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

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.