A deep learning approach for lower back-pain risk prediction during manual lifting

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.

Automated summary

Occupationally-induced back pain is a major cause of decreased productivity in industry. Detecting incorrect lifting techniques can lead to benefits such as lower back injury rates for workers and fewer compensation claims for employers. A novel method using a deep CNN showed higher accuracy in classifying lifting data compared to other methods, and could potentially be adapted for other challenging activities in industrial environments.