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.
Repetitive occupational lifting has been shown to create an increased risk for incidence of back pain. Ergonomic workstations that promote proper lifting technique can reduce risk, but it is difficult to assess the workstations without constant risk monitoring. Machine learning systems using inertial measurement unit (IMU) data have been successful in various human activity recognition (HAR) applications, but limited work has been done regarding tasks for which it is difficult to collect significant amounts of data, such as manual lifting tasks. In this article, we discuss why traditional methods of data expansion may fail to improve performance on IMU data, and we present a machine learning system capable of detecting lifting action for assessing the risk for back pain using a relatively small amount of data. The proposed models outperform baseline HAR models and function on raw time-series data with minimal preprocessing for efficient real-time application.
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