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The accuracy and predictability of micro Doppler radar signature projection algorithm measuring functional movement in NCAA athletes
Journal article   Open access   Peer reviewed

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

Cayce Onks, Donald Hall, Tyler Ridder, Zacharie Idriss, Joseph Andrie and Ram Narayanan
Gait & posture, v 85, pp 96-102
Mar 2021
DOI: https://doi.org/10.1016/j.gaitpost.2021.01.021
PMID: 33524666
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://scholarsphere.psu.edu/resources/4a9990a5-b954-46e2-96da-49459eb7ae65View
SubmittedCC BY-NC-ND V4.0,  Open

Abstract

Feature extraction Micro-Doppler radar Injury prevention Machine learning Musculoskeletal injury risk
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.

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6 citations in Web of Science
7 citations in Scopus

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

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Web of Science research areas
Neurosciences
Orthopedics
Sport Sciences
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