The accuracy and predictability of micro Doppler radar signature projection algorithm measuring functional movement in NCAA athletes

Background: 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. Research Question: Can micro Doppler radar predictably and accurately identify subtle differences in movement conditions? Methods: 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. Results: 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. Conclusion: 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.



Work Title The accuracy and predictability of micro Doppler radar signature projection algorithm measuring functional movement in NCAA athletes
Open Access
  1. Cayce Onks
  2. Donald Hall
  3. Tyler Ridder
  4. Zacharie Idriss
  5. Joseph Andrie
  6. Ram Narayanan
License CC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives)
Work Type Article
  1. Gait and Posture
Publication Date March 1, 2021
Publisher Identifier (DOI)
Deposited November 23, 2021




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  • Created
  • Added The_accuracy_and_predictability_of_micro_Doppler_radar_signature_projection_algorithm_measuring_functional_movement_in_NCAA_athletes.pdf
  • Added Creator Cayce Onks
  • Added Creator Donald Hall
  • Added Creator Tyler Ridder
  • Added Creator Zacharie Idriss
  • Added Creator Joseph Andrie
  • Added Creator Ram Narayanan
  • Published
  • Updated
  • Updated