GloveSense: A Hand Gesture Recognition System Based on Inductive Sensing

Gesture recognition, in particular, hand gesture recognition, has received great attention in the field of human–computer interaction (HCI). Although several hand gesture recognition designs have been proposed in the past, they still suffer many limitations in practice. Hence, we present a wearable device, called GloveSense, which is made on a regular glove using conductive threads. The system consists of ten coils, with each pair of coils sewn on both sides of each finger and connected in series. To achieve high sensitivity, a data acquisition system is employed, in which a tank circuit made of each sewn coil is measured. Since the utilized commercial data acquisition board has limited channels, a switching mechanism is employed to measure some of the extra coils. The utilized switching mechanism reduces the crosstalk effect between the readings. The GloveSense system is tested for the recognition of numbers 1–10 in American Sign Language (ASL). A dataset of 1400 responses is employed along with a machine learning algorithm (MLA) for gesture recognition. The proposed system demonstrates a high accuracy of 99% and a short recognition time of less than 1 s.