Textile electrodes are becoming an attractive means in the facilitation of surface electrical stimulation. However, the stimulation comfort of textile electrodes and the mechanism behind stimulation discomfort is still unknown. In this study, a textile stimulation electrode was developed using conductive fabrics and then its impedance spectroscopy, stimulation thresholds, and stimulation comfort were quantitatively assessed and compared with those of a wet textile electrode and a hydrogel electrode on healthy subjects. The equivalent circuit models and the finite element models of different types of electrode were built based on the measured impedance data of the electrodes to reveal the possible mechanism of electrical stimulation pain. Our results showed that the wet textile electrode could achieve similar stimulation performance as the hydrogel electrode in motor threshold and stimulation comfort. However, the dry textile electrode was found to have very low pain threshold and induced obvious cutaneous painful sensations during stimulation, in comparison to the wet and hydrogel electrodes. Indeed, the finite element modeling results showed that the activation function along the z direction at the depth of dermis epidermis junction of the dry textile electrode was significantly larger than that of the wet and hydrogel electrodes, thus resulting in stronger activation of pain sensing fibers. Future work will be done to make textile electrodes have similar stimulation performance and comfort as hydrogel electrodes.

Real-time detection of gait events can be applied as a reliable input to control drop foot correction devices and lower-limb prostheses. Among the different sensors used to acquire the signals associated with walking for gait event detection, the accelerometer is considered as a preferable sensor due to its convenience of use, small size, low cost, reliability, and low power consumption. Based on the acceleration signals, different algorithms have been proposed to detect toe off (TO) and heel strike (HS) gait events in previous studies. While these algorithms could achieve a relatively reasonable performance in gait event detection, they suffer from limitations such as poor real-time performance and are less reliable in the cases of up stair and down stair terrains. In this study, a new algorithm is proposed to detect the gait events on three walking terrains in real-time based on the analysis of acceleration jerk signals with a time-frequency method to obtain gait parameters, and then the determination of the peaks of jerk signals using peak heuristics. The performance of the newly proposed algorithm was evaluated with eight healthy subjects when they were walking on level ground, up stairs, and down stairs. Our experimental results showed that the mean F1 scores of the proposed algorithm were above 0.98 for HS event detection and 0.95 for TO event detection on the three terrains. This indicates that the current algorithm would be robust and accurate for gait event detection on different terrains. Findings from the current study suggest that the proposed method may be a preferable option in some applications such as drop foot correction devices and leg prostheses.