Step length measurement in the wild using FMCW radar

TL;DR

This work introduces a radar-based method to measure step length in real-world home environments for frail older adults, addressing the gap that prior radar approaches largely focused on gait speed in controlled settings. The pipeline leverages radar point-cloud detections, DBSCAN-Hungarian-Kalman tracking, and torso Doppler profiling to compute step length via torso speed peak-to-peak distances on radially aligned tracks, with distinct processing for clinic and home contexts. In-clinic validation with 35 frail participants against a 4 m Zeno Walkway reports a mean error of 4.5 cm and ICC(2,k)=0.83 for reliability, while in-home validation with 21 participants shows week-to-week reliability ICC(2,k)=0.91 and inter-context agreement ICC(3,k)=0.81, demonstrating feasibility of privacy-preserving, in-home step-length monitoring. The results underscore the potential of continuous, in-home gait monitoring to support frailty, fall risk, and hospitalization risk prediction, though home-track availability depends on room geometry and radar orientation, indicating avenues for hardware and algorithmic refinements.

Abstract

With an aging population, numerous assistive and monitoring technologies are under development to enable older adults to age in place. To facilitate aging in place predicting risk factors such as falls, and hospitalization and providing early interventions are important. Much of the work on ambient monitoring for risk prediction has centered on gait speed analysis, utilizing privacy-preserving sensors like radar. Despite compelling evidence that monitoring step length, in addition to gait speed, is crucial for predicting risk, radar-based methods have not explored step length measurement in the home. Furthermore, laboratory experiments on step length measurement using radars are limited to proof of concept studies with few healthy subjects. To address this gap, a radar-based step length measurement system for the home is proposed based on detection and tracking using radar point cloud, followed by Doppler speed profiling of the torso to obtain step lengths in the home. The proposed method was evaluated in a clinical environment, involving 35 frail older adults, to establish its validity. Additionally, the method was assessed in people's homes, with 21 frail older adults who had participated in the clinical assessment. The proposed radar-based step length measurement method was compared to the gold standard Zeno Walkway Gait Analysis System, revealing a 4.5cm/8.3% error in a clinical setting. Furthermore, it exhibited excellent reliability (ICC(2,k)=0.91, 95% CI 0.82 to 0.96) in uncontrolled home settings. The method also proved accurate in uncontrolled home settings, as indicated by a strong agreement (ICC(3,k)=0.81 (95% CI 0.53 to 0.92)) between home measurements and in-clinic assessments.

Figures (19)

• Figure 1: The forward velocity and acceleration of the center of mass during a single gait cycle. The peak to peak distance of velocity and acceleration is equivalent to one step length. Illustration based on speed profile and gait descriptions given in winter1987biomechanicss21041264.
• Figure 2: In clinic setup of the 4 meter ProtoKinetics Zeno Walkway Gait Analysis System and Chirp Smart Home Sensor for testing concurrent validity of step length measurement. Obstacles are only used for obstacle walks. Narrow walk pathway is used for narrow walking scenario only.
• Figure 3: Placement of Chirp sensor in the room.
• Figure 4: Step length measurement methodology: Radar signal processing generates 3D point clouds with speeds, enabling detection and tracking of individuals. In the home, linear track segments along the radar's radial axis are isolated, while in the clinic, track segments along Zeno Walkway's linear path are extracted. Step length is determined as the peak-to-peak distance of torso speed.
• Figure 5: Tracking illustration: Radar point clouds are clustered to form detections, which are associated to tracks through the Hungarian Algorithm, and tracked using Kalman Filtering. The floor is depicted with a 1m by 1m checkerboard pattern, while Zeno Walkway is represented by a cyan rectangle.