Wrist-Worn Gesture Sensing With Wearable Intelligence

This paper presents an innovative wrist-worn device with machine learning capabilities and a wearable pressure sensor array. The device is used for monitoring different hand gestures by tracking tendon movements around the wrist. Thus, an array of PDMS-encapsulated capacitive pressure sensors is attached to the user to capture wrist movement. The sensors are embedded on a flexible substrate and their readout requires a reliable approach for measuring small changes in capacitance. This challenge was addressed by measuring the capacitance via the switched capacitor method. The values were processed using a programme on LabVIEW to visually reconstruct the gestures on a computer. In addition, to overcome limitations of tendo's uncertainty when the wristband is re-worn, or the user is changed, a calibration step based on the support vector machine (SVM) learning technique is implemented. Sequential minimal optimization algorithm is also applied in the system to generate SVM classifiers efficiently in real-time. The working principle and the performance of the SVM algorithms demonstrate through experiments. Three discriminated gestures have been clearly separated by SVM hyperplane and correctly classified with high accuracy (>90%) during real-time gesture recognition.