Predicting Individualized Joint Kinematics over Continuous Variations of
Walking, Running, and Stair Climbing
Abstract
GOAL: Accounting for gait individuality is important to positive
outcomes with wearable robots, but tuning multi-activity models is
time-consuming and not viable in a clinic. Generalizations can be made
to predict gait individuality in unobserved conditions. METHODS:
Kinematic individuality—how one person’s joint angles differ from the
group—is quantified for every subject, joint, ambulation mode
(walking, running, stair ascent, and stair descent), and intramodal task
(speed, incline) in an open-source able-bodied dataset. Four N-way
ANOVAs test how prediction methods affect the fit to experimental data
between and within ambulation modes. We test whether walking
individuality carries across modes, or whether a modal prediction is
more effective against average kinematics. RESULTS: Kinematic
individualization improves fit across joint and task if we consider each
mode separately. Across all modes, tasks, and joints, individualization
improved the fit in 81% of trials, improving the fit on average by 4.3º
across the gait cycle. This was statistically significant at all joints
for walking and running, and half the joints for stair ascent/descent.
CONCLUSIONS: Kinematic individualization tends to improve fit across all
joints and can be easily predicted by observing only one task within an
ambulation mode.