recovery evaluation, or robot motion control [2,3]. The information gathered from healthy subjects can, for instance, constitute a benchmark for performance evaluations of pathological individuals executing the same tasks [4] or for posture evaluations during working activities. The introduction of a benchmark for the correct postures that people should have during their working activities could help identify kinematics quantities that can be linked to musculoskeletal overloads
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