To study the complex neuromuscular control pathways in human movement, biomechanical parametric models and system identification methods are employed. Although test-retest reliability is widely used to validate the outcomes of motor control tasks, it was not incorporated in system identification methods. This study investigates the feasibility of incorporating test-retest reliability in our previously published method of selecting sensitive parameters. We consider the selected parameters via this novel approach to be the key neuromuscular parameters, because they meet three criteria: reduced variability, improved goodness of fit, and excellent reliability. These criteria ensure that the parameter variability is below a user-defined value, the number of these parameters is maximized to enhance goodness of fit, and their test-retest reliability is above a user-defined value. We measured variability, the goodness of fit, and reliability using Fisher information matrix, variance accounted for, and intraclass correlation, respectively. We also incorporated model diversity as a fourth optional criterion to narrow down the solution space of key parameters. We applied this approach to the head position tracking tasks in axial rotation and flexion/extension. A total of forty healthy subjects performed the tasks during two visits. With variability and reliability measures ≤0.35 and ≥0.75, respectively, we selected three key parameters out of twelve with the goodness of fit >69%. The key parameters were associated with at least two neuromuscular pathways out of four modeled pathways (visual, proprioceptive, vestibular, and intrinsic), which is a measure of model diversity. Therefore, it is feasible to incorporate reliability and diversity in system identification of key neuromuscular pathways in our application.
|Number of pages||8|
|Journal||IEEE Transactions on Neural Systems and Rehabilitation Engineering|
|Publication status||Published - 2019|
Bibliographical noteFunding Information:
Manuscript received July 2, 2018; revised November 22, 2018; accepted January 1, 2019. Date of publication January 10, 2019; date of current version February 8, 2019. This work was supported in part by the National Center for Complementary and Integrative Health (NCCIH) at the National Institutes of Health under Grant U19AT006057. The work of J. Choi was supported in part by the Mid-career Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT under Grant NRF-2018R1A2B6008063. (Corresponding author: Jongeun Choi.) A. Ramadan is with the Maryland Robotics Center, University of Maryland, College Park, MD 20742 USA (e-mail: firstname.lastname@example.org).
© 2019 IEEE.
All Science Journal Classification (ASJC) codes
- Internal Medicine
- Biomedical Engineering