Use of Sonomyography for Continuous Estimation of Hip, Knee and Ankle Moments during Multiple Ambulation Tasks

Kaitlin G. Rabe, Mohammad Hassan Jahanandish, Kenneth Hoyt, Nicholas P. Fey

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

1 Scopus citations

Abstract

Accurate user intent recognition is vital to the success of achieving volitional control of rehabilitation robotics. Real-time ultrasound (US) imaging of skeletal muscle, or sonomyography, is an alternative noninvasive sensing mechanism for device control. The objective of this study was to evaluate sonomyography for continuous estimation of hip, knee and ankle-joint moments during multiple ambulation tasks. Ten able-bodied subjects completed level, incline and decline walking while equipped with a portable US transducer on their anterior thigh. Multiple time-intensity features were extracted from US images of the knee extensor muscles collected during the three ambulation tasks. Hip, knee and ankle moments were continuously estimated by Gaussian process regression models in both fully subject-dependent and partially subject independent frameworks. A two-way analysis of variance was completed to assess the effect of subject independence as well as joint level (hip/knee/ankle) on the moment estimation. Subject Dependent regression models resulted in the lowest error for estimation of hip, knee and ankle moment during all three ambulation tasks in comparison to partially subject-independent regression models (p<0.01). Remarkably, within the subject dependent regression models there was no significant difference in the mean error of moment estimation when comparing across the three joints, with mean percent errors as low as 0.74%, 0.68%, and 3.02% for the hip, knee, and ankle, respectively. Despite only capturing sonomyographic features from the anterior thigh, this high-dimensional sensing data can be used to accurately estimate changes in both proximal and distal joint kinetics during varying ambulation tasks.

Original languageEnglish (US)
Title of host publication2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020
PublisherIEEE Computer Society
Pages1134-1139
Number of pages6
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
CountryUnited States
CityNew York City
Period11/29/2012/1/20

ASJC Scopus subject areas

  • Artificial Intelligence
  • Biomedical Engineering
  • Mechanical Engineering

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