RESUMEN
Current methods for fall risk assessment rely on Quantitative Gait Analysis (QGA) using costly optical tracking systems, which are often only available at specialized laboratories that may not be easily accessible to rural communities. Radar placed in a home or assisted living facility can acquire continuous ambulatory recordings over extended durations of a subject's natural gait and activity. Thus, radar-based QGA has the potential to capture day-to-day variations in gait, is time efficient and removes the burden for the subject to come to a clinic, providing a more realistic picture of older adults' mobility. Although there has been research on gait-related health monitoring, most of this work focuses on classification-based methods, while only a few consider gait parameter estimation. On the one hand, metrics that are accurately and easily computable from radar data have not been demonstrated to have an established correlation with fall risk or other medical conditions; on the other hand, the accuracy of radar-based estimates of gait parameters that are well-accepted by the medical community as indicators of fall risk have not been adequately validated. This paper provides an overview of emerging radar-based techniques for gait parameter estimation, especially with emphasis on those relevant to fall risk. A pilot study that compares the accuracy of estimating gait parameters from different radar data representations - in particular, the micro-Doppler signature and skeletal point estimates - is conducted based on validation against an 8-camera, marker-based optical tracking system. The results of pilot study are discussed to assess the current state-of-the-art in radar-based QGA and potential directions for future research that can improve radar-based gait parameter estimation accuracy.
RESUMEN
BACKGROUND: Low levels of sensory noise applied to the skin through electrical stimulation (ES) can improve balance control through a mechanism called stochastic resonance (SR). Little is known regarding the extent subsensory ES can improve reactive control of balance after unanticipated balance perturbations and the best location where to apply the stimulation. RESEARCH QUESTIONS: How efficient is subsensory ES in improving reactive control of balance following visual perturbations delivered in a virtual reality (VR) environment? 2) Does lower trunk stimulation have greater effects than lower legs stimulation? METHODS: Eighteen healthy young adults stood on a force plate while wearing a Valve Index VR headset in eyes closed (EC), eyes open (EO), eyes open with anteroposterior visual perturbations (AP) and eyes open with mediolateral visual perturbations (ML) conditions. No-stimulation (NS), leg stimulation (LS), or trunk stimulation (TS) equal to 90% of the sensory threshold (ST) was applied. The 95% confidence ellipse area (95%EA), the lengths of AP and ML sway path (APPath, MLPath), and the AP and ML 50% and 95% power frequencies (APPF50, MLPF50, APPF95, and MLPF95) were calculated. Repeated-measures ANOVA and Tukey post-hoc tests were used to analyze the main and interaction effects of stimulation and visual conditions. RESULTS: During AP perturbations, participants showed higher frequencies, longer paths, and larger ellipse areas. TS caused lower APPF50, MLPF50, MLPF95, APPath and EA while LS caused lower MLPF50 and EA. During ML perturbations, TS reduced APPF50 and both LS and TS caused reduction of MLPF95. Higher instability following AP perturbations was associated with greater effects of TS and LS. SIGNIFICANCE: The application of subsensory ES improved postural control during AP perturbations and TS reduced postural sway more effectively than LS. TS may be an effective strategy to enhance balance control during reactive postural tasks, thus potentially reducing fall risk.