Classifying diverse manual material handling tasks using a single wearable sensor

Abstract

The use of inertial measurement units (IMUs) for monitoring and classifying physical activities has received substantial attention in recent years, both in occupational and non-occupational contexts. However, a “user-friendly” approach is needed to promote this approach to quantify physical demands in actual workplaces. We explored the use of a single IMU for extracting information about different manual material handling (MMH) tasks (i.e., specific type of task performed, and associated duration and frequency), using a bidirectional long short-term memory network for classification. Classification performance using single IMUs placed on several body parts was compared with performance using multiple IMU configurations (2, 3, and 17 IMUs). Overall, the use of a single sensor led to satisfactory results (e.g., median accuracy >97%) in classifying MMH tasks and estimating task duration and frequency. Limited benefits were obtained using additional sensors, and several sensor locations yielded similar outcomes. Classification performance, though, was relatively inferior for push/pull vs. other tasks.