Online classification and sensor selection optimization with applications to human material handling tasks using wearable sensing technologies

Abstract

Occupational jobs often involve different types of manual material handling (MMH) tasks. Performing such tasks can be physically demanding, and which may put workers at an increased risk of work-related musculoskeletal disorders (WMSDs). To control and prevent WMSDs, there has been a growing interest in online posture monitoring using wearable sensors. In this paper, we developed an online, supervised, task classification algorithm for monitoring and evaluation of MMH activities. The classification algorithm is based on a fast sparse estimation methodology, which makes it computationally efficient for online decision making. We further propose an optimization approach to improve classification performance, by differentially weighting sensors, thereby representing the relative influence of a sensor in classification performance. Optimizing these weights enables us to determine the most relevant sensors for classification. A case study using 37 sensors with 111 channels of data was completed to validate performance of the proposed method. With only 30 optimally selected sensor channels, our method provides high classification accuracy (>84%) and outperforms several benchmark methods, including support vector machine, quadratic discriminant analysis, and neural network.