Quantitative Elbow Spasticity Measurement Based on Muscle Activation Estimation Using Maximal Voluntary Contraction

Conventional measurement of spasticity in stroke patients, e.g., modified Ashworth scale (MAS), has been challenged about its reliability issues. Surface electromyography (sEMG) has been used to identify neuromuscular abnormalities since it directly measures electrical activity in the muscle, however its performance is affected by the placement of electrodes and crosstalk, and it cannot detect the activities of deep muscles. This study proposes a novel spasticity measurement method by quantifying the difference between the impaired and unaffected sides in the elbow maximal voluntary contraction (MVC) task. Five inertial measurement units (IMUs) and a force sensor were used to capture the movement dynamics for the MVC test, by which a neuromusculoskeletal model is established to estimate the muscle activation using the inverse dynamics and optimization techniques. Normalized keeping time of peak activation is a quantitative feature that identifies the disparity between the impaired and unaffected side in the MVC test is defined as a measurement of spasticity. Six stroke patients and eight healthy subjects were recruited to evaluate the muscle activation estimation model. The outcomes of our measurement for patients were compared with the spasticity rated by an experienced physical therapist (PT) using MAS. The estimated muscle activation shows promising accuracy compared to the sEMG profiles (patients: mean $R^{2} \approx 0.705$ ; healthy: mean $R^{2} \approx 0.91$ ). The outcomes of our approach are highly correlated with MAS (Pearson’s $r \approx 0.96$ , $p < 0.05$ ). These findings indicate that our approach can provide a quantitative measure of spasticity and can be used as a complementary measurement along with the existing clinical methods. This approach will also enhance the efficiency of upper limb robot-aided rehabilitation in stroke patients.