Estimation of Walking Body Center of Mass Velocity by Means of Microwave Radars and Deep Learning.

OBJECTIVE: This study introduces an innovative framework combining microwave Doppler radar networks with deep learning to estimate three-dimensional body center of mass (BCoM) velocity. We evaluated the system's ability to derive clinical gait-quality indices, aiming for a privacy-preserving alternative to laboratory-based motion capture. METHODS: Sixty healthy adults performed treadmill walking at 2, 4, and 6 km/h, including a simulated hemiplegic gait. Three radars captured micro-Doppler spectrograms in anterior-posterior (AP), medio lateral (ML), and cranio-caudal (CC) directions. Recurrent neural networks, optimized via Bayesian techniques, estimated 3D BCoM velocities, which were validated against gold-standard motion capture. Three parameters were derived to assess gait smoothness (LDLJ), symmetry (iHR), and stability (RMS acceleration). RESULTS: The system reconstructed BCoM velocity waveforms with high fidelity and minimal bias. In particular, LDLJ achieved remarkable accuracy in the AP and CC directions, with errors below 6.1%. While medio-lateral and acceleration-derived metrics (particularly RMS) proved more sensitive to estimation noise- peaking at 22.3% error during simulated hemiplegia-the framework successfully preserved key gait-quality patterns at the group level. CONCLUSION: Integrating radar technology with sequence learning effectively recovers complex BCoM dynamics. Despite inherent challenges in acceleration-based metrics, this approach captures nuanced gait characteristics that go beyond traditional spatiotemporal parameters, maintaining clinical relevance in a non-invasive format. SIGNIFICANCE: This work represents a significant step toward unobtrusive, markerless gait monitoring. It offers a scalable, privacy-preserving solution for continuous assessment in clinical and home settings, bridging the gap between laboratory research and real-world applications.