A deep learning approach for lower back-pain risk prediction during manual lifting.
A deep learning approach for lower back-pain risk prediction during manual lifting.
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DOI：
10.1371/journal.pone.0247162
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
Werren D
中科院分区：
文献类型：
--
作者：
Snyder K;Thomas B;Lu ML;Jha R;Barim MS;Hayden M;Werren D
职业性背痛是导致工业生产力下降的主要原因。当工人不正确地举起并且背部受伤的风险增加时，检测到这一点会带来显著的可能益处。这些措施包括提高生活质量的工人，由于较低的背部受伤率和较少的工人的赔偿要求和错过的时间为雇主。然而，由于加速度计和陀螺仪数据中通常较小的数据集和微妙的潜在特征，识别提升风险带来了挑战。本文提出了一种使用2D卷积神经网络（CNN）对提升数据集进行分类的新方法，而无需手动特征提取;该数据集由10个受试者在距离身体不同的相对距离处进行提升，总共进行了720次试验。与替代CNN和多层感知器（MLP）相比，所提出的深度CNN显示出更高的准确性（90.6%）。深度CNN可以适用于对许多其他活动进行分类，这些活动由于其规模和复杂性而传统上在工业环境中构成更大的挑战。
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.
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DOI：
10.1016/j.apmr.2006.07.259
发表时间：
2006-10-01
影响因子：
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