Ultrasound Features of Skeletal Muscle Can Predict Kinematics of Upcoming Lower-Limb Motion
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Annals of Biomedical Engineering (Ó 2020) https://doi.org/10.1007/s10439-020-02617-7
Original Article
Ultrasound Features of Skeletal Muscle Can Predict Kinematics of Upcoming Lower-Limb Motion M. HASSAN JAHANANDISH,1 KAITLIN G. RABE,1 NICHOLAS P. FEY,1,2,3 and KENNETH HOYT 1,4 1
Department of Bioengineering, The University of Texas at Dallas, Richardson, TX 75080, USA; 2Department of Mechanical Engineering, The University of Texas at Dallas, Richardson, TX, USA; 3Department of Physical Medicine and Rehabilitation, UT Southwestern Medical Center, Dallas, TX, USA; and 4Department of Radiology, UT Southwestern Medical Center, Dallas, TX, USA (Received 30 April 2020; accepted 10 September 2020) Associate Editor Thurmon E. Lockhart oversaw the review of this article.
Abstract—Seamless integration of lower-limb assistive devices with the human body requires an intuitive humanmachine interface, which would benefit from predicting the intent of individuals in advance of the upcoming motion. Ultrasound imaging was recently introduced as an intuitive sensing interface. The objective of the present study was to investigate the predictability of joint kinematics using ultrasound features of the rectus femoris muscle during a nonweight-bearing knee extension/flexion. Motion prediction accuracy was evaluated in 67 ms increments, up to 600 ms in time. Statistical analysis was used to evaluate the feasibility of motion prediction, and the linear mixed-effects model was used to determine a prediction time window where the joint angle prediction error is barely perceivable by the sample population, hence clinically reliable. Surprisingly, statistical tests revealed that the prediction accuracy of the joint angle was more sensitive to temporal shifts than the accuracy of the joint angular velocity prediction. Overall, predictability of the upcoming joint kinematics using ultrasound features of skeletal muscle was confirmed, and a time window for a statistically and clinically reliable prediction was found between 133 and 142 ms. A reliable prediction of user intent may provide the time needed for processing, control planning, and actuation of the assistive devices at critical points during ambulation, contributing to the intuitive behavior of lower-limb assistive devices. Keywords—Ultrasound imaging, Skeletal muscle, Motion prediction, Human-machine interface.
Address correspondence to Nicholas P. Fey, and Kenneth Hoyt, Department of Bioengineering, The University of Texas at Dallas, Richardson, TX 75080, USA. Electronic mails: nicholas.fey@ utdallas.edu, [email protected] Nicholas P. Fey and Kenneth Hoyt share co-senior responsibilities over this work.
INTRODUCTION Approximately 11.4% of the world population—an estimated 877 million people—face moderate to extreme difficulty with their daily mobility.50 Lower-limb assistive devices hold the promise to enhance activity and community involvement of this population.6,33 However, effective human-device integration is still limited by the lack of a reliable interface between th
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