Generation of Healthy Human Gait Patterns Based on Gait Speed, Sex, Age, and Body Mass Index

With the worldwide population ageing the already raising demand for rehabilitation of movement dis- orders and motion assistance is expected to further increase. Motion models and normative reference movement patterns, especially with respect to gait, are thereby an essential factor for the research on wearable robotic systems and novel approaches to assess and assist the movement task of walking. In this paper, a methodology to generate healthy human gait motion is presented. It is hypothesized that, in addition to gait speed, also the body parameters sex, age and physique have significant and predictable influence on the gait pattern. A model of the human lower body, described as a kinematic chain, is used together with cumulative data from various gait datasets. Due to the cyclic nature of locomotion patterns, Fourier series are utilized to reproduce the motion of the individual gait cycles in terms of joint angle trajectories. For each Fourier coefficient and for male and female persons, regression parameters with significance for gait speed, age and body mass index are identified. This allows for the synthesis of physiologically consistent reference gait patterns based on a prescribed gait speed and given body pa- rameters, which are deemed particularly useful for the development and simulation of novel approaches regarding and gait assistance and rehabilitation.