The Impact of Stimulation Intensity on Spatial Discrimination with Multi-Pad Finger Electrode

Multi-pad electrotactile stimulation can be used to provide tactile feedback in different applications. The electrotactile interface needs to be calibrated before each use, which entails adjusting the intensity to obtain clear sensations while allowing the subjects to differentiate between active pads. The present study investigated how the stimulation intensity affects the localization of sensations using a multi-pad electrode placed on a fingertip and proximal phalange. First, the sensation, localization, smearing and discomfort thresholds were determined in 11 subjects. Then, the same subjects performed a spatial discrimination test across a range of stimulation intensities. The results have shown that all thresholds were significantly different, while there was no difference in the threshold values between the pads and phalanges. Despite the subjective feeling of spreading of sensations, the success rates in spatial discrimination were not significantly different across the tested stimulation intensities. However, the performance was better for distal compared to proximal phalange. Presented results indicate that spatial discrimination is robust to changes in the stimulation intensity. Considering the lack of significant difference in the thresholds between the pads, these results imply that more coarse adjustment of stimulation amplitude (faster calibration) might be enough for practical applications of a multi-pad electrotactile interface.

Development of a Low-Cost Wearable Data Glove for Capturing Finger Joint Angles

Capturing finger joint angle information has important applications in human–computer interaction and hand function evaluation. In this paper, a novel wearable data glove is proposed for capturing finger joint angles. A sensing unit based on a grating strip and an optical detector is specially designed for finger joint angle measurement. To measure the angles of finger joints, 14 sensing units are arranged on the back of the glove. There is a sensing unit on the back of each of the middle phalange, proximal phalange, and metacarpal of each finger, except for the thumb. For the thumb, two sensing units are distributed on the back of the proximal phalange and metacarpal, respectively. Sensing unit response tests and calibration experiments are conducted to evaluate the feasibility of using the designed sensing unit for finger joint measurement. Experimental results of calibration show that the comprehensive precision of measuring the joint angle of a wooden finger model is 1.67%. Grasping tests and static digital gesture recognition experiments are conducted to evaluate the performance of the designed glove. We achieve a recognition accuracy of 99% by using the designed glove and a generalized regression neural network (GRNN). These preliminary experimental results indicate that the designed data glove is effective in capturing finger joint angles.