A deep learning approach for lower back-pain risk prediction during manual lifting
This addresses the challenge of detecting incorrect lifting to reduce back injuries for workers and employers, though it is incremental as it applies an existing deep learning method to a specific domain.
The paper tackled the problem of predicting lower back-pain risk during manual lifting by proposing a 2D convolutional neural network (CNN) that achieved 90.6% accuracy on a dataset of 10 subjects and 720 trials, outperforming alternative models.
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.