Research Unit of Advanced Robotics and Human-Centred Technologies, Università Campus Bio-Medico di Roma, 00128 Rome, Italy.
Sensors (Basel). 2021 Nov 16;21(22):7600. doi: 10.3390/s21227600.
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.
本文旨在通过提出 WGD-Working Gesture Dataset（工作手势数据集）来强调人体运动监测对于预防肌肉骨骼疾病的重要性，该数据集是一个公开可用的生产线工作手势数据集，旨在用于工人运动学分析。它包含使用光电运动捕捉系统执行生产线工作活动的健康受试者获得的运动学数据。所获得的数据用于提取定量指标，以评估工作任务的执行情况，并检测有用信息以估计可能导致肌肉骨骼疾病发生的因素的暴露情况。所得到的结果表明，所提出的指标可用于及早检测到不正确的姿势和姿势，从而预防与工作相关的疾病。该方法是通用的，不依赖于所采用的运动分析系统。它旨在提供安全执行工作活动的指示。例如，通过采用非侵入性测量系统（例如可穿戴传感器），通过提取指标和阈值，也可以使用所提出的 WGD 来评估实际工作环境中的工人的运动学。
