Snyder Kristian, Thomas Brennan, Lu Ming-Lun, Jha Rashmi, Barim Menekse S, Hayden Marie, Werren Dwight
Department of Electrical Engineering and Computing Systems, University of Cincinnati, Cincinnati, Ohio, United States of America.
National Institute for Occupational Safety and Health, Cincinnati, Ohio, United States of America.
PLoS One. 2021 Feb 19;16(2):e0247162. doi: 10.1371/journal.pone.0247162. eCollection 2021.
Occupationally-induced back pain is a leading cause of reduced productivity in industry. Detecting when a worker is lifting incorrectly and at increased risk of back injury presents significant possible benefits. These include increased quality of life for the worker due to lower rates of back injury and fewer workers' compensation claims and missed time for the employer. However, recognizing lifting risk provides a challenge due to typically small datasets and subtle underlying features in accelerometer and gyroscope data. A novel method to classify a lifting dataset using a 2D convolutional neural network (CNN) and no manual feature extraction is proposed in this paper; the dataset consisted of 10 subjects lifting at various relative distances from the body with 720 total trials. The proposed deep CNN displayed greater accuracy (90.6%) compared to an alternative CNN and multilayer perceptron (MLP). A deep CNN could be adapted to classify many other activities that traditionally pose greater challenges in industrial environments due to their size and complexity.
职业性背痛是工业生产率下降的主要原因。检测工人何时进行不正确的提举动作以及背部受伤风险增加，可能会带来显著的好处。这些好处包括因背部受伤率降低而提高工人的生活质量，以及减少雇主的工伤赔偿索赔和误工时间。然而，由于加速度计和陀螺仪数据通常数据集较小且潜在特征细微，识别提举风险具有挑战性。本文提出了一种使用二维卷积神经网络（CNN）且无需手动特征提取的新颖方法来对提举数据集进行分类；该数据集由10名受试者组成，他们在与身体不同相对距离处进行提举，总共进行了720次试验。与另一种CNN和多层感知器（MLP）相比，所提出的深度CNN显示出更高的准确率（90.6%）。深度CNN可进行调整，以对许多其他活动进行分类，这些活动由于其规模和复杂性，传统上在工业环境中带来更大挑战。
