论文标题
一种深度学习方法，用于手动提升期间的背痛风险预测
A deep learning approach for lower back-pain risk prediction during manual lifting
论文作者
论文摘要
职业引起的背痛是行业生产力降低的主要原因。检测工人何时不正确地提起且遭受背部伤害的风险增加会带来很大的可能收益。这些包括由于较低的背部受伤率和工人的薪酬索赔减少以及雇主错过的时间而增加了工人的生活质量。但是，由于通常小的数据集和加速度计和陀螺仪数据中的微妙的基础功能，因此认识到提升风险带来了挑战。一种新的方法，可以使用2D卷积神经网络（CNN）对提升数据集进行分类，并且在本文中没有提出手动特征提取。该数据集由10名受试者组成，在与人体的各个相对距离上举行，共有720个试验。与替代CNN和多层感知器（MLP）相比，提出的深CNN的精度（90.6％）显示出更高的精度（90.6％）。可以将深入的CNN改编成对许多其他活动进行分类，这些活动传统上由于其规模和复杂性而在工业环境中提出了更大的挑战。
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.
