Inertia Sensors for Measuring Spasticity of the Ankle Plantarflexors Using the Modified Tardieu Scale-A Proof of Concept Study

Ankle spasticity is clinically assessed using goniometry to measure the angle of muscle reaction during the Modified Tardieu Scale (MTS). The precision of the goniometric method is questionable as the measured angle may not represent when the spastic muscle reaction occurred. This work proposes a method to accurately determine the angle of muscle reaction during the MTS assessment by measuring the maximum angular velocity and the corresponding ankle joint angle, using two affordable inertial sensors. Initially we identified the association between muscle onset and peak joint angular velocity using surface electromyography and an inertial sensor. The maximum foot angular velocity occurred 0.049 and 0.032 s following the spastic muscle reaction for Gastrocnemius and Soleus, respectively. Next, we explored the use of two affordable inertial sensors to identify the angle of muscle reaction using the peak ankle angular velocity. The angle of muscle reaction and the maximum dorsiflexion angle were significantly different for both Gastrocnemius and Soleus MTS tests (p = 0.028 and p = 0.009, respectively), indicating that the system is able to accurately detect a spastic muscle response before the end of the movement. This work successfully demonstrates how wearable technology can be used in a clinical setting to identify the onset of muscle spasticity and proposes a more accurate method that clinicians can use to measure the angle of muscle reaction during the MTS assessment. Furthermore, the proposed method may provide an opportunity to monitor the degree of spasticity where the direct help of experienced therapists is inaccessible, e.g., in rural or remote areas.

Analysis of Machine Learning-Based Assessment for Elbow Spasticity Using Inertial Sensors

Spasticity is a frequently observed symptom in patients with neurological impairments. Spastic movements of their upper and lower limbs are periodically measured to evaluate functional outcomes of physical rehabilitation, and they are quantified by clinical outcome measures such as the modified Ashworth scale (MAS). This study proposes a method to determine the severity of elbow spasticity, by analyzing the acceleration and rotation attributes collected from the elbow of the affected side of patients and machine-learning algorithms to classify the degree of spastic movement; this approach is comparable to assigning an MAS score. We collected inertial data from participants using a wearable device incorporating inertial measurement units during a passive stretch test. Machine-learning algorithms-including decision tree, random forests (RFs), support vector machine, linear discriminant analysis, and multilayer perceptrons-were evaluated in combinations of two segmentation techniques and feature sets. A RF performed well, achieving up to 95.4% accuracy. This work not only successfully demonstrates how wearable technology and machine learning can be used to generate a clinically meaningful index but also offers rehabilitation patients an opportunity to monitor the degree of spasticity, even in nonhealthcare institutions where the help of clinical professionals is unavailable.