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Gait analysis is one of the most basic methods for assessing a patient&#x2019;s biopsychological status. Doctors can distinguish people with mental and neurological disorders by monitoring their gait. To perform gait analysis in a more quantitative and accurate manner, many studies have used inertial measurement units (IMUs), cameras and ground reaction force platforms. However, conventional gait analysis requires sensors to be attached to the subject&#x2019;s body, and some of them are cost prohibitive. Currently, studies of noncontact gait analysis using radar sensors are being performed. Such studies have successfully measured several gait parameters associated with the noncontact method but have been unable to distinguish between individual legs. In this study, we proposed a method for noncontact gait analysis with a treadmill that could separate the left and right legs using multi-input and multi-output frequency-modulated continuous-wave (MIMO FMCW) radar. By recognizing two legs in a range-Doppler map and estimating their angles, ranges and velocities, the gait parameters of the individual legs could be identified. We performed experiments with 15 participants in 4 scenarios (walking, running, left leg limping, right leg limping) and compared gait parameters obtained using FMCW radar and IMUs. The gait parameter measurements were validated using the intraclass correlation value, and they showed excellent agreement except for flight time. Moreover, a parameter was identified that can accurately detect gait asymmetry, and its sensitivity (0.83) and specificity (1.00) were validated. Our future research will analyze not only feet movement but also arm movement so that it can be further applied to the medical field.

Noncontact Extraction of Biomechanical Parameters in Gait Analysis Using a Multi-Input and Multi-Output Radar Sensor