Biomechanical Parameters Estimation for Real-Time Gait Analysis Using a Compact Radar Sensor

Abstract

Compact radar sensors for the Internet of Things (IoT) applications can be used to analyze indoor human gait characteristics. Conventional human gait analysis methods typically involve generating 2-D high-resolution time-frequency images and employing image processing techniques to estimate the gait parameters of a walking human. However, these computations can be resource-intensive for compact radar sensors. To address this problem, we propose a new scheme for estimating gait parameters. Our method has four significant contributions: 1) utilization of 1-D phase modulation in a radar echo for efficient gait parameter estimation, as opposed to relying on 2-D time-frequency images; 2) decomposition of microphase modulations corresponding to the torso or pelvis and lower body parts (e.g., knee, tibia, and ankle) using dedicated filtering techniques to mitigate the interference between body components; 3) compensation for effects of nonlinear macrophase modulation caused by whole-body movements; and 4) robust estimation of gait parameters, including time-varying radial velocity, gait rate, step length, and the height of the lower body. In experiments performed using a 5.8-GHz continuous-wave (CW) Doppler radar, we observed that the proposed scheme can perform efficient and robust gait parameter estimation of indoor human walking. © 2025 IEEE.