Injury Prevention clinical trials

Can Micro-Doppler Predict Human Movement?

The analysis of human motion using radar has become an increasingly active topic of study due to the diverse applications offered by such an analysis (Lai et al., 2008; Fairchild & Narayanan, 2016; Narayanan et al., 2014). Information about human motion has important applications for urban military operations, search-and-rescue missions, surveillance, and hospital patient monitoring. The micro-motions of human movement in the presence of radar illumination creates unique modulations in the received signal known as the micro-Doppler effect. By analyzing these frequency modulations, one can infer the type of movement being performed. This micro-motion associated with human movement produces a nonlinear and non-stationary signal that can be characterized using time-frequency domain analysis. Such signals will be used to identify high injury risk versus low injury risk athletes, which creates an opportunity to direct limited prevention resources to these high-risk athletes; identify individuals at risk of falls; and, may even be useful in diagnosing conditions such as Parkinson's where asymmetrical movement patterns occur as an early indicator. Traditional methods of movement analysis involve the use of expensive video motion capture systems that accurately measure the 3-dimensional position of passive reflective markers affixed to human body landmarks such as joints and body segments, and while motion capture systems are used to effectively estimate movement dynamics, they are generally not portable, they are expensive, and they can be cumbersome when the reflective markers are applied to older persons or persons with movement deficiencies. Drs. Narayanan and Onks have successfully tested a novel use of Doppler radar that is portable, less expensive, and eliminates the need for affixing cumbersome reflective markers to participants. In addition, preliminary testing has demonstrated the ability to discriminate between certain movement conditions at a level of precision we feel are not obtainable with video motion capture.