Towards Radar-Agnostic Gait Analysis Across UWB and FMCW Systems

Radar sensing has emerged in recent years as a promising solution for unobtrusive and continuous in-home gait monitoring. This study evaluates whether a unified processing framework can be applied to radar-based spatiotemporal gait analysis independent of radar modality. The framework is validated using collocated impulse-radio ultra-wideband (IR-UWB) and frequency-modulated continuous-wave (FMCW) radars under identical processing settings, without modality-specific tuning, during repeated overground walking trials with 10 healthy participants. A modality-independent approach for automatic walking-segment identification is also introduced to ensure fair and reproducible modality performance assessment. Clinically relevant spatiotemporal gait parameters, including stride time, stride length, walking speed, swing time, and stance time, extracted from each modality were compared against gold-standard motion capture reference estimates. Across all parameters, both radar modalities achieved comparably high mean estimation accuracy in the range of 85-98%, with inter-modality differences remaining below 4.1%, resulting in highly overlapping accuracy distributions. Correlation and Bland-Altman analyses revealed minimal bias, comparable limits of agreement, and strong agreement with reference estimates, while intraclass correlation analysis demonstrated high consistency between radar modalities. These findings indicate that no practically meaningful performance differences arise from radar modality when using a shared processing framework, supporting the feasibility of radar-agnostic gait analysis systems.

A mixed-signal analogue front-end for brain-implantable neural interfaces using a digital fixed-point IIR filter and bulk offset cancellation

Advances in miniaturised implantable neural electronics have paved the way for therapeutic brain-computer interfaces with clinical potential for movement disorders, epilepsy, and broader neurological applications. This paper presents a mixed-signal analogue front end (AFE) designed to record both extracellular action potentials (EAPs) and local field potentials (LFPs). The feedforward path integrates a low-noise amplifier (LNA) and a successive-approximation-register (SAR) analogue-to-digital converter (ADC), while the feedback path employs a fixed-point infinite-impulse-response (IIR) Chebyshev Type II low-pass filter to suppress sub-mHz components via bulk-voltage control of the LNA input differential pair using two R-2R pseudo-resistor digital-to-analogue converters (DACs). The proposed AFE achieves up to 41.42dB gain, consumes 2.178uA per channel, occupies 0.198mm2 per channel, and supports neural signal monitoring from 0.1Hz to 10kHz with 3.59uVrms input-referred integrated noise.