Abstract:
Airwriting enables users to write letters or characters in free space using hand or finger
movements, with potential applications in human-computer interaction, virtual reality,
augmented reality and development of assistive technologies. Despite advancements in
gesture recognition technology, dynamic airwriting faces challenges with accuracy and
often lacks real-time capabilities, limiting its application in non-verbal communication
and rehabilitation devices. The primary objective of this research is to develop a novel
real-time deep learning-based framework for airwriting recognition using surface
electromyography (sEMG). This study presents a technique for real-time identification of
uppercase English language alphabets written in free space by analyzing the electrical
activity of forearm muscles involved in writing letters. The proposed framework involves
sEMG data collection from 16 right handed healthy subjects with no neuromuscular or
motor impairments, signal preprocessing, feature extraction, classification using
Convolution neural network (CNN), Deep neural network (DNN) and Recurrent Neural
Network (RNN).The best performing model was implemented in real-time and it was
evaluated using performance metrics such as accuracy, precision, recall, F1 score,
Confusion metrics and latency. Results show that 1 Dimensional (1D) CNN outperforms
other models (p<0.05) and achieved an offline test accuracy of 89.81 ±0.87% and an
average real-time test accuracy of 73.71 ±8.46% across subjects. The individual model of
each subject performed even better, with an accuracy of 90.01 ±2.85% on offline testing
of data and 75.45 ±1.53 % in real-time alphabet prediction. Thus, this work highlights the
potential of deep learning models for real-time airwriting detection and provides
foundations for sEMG-based airwriting applications in healthcare and telemedicine.
