<p>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.</p>
