Recurrence quantification analysis of motor learning and training

The goal of this study was to apply recurrence quantification analysis (RQA) to surface electromyographic (sEMG) signals during motor learning and training activities. It has been previously demonstrated that the RQA variable, percentage of determinism (�T), is related to the synchronization of motor units. It is suggested that �T will change throughout the motor learning and training process. As a result, the experiment consisted of two separate parts. The motor learning part required a male subject to train using the Nintendo Wii Fit® software, Wii Fit® balance board and the Nintendo Wii® gaming console. The myoelectric signals were acquired from the peroneus longus (PL) and soleusu (S) muscles. During the course of this experiment a soccor simulator and three in-game balance tests were used to evaluate motor learning. The second part of the experiment consisted of a chronic incomplete spinal cord injured patient from Toronto Rehabilitation Institute. The subject trained three times a week for fourteen days. Each training session consisted of the subject performing weighted dorsal and plantar flexion. Both parts of the experiments suggests that there is a decrease in synchronization of motor units after motor learning and training (decrease in �T). Additionally, the time course of �T displayed a convergence of levels between the right PL and right S during the virtual environment training. It is concluded that RQA demonstrates the ability to detect motor learning and training. Possible applications for the use of RQA on sEMG signal could be the evaluation of rehabilitation programs. By monitoring the �T, it may be possible to determine if a particular rehabilitation program is effective for a patient. This could lead to customizable programs, suited for a specific person, in order to increase the rate of recovery.