Multi-level information fusion with motion constraints: Key to achieve high-precision gait analysis using low-cost inertial sensors

Gait can reflect locomotion and physical condition and thus is used to assess people's health. Traditional high-precision gait analysis devices are expensive and are limited to laboratory conditions, so the trend is to use wearable devices to analyze gait. The existing wearable inertial-based gait-analysis methods have stride-length and foot-clearance estimation accuracy of 2.0 cm to 5.0 cm (in root mean square) and have limitations. They have not considered the regularity of the movement between the toe and the heel, the varying dual-feet distance due to foot dynamics, and the diversity of sensors used. Involving these factors, this paper achieves an improved gait-analysis system that provides stride-length and foot-clearance accuracy of 1.5 cm and 1.0 cm, respectively. Such accuracy is state-of-the-art for low-cost inertial systems and is even competitive with those from visual-sensor-based gait-analysis systems. A key to the proposed method is a new multi-level information fusion architecture and the extraction of human-walking constraints. The information-fusion architecture involves data fusion from the sensor, single-foot, and dual-foot levels. Two gait-characteristic-based motion constraints are presented to achieve such fusion, including the toe-heel constant distance constraint and the dual-foot flexible distance constraint. To implement these constraints, a constrained Kalman filter is constructed. The corresponding hardware system has been designed using multiple dollar-level inertial measurement units.