Abstract
We propose a series of algorithms for air-typing recognition from data collected by a finger- wearable motion-sensing ring. This finger-worn unit consists of a miniature inertial measurement unit (IMU) and a microcontroller unit (MCU) with an on-chip Bluetooth Low Energy (BLE) transceiver. Our proposed algorithms perform data segmentation, feature extraction, and classification based on k-Nearest Neighbors (kNN) to recognize the gestures and map them into the imaginary keyboard. Experimental results show that our air-typing system can achieve 90.8% and 90.2% on user-dependent and user-independent cases, respectively.