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 manually tuning multi-activity models is time-consuming and not viable in a clinic. Generalizations can possibly 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-access dataset with 10 able-bodied subjects. Four N-way ANOVAs test how prediction methods affect the fit to experimental data between and within ambulation modes. We test whether walking individuality (measured at a single speed on level ground) carries across modes

SUBMITTER: Reznick E 

PROVIDER: S-EPMC9928215 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature