In the past, investigators tend to use multi-channel surface electromyography (sEMG) signal acquisition devices to improve the recognition accuracy for the study of gesture recognition systems based on sEMG. The disadvantages of the method are the increased complexity and the problems such as signal crosstalk. This paper explores a gesture recognition method based on a single-channel sEMG envelope signal feature in the time domain. First, we get the sEMG envelope signal by using a preprocessing circuit. Then, we use the improved method of valid activity segment extraction to find every valid activity segment and extract 15 features from every valid activity segment. Next, we calculate the absolute value of the correlation coefficient between each of the features and target values. After removing the feature with the smaller correlation coefficient, we reserve the 14 features. By the PCA dimensionality reduction algorithm, we transform the 14-dimensional feature into 2-dimensional feature space. Finally, we use the improved KNN algorithm and the soft margin SVM algorithm to complete the classification of five types of gestures. We obtain the gesture recognition rates of 75.8 and 79.4% by using the improved KNN algorithm and the soft margin SVM algorithm.

Photoplethysmography imaging (PPGI) is a non-invasive and non-contact technology that can measure and monitor human vital signs such as the heart rate (HR), respiratory rate (RR), heart rate variability (HRV), and blood oxygen saturation (SpO₂) by measuring the minute changes in the skin color due to the heartbeat with a normal camera.¹ PPGI technology has been attracting a lot of attention from researchers because of its several advantages. First, it does not cause any discomfort to the people and also not interfere with their activities. Next, it allows measurement of human cardiac activities with only a smartphone, which requires a low cost to implement. The amount of hemoglobin changes in the microvascular network in the dermis due to the systole and diastole of the heart. At the same time, the absorption of light also changes according to the amount of hemoglobin, and it makes a variation in the intensity of the reflected light. Based on this fact, PPGI technology extracts the pulse signal from the image of the exposed skin. Therefore, PPGI technology requires not only sufficient knowledge of the light reflection mechanism on the skin and the transformation relationship between the color spaces but also the various signal processing techniques.