Gurbuz Sevgi Z, Rahman Mohammad Mahbubur, Bassiri Zahra, Martelli Dario
Department of Electrical and Computer EngineeringUniversity of Alabama Tuscaloosa AL 35487 USA.
Advanced Radar Systems Team of Aptiv Corporation Kokomo IN 13085 USA.
IEEE Open J Eng Med Biol. 2024 Jun 3;5:735-749. doi: 10.1109/OJEMB.2024.3408078. eCollection 2024.
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.
当前的跌倒风险评估方法依赖于使用昂贵光学跟踪系统的定量步态分析（QGA），而这些系统通常仅在专业实验室才有，农村社区可能难以使用。放置在家庭或辅助生活设施中的雷达可以在受试者自然步态和活动的较长时间段内获取连续的动态记录。因此，基于雷达的QGA有潜力捕捉步态的日常变化，效率高，且消除了受试者前往诊所的负担，能更真实地呈现老年人的行动能力。尽管已有关于步态相关健康监测的研究，但大部分工作集中在基于分类的方法上，只有少数研究考虑步态参数估计。一方面，尚未证明能从雷达数据准确且轻松计算出的指标与跌倒风险或其他医疗状况有既定关联；另一方面，医学界认可的作为跌倒风险指标的基于雷达的步态参数估计的准确性尚未得到充分验证。本文概述了新兴的基于雷达的步态参数估计技术，尤其着重于与跌倒风险相关的技术。基于与一个8摄像头、基于标记的光学跟踪系统的验证，进行了一项初步研究，比较从不同雷达数据表示（特别是微多普勒特征和骨骼点估计）估计步态参数的准确性。讨论了初步研究的结果，以评估基于雷达的QGA的当前技术水平以及未来研究的潜在方向，这些研究可以提高基于雷达的步态参数估计的准确性。
