A two-electrode frequency-scan system for gesture recognition

In this paper, we proposed a novel two-electrode, frequency-scan gesture recognition system based on bioimpedance measurement. This method not only achieves a high accuracy in recognizing common gestures and pinch gestures, but also reduces the measurement complexity and the number of electrodes. We developed a bespoke circuit with two medical electrodes to collect data from the back of the hand and presented a frequency scan method to increase the diversity of impedance data. Feature extraction method was adapted to explore the representative features for gesture recognition, and machine learning classification models with five-fold cross validation were used to train and realize accurate gesture recognition. To verify the effectiveness of this system, we designed two groups of nine gestures in a hand-gesture recognition experiment. The results showed that the system achieved a recognition accuracy of 98.3% with a group of four common gestures and an accuracy of 98.5% with a group of six pinch gestures. The proposed method realized a higher accuracy in pinch gesture set while using fewer electrodes. Additionally, we designed two real-time proof-of-concept interactive scenarios to demonstrate the general applications of this system.