Department of Economics, Engineering, Society and Business Organization (DEIM), University of Tuscia, 01100 Viterbo, VT, Italy.
ErgoCert-Ente di Certificazione per l'Ergonomia srl, 33100 Udine, UD, Italy.
Sensors (Basel). 2020 Mar 16;20(6):1643. doi: 10.3390/s20061643.
OCRA (OCcupational Repetitive Action) is currently one of the most widespread procedures for assessing biomechanical risks related to upper limb repetitive movements. Frequency factor of the technical actions represents one of the OCRA elements. Actually, the frequency factor computation is based on workcycle video analysis, which is time-consuming and may lead to up to 30% of intra-operator variability. This paper aims at proposing an innovative procedure for the automatic counting of dynamic technical actions on the basis of inertial data. More specifically, a threshold-based algorithm was tested in four industrial case studies, involving a cohort of 20 workers. Nine combinations of the algorithm were tested by varying threshold values related to time and amplitude. The computation of frequency factor showed an average relative error lower than 5.7% in all industrial-based case studies after the appropriate selection of the time and amplitude threshold values. These findings open the possibility to use the threshold-based algorithm proposed here for the automatic computation of OCRA frequency factor, avoiding the time efforts in video analysis.
OCRA（职业重复性动作）目前是评估与上肢重复性动作相关的生物力学风险的最广泛程序之一。技术动作的频率因素是 OCRA 要素之一。实际上，频率因素的计算基于工作周期视频分析，这既耗时又可能导致高达 30%的操作员内变异性。本文旨在提出一种基于惯性数据的自动计算动态技术动作的创新方法。具体来说，基于阈值的算法在四个工业案例研究中进行了测试，涉及 20 名工人的队列。通过改变与时间和幅度相关的阈值，测试了该算法的九种组合。在适当选择时间和幅度阈值后，所有基于工业的案例研究中的频率因素计算平均相对误差均低于 5.7%。这些发现为使用这里提出的基于阈值的算法自动计算 OCRA 频率因素提供了可能性，从而避免了视频分析的时间投入。
