Pennsylvania State University College of Medicine, 500 University Drive, P.O. Box 850, Hershey, PA, 17033-0850, United States.
Pennsylvania State University College of Engineering, 202 EE East Building, The Pennsylvania State University, University Park, PA, 16802, United States.
Gait Posture. 2021 Mar;85:96-102. doi: 10.1016/j.gaitpost.2021.01.021. Epub 2021 Jan 26.
Development of accessible cost-effective technology to objectively, reliably, and accurately predict musculoskeletal injury risk could aid the effort to prevent chronic pain and disability. Recent work on micro-Doppler radar suggests it merits investigation towards these goals. The micro-Doppler signals that are created can infer differences in gross movements such as walking versus crawling in military settings where direct vision is not possible. Unique micro-Doppler signals may be able to identify more subtle movement patterns which would not be easily seen by the human eye.
Can micro Doppler radar predictably and accurately identify subtle differences in movement conditions?
This is a cross sectional study recruiting NCAA athletes to jump in front of the micro-Doppler radar barefoot, with shoes, and shoes with a heel lift. The micro-Doppler radar signature projection algorithm was developed to determine whether the radar is able to distinguish the three distinct movement patterns.
Confusion matrices were used to visualize the performance of the support-vector machine at the 80/20 test/train split correctly classifying barefoot subjects, shoes and heel lift, and shoes correctly at 0° with respect to the radar 90.9 %, 86.7 %, and 89.5 % of the time, respectively. At 90° with respect to the radar, it was successful 94.1 %, 100 %, and 80 % of the time, respectively.
This study suggests that the micro-Doppler radar signature projection algorithm is highly accurate and able to predict subtle differences in movement that are not readily observed with conventional motion capture systems. Future studies are needed to better understand if micro-Doppler signals can identify pathologic movement patterns or movement that is associated with increased risk of injury.
开发一种具有成本效益、易于使用的技术，客观、可靠、准确地预测肌肉骨骼损伤风险，有助于预防慢性疼痛和残疾。最近的微多普勒雷达研究表明，该技术值得进一步研究以实现上述目标。该技术产生的微多普勒信号可以推断出行走与爬行等大体运动的差异，而这些差异在军事环境中是无法直接观察到的。独特的微多普勒信号或许能够识别更细微的运动模式，这些模式是肉眼难以察觉的。
微多普勒雷达能否可预测且准确地识别运动条件的细微差异？
这是一项横断面研究，招募 NCAA 运动员在微多普勒雷达前赤脚、穿鞋和穿高跟鞋跳跃。微多普勒雷达特征投影算法用于确定雷达是否能够区分三种不同的运动模式。
混淆矩阵用于可视化支持向量机在 80/20 测试/训练分割中的性能，正确分类赤脚、穿鞋和高跟鞋以及以雷达 0°角的运动模式的准确率分别为 90.9%、86.7%和 89.5%。以雷达 90°角的运动模式的准确率分别为 94.1%、100%和 80%。
本研究表明，微多普勒雷达特征投影算法具有高度准确性，能够预测常规运动捕捉系统难以观察到的细微运动差异。需要进一步研究以更好地了解微多普勒信号是否可以识别病理运动模式或与增加受伤风险相关的运动。
