Recognition of Hand Gestures Using a Smart Glove with Inductive Sensors
Shokoufeh Davarzani, Maryam Ravan, Reza Khalaj Amineh · 2025
This study presents an improved gesture recognition system for American Sign Language (ASL) hand gestures, utilizing inductive sensors. The system integrates conductive threads to sew eight coils onto a standard glove, covering the 5 fingers, wrist, palm, and ulnar areas. Testing was conducted with 10 participants, and three machine learning algorithms (MLAs)—Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN)—were evaluated for recognizing all 26 ASL letters. To increase the diversity and generalizability of the MLAs, a Generative Adversarial Network (GAN) was employed for data augmentation, generating 5050 trials per gesture. The system achieved a high accuracy of 97.46% using leave-one-subject-out cross-validation (LOSO-CV) with the RF algorithm, demonstrating effectively addresses the shortcomings of previous hand gesture recognition systems, which relied on five coils sewn onto five fingers. This proposed approach, offering a robust and practical solution for human-computer interaction.