TY - JOUR
T1 - Lower Limb Motion Estimation Using Ultrasound Imaging
T2 - A Framework for Assistive Device Control
AU - Jahanandish, Mohammad Hassan
AU - Fey, Nicholas P.
AU - Hoyt, Kenneth
N1 - Funding Information:
Manuscript received August 29, 2018; revised November 19, 2018; accepted December 26, 2018. Date of publication January 9, 2019; date of current version November 6, 2019. This work was supported by the National Institutes of Health under NIH/NCI Grant R21CA212851 and NIH/NIBIB Grant K25EB017222. (Corresponding author: Kenneth Hoyt.) M.H. Jahanandish is with the Department of Bioengineering, University of Texas at Dallas, Richardson, TX 75080 USA (e-mail:, mhja-hanand@utdallas.edu).
Publisher Copyright:
© 2013 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Objective: Powered assistive devices need improved control intuitiveness to enhance their clinical adoption. Therefore, the intent of individuals should be identified and the device movement should adhere to it. Skeletal muscles contract synergistically to produce defined lower limb movements, so unique contraction patterns in lower extremity musculature may provide a means of device joint control. Ultrasound (US) imaging enables direct measurement of the local deformation of muscle segments. Hence, the objective of this study was to assess the feasibility of using US to estimate human lower limb movements. Methods: A novel algorithm was developed to calculate US features of the rectus femoris muscle during a non-weight-bearing knee flexion/extension experiment by nine able-bodied subjects. Five US features of the skeletal muscle tissue were studied, namely thickness, angle between aponeuroses, pennation angle, fascicle length, and echogenicity. A multiscale ridge filter was utilized to extract the structures in the image and a random sample consensus (RANSAC) model was used to segment muscle aponeuroses and fascicles. A localization scheme further guided RANSAC to enable tracking in a US image sequence. Gaussian process regression models were trained using segmented features to estimate both knee joint angle and angular velocity. Results: The proposed segmentation-estimation approach could estimate knee joint angle and angular velocity with an average root mean square error value of 7.45° and 0.262 rad/s, respectively. The average processing rate was 3-6 frames/s that is promising toward real-time implementation. Conclusion: Experimental results demonstrate the feasibility of using US to estimate human lower extremity motion. The ability of the algorithm to work in real time may enable the use of US as a neural interface for lower limb applications. Significance: Intuitive intent recognition of human lower extremity movements using wearable US imaging may enable volitional assistive device control and enhance locomotor outcomes for those with mobility impairments.
AB - Objective: Powered assistive devices need improved control intuitiveness to enhance their clinical adoption. Therefore, the intent of individuals should be identified and the device movement should adhere to it. Skeletal muscles contract synergistically to produce defined lower limb movements, so unique contraction patterns in lower extremity musculature may provide a means of device joint control. Ultrasound (US) imaging enables direct measurement of the local deformation of muscle segments. Hence, the objective of this study was to assess the feasibility of using US to estimate human lower limb movements. Methods: A novel algorithm was developed to calculate US features of the rectus femoris muscle during a non-weight-bearing knee flexion/extension experiment by nine able-bodied subjects. Five US features of the skeletal muscle tissue were studied, namely thickness, angle between aponeuroses, pennation angle, fascicle length, and echogenicity. A multiscale ridge filter was utilized to extract the structures in the image and a random sample consensus (RANSAC) model was used to segment muscle aponeuroses and fascicles. A localization scheme further guided RANSAC to enable tracking in a US image sequence. Gaussian process regression models were trained using segmented features to estimate both knee joint angle and angular velocity. Results: The proposed segmentation-estimation approach could estimate knee joint angle and angular velocity with an average root mean square error value of 7.45° and 0.262 rad/s, respectively. The average processing rate was 3-6 frames/s that is promising toward real-time implementation. Conclusion: Experimental results demonstrate the feasibility of using US to estimate human lower extremity motion. The ability of the algorithm to work in real time may enable the use of US as a neural interface for lower limb applications. Significance: Intuitive intent recognition of human lower extremity movements using wearable US imaging may enable volitional assistive device control and enhance locomotor outcomes for those with mobility impairments.
KW - Lower-limb assistive robots
KW - machine learning
KW - motion estimation
KW - rehabilitation robotics
KW - ultrasound imaging
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U2 - 10.1109/JBHI.2019.2891997
DO - 10.1109/JBHI.2019.2891997
M3 - Article
C2 - 30629522
AN - SCOPUS:85070512754
SN - 2168-2194
VL - 23
SP - 2505
EP - 2514
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
IS - 6
M1 - 8606176
ER -