Machine Learning-Based Real-Time Walking Activity and Posture Estimation in Construction With a Single Wearable Inertial Measurement Unit

Construction workers regularly perform walking locomotion on level and inclined surfaces. It is critical to detect walking activity and estimate body postures in real time for monitoring workers’ safety and health conditions. This article presents a machine learning-based framework for real-time activity detection and posture estimation during human walking on level and sloped terrains using a single wearable inertial measurement unit (IMU). The framework integrates recurrent neural networks with Gaussian process dynamical models to achieve accurate predictions of walking activity, floor slope angles, and workers’ turning angles and full-body limb joint angles estimation in real time. The proposed design offers a streamlined, cost-effective solution with significant advantages over multi-sensor systems. Extensive experiments of different walking activities on level and sloped surfaces are conducted to validate and demonstrate the design. The proposed algorithm detects gait activities with 96% accuracy, the estimated human limb joint angle errors are within 11 deg, the predicted turning angles have an error less than 16 deg and the end-to-end detection latency is within 21 ms using only one single IMU attached to the human shank.