A Kinematic Model to Predict a Continuous Range of Human-Like Walking Speed Transitions

While constant speed gait is well understood, far less is known about how humans change walking speed. It is also unknown if the transition steps smoothly morph between speeds, or if they are unique. Using data from a prior study in which subjects transitioned between five speeds while walking on a treadmill, joint kinematic data were decomposed into trend and periodic components. The trend captured the time-varying nature of the gait, and the periodic component captured the cyclic nature of a stride. The start and end of the transition were found by detecting where the trend diverged from a ±2 standard deviation band around the mean of the pre- and post-transition trend. On average, the transition started within half a step of when the treadmill changed speed (
${p\ll {0}.{001}}$
for equivalence test). The transition length was 2 to 3 steps long. A predictive kinematic model was fit to the experimental data using Bezier polynomials for the trend and Fourier series for the periodic component. The model was fit using 1) only constant speed walking, 2) only speed transition steps, and 3) a random sample of five step types and then validated using the complement of the training data. Regardless of the training set, the model accurately predicted untrained gaits (normalized RMSE
${ < {0}.{4} \approx {2}^{\circ} }$
, normalized maximum error generally
${ < {1}.{5} \approx {7}.{5}^{\circ} }$
). Because the errors were similar for all training sets, this implies that joint kinematics smoothly morph between gaits when humans change speed.