Gait Analysis Using mmWave Radar: A Skeleton-Based Approach with Point Cloud Transformer.

This study presents a skeleton-based approach for gait analysis using millimeter-Wave radar sensors. mmWave radar is non-intrusive, privacy-preserving, unaffected by lighting conditions, and both cost-effective and energy-efficient. Current radar-based gait analysis methods typically use micro-Doppler signatures to identify walking phases and extract features. In contrast, our approach leverages a pose-estimation model to reconstruct the human skeleton from radar point cloud data, enabling comprehensive full-body analysis. This enables a more intuitive and detailed gait analysis while also allowing for direct signal-level comparisons with wearable sensor-based approaches. In our study, we recruited 78 participants and conducted gait tests across four distinct environments to evaluate the effectiveness of the proposed method. The results showed that gait features, including stride time, stride length, and stride velocity, exhibited good to excellent Intraclass Correlation Coefficients (ICCs) when compared with wearable sensors. Additionally, we are the first to analyze sub-phase features such as swing, stance, and double support using a radar system. Our findings show that mmWave radar sensors can accurately capture stride-level gait features, while their performance is less effective for detailed sub-phase analysis. This study underscores the significant potential of mmWave radar for gait analysis in older adults, providing a low-cost, non-contact, and privacy-preserving solution.