Predicting Individualized Joint Kinematics over a Continuous Range of Slopes and Speeds

Emma Reznick, Kyle Embry, Robert D. Gregg

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations

Abstract

Individuality in clinical gait analysis is often quantified by an individual's kinematic deviation from the norm, but it is unclear how these deviations generalize across different walking speeds and ground slopes. Understanding individuality across tasks has important implications in the tuning of prosthetic legs, where clinicians have limited time and resources to personalize the kinematic motion of the leg to therapeutically enhance the wearer's gait. This study seeks to determine an efficient way to predictively model an individual's kinematics over a continuous range of slopes and speeds given only one personalized task at level ground. We were able to predict the kinematics of able-bodied individuals at a wide variety of conditions that were not specifically tuned. Applied to 10 human subjects, the individualization method reduced the RMSE between the model and subject's kinematics over all tasks by an average of 2% (max 52%) at the ankle, 27% (max 59%) at the knee, and 45% (max 83%) at the hip. Our results indicate that knowing how an individual subject differs from the average subject at level ground alone is enough information to improve kinematic predictions across all tasks. This research offers a new method for personalizing robotic prosthetic legs over a variety of tasks without the need of an engineer, which could make these complex devices more clinically viable.

Original languageEnglish (US)
Title of host publication2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020
PublisherIEEE Computer Society
Pages666-672
Number of pages7
ISBN (Electronic)9781728159072
DOIs
StatePublished - Nov 2020
Event8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020 - New York City, United States
Duration: Nov 29 2020Dec 1 2020

Publication series

NameProceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics
Volume2020-November
ISSN (Print)2155-1774

Conference

Conference8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020
Country/TerritoryUnited States
CityNew York City
Period11/29/2012/1/20

ASJC Scopus subject areas

  • Artificial Intelligence
  • Biomedical Engineering
  • Mechanical Engineering

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