Abstract
In this study, a wearable prototype system was developed for multiple-gesture rehabilitation using electrical stimulation controlled by a volitional surface electromyography (sEMG) scan of a healthy forearm. The purpose of the prototype system is to reconstruct multiple gestures of a paralysed limb and to simplify the positioning of sEMG detection sites on a healthy forearm. A self-designed eight-channel sEMG detection armband was used to detect the sEMG signal distributions of the muscle groups in healthy forearms. Linear discriminant analysis (LDA) was used to classify the sEMG signal distributions corresponding to different gestures, and then the classification results were mapped to corresponding stimulation channels. The sEMG signal with the maximum root mean square (RMS) was used as the source of stimulus coding for each gesture. Our proposed mean absolute value (MAV)/number of slope sign changes (NSS) dual-coding (MNDC) algorithm was used to encode the sEMG signal into an electrical stimulus with a dynamic pulse width and frequency. The constant-current stimulation armband electrically stimulated multiple muscles in the affected forearm by means of a circuit designed with a time-division multiplexed stimulation channel. An experiment involving 6 able-bodied volunteers showed that when the detection armband was located near the middle of the forearm, the gesture classification accuracy was greater than 90%, and each active sEMG signal was high. Gesture bridge experiments, including grasping, wrist flexion, wrist extension and finger extension, were carried out among six hemiplegic subjects and between one able-bodied volunteer acting as a controller and each of six stroke patients as the controllee. Both sets of results show that the proposed system can reconstruct these four gestures in the controlled subject with a delay of at most 360 ms and with a correlation coefficient of >0.72.
Highlights
Functional electrical stimulation (FES) is a neurorehabilitation technique that is frequently used to maintain and restoreThe associate editor coordinating the review of this manuscript and approving it for publication was Yizhang Jiang .limb motor function in patients with stroke and spinal cord injury (SCI) [1], [2]
We propose an eight-channel detection armband that surrounds the forearm, and we select the channel with the largest root mean square (RMS) signal for each gesture as the detection site to simplify the positioning of the detection sites
The mean success rates for the reconstruction of wrist extension and wrist flexion (93.3 ± 2.5% and 90.8 ± 3.7%, respectively) were significantly higher than those for finger extension and grasping (74.1 ± 13.9% and 64 ± 23.2%, respectively, ∗∗∗P < 0.001), as shown in Fig. 12, partly because the flexor carpi radialis and flexor carpi ulnaris are located on the dorsal and palmar surfaces of the forearm, respectively, and are easy to recruit through electrical stimuli
Summary
Functional electrical stimulation (FES) is a neurorehabilitation technique that is frequently used to maintain and restoreThe associate editor coordinating the review of this manuscript and approving it for publication was Yizhang Jiang .limb motor function in patients with stroke and spinal cord injury (SCI) [1], [2]. Several sEMG-controlled FES systems have been proposed, such as EMG-triggered FES [5], [6], proportional EMG-controlled FES [7] and contralaterally controlled FES (CCFES) [8]. In these techniques, patients’ volitional sEMG signals are employed to trigger or regulate the FES system to assist the affected limb’s movement. According to the theory of Hebbian plastic connections [9], [10], sEMG-controlled FES can gradually improve the motor rehabilitation effect by strengthening the synchronous activation of the central and peripheral nervous systems over time
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