Normalization of gait data is performed to reduce the effects of intersubject variations due to physical characteristics. This study reports a multiple regression normalization approach for spatiotemporal gait data that takes into account intersubject variations in self-selected walking speed and physical properties including age, height, body mass, and sex. Spatiotemporal gait data including stride length, cadence, stance time, double support time, and stride time were obtained from healthy subjects including 782 children, 71 adults, 29 elderly subjects, and 28 elderly Parkinson’s disease (PD) patients. Data were normalized using standard dimensionless equations, a detrending method, and a multiple regression approach. After normalization using dimensionless equations and the detrending method, weak to moderate correlations between walking speed, physical properties, and spatiotemporal gait features were observed (0.01 < |r| < 0.88), whereas normalization using the multiple regression method reduced these correlations to weak values (|r| < 0.29). Data normalization using dimensionless equations and detrending resulted in significant differences in stride length and double support time of PD patients; however the multiple regression approach revealed significant differences in these features as well as in cadence, stance time, and stride time. The proposed multiple regression normalization may be useful in machine learning, gait classification, and clinical evaluation of pathological gait patterns.
Ferdous Wahid, Saman Halgamuge, and David C. Ackland are with the Department of Mechanical Engineering, University of Melbourne, Australia. Rezaul Begg is with the College of Sport and Exercise Science, Victoria University, Australia. Noel Lythgo is with the School of Medical Sciences, RMIT University, Australia. Chris J. Hass is with the Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, USA.