Prediction of distal lower-limb motion using ultrasound-derived features of proximal skeletal muscle

Mohammad Hassan Jahanandish, Nicholas P. Fey, Kenneth Hoyt

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

4 Scopus citations


Control of lower-limb assistive devices would benefit from predicting the intent of individuals in advance of upcoming motion, rather than estimating the current states of their motion. Human lower-limb motion estimation using ultrasound (US) image derived features of skeletal muscle has been demonstrated. However, predictability of motion in time remains an open question. The objective of this study was to assess the predictability of distal lower-limb motion using US image features of rectus femoris (RF) muscle during non-weight-bearing knee flexion/extension. A series of time shifts was introduced between the US features and the joint position in 67 ms steps from 0 ms (i.e., estimation, no prediction) up to predicting 467 ms in advance. A US-based algorithm to estimate lower-limb motion was then used to predict the knee joint position in time using the US features after introducing the time shifts. The accuracy of joint motion prediction after each time shift was compared to the accuracy of joint motion estimation. The reliability of the prediction was then assessed using an analysis of variance (ANOVA) test. The motion prediction accuracy was found to be reliable up to 200 ms, where the average root mean square error (RMSE) of prediction across 9 healthy subjects was 0.89 degrees greater than the average RMSE (7.39 degrees) of motion estimation for the same group of subjects. These findings suggest a reliable prediction of upcoming lower-limb motion is feasible using the US features of skeletal muscle up to a certain point. A reliable prediction may provide lower-limb assistive device control systems with a time-window for processing and control planning, and actuation hence improving the volitional control behaviors of lower-limb assistive devices.

Original languageEnglish (US)
Title of host publication2019 IEEE 16th International Conference on Rehabilitation Robotics, ICORR 2019
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9781728127552
StatePublished - Jun 2019
Event16th IEEE International Conference on Rehabilitation Robotics, ICORR 2019 - Toronto, Canada
Duration: Jun 24 2019Jun 28 2019

Publication series

NameIEEE International Conference on Rehabilitation Robotics
ISSN (Print)1945-7898
ISSN (Electronic)1945-7901


Conference16th IEEE International Conference on Rehabilitation Robotics, ICORR 2019

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Rehabilitation
  • Electrical and Electronic Engineering


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