SurfMyoAiR: A surface Electromyography based framework for Airwriting Recognition

TL;DR

This paper presents SurfMyoAiR, a deep learning framework using EMG signals for airwriting recognition, achieving up to 78.50% accuracy, and explores various features, representations, and parameters to improve performance.

Contribution

The work introduces a novel EMG-based airwriting recognition framework with a new dataset and evaluates multiple features, representations, and deep learning models for improved accuracy.

Findings

Best accuracy of 78.50% user-dependent

62.19% accuracy in user-independent scenario

Short-Time Fourier Transform with CNN yields optimal results

Abstract

Airwriting Recognition is the task of identifying letters written in free space with finger movement. Electromyography (EMG) is a technique used to record electrical activity during muscle contraction and relaxation as a result of movement and is widely used for gesture recognition. Most of the current research in gesture recognition is focused on identifying static gestures. However, dynamic gestures are natural and user-friendly for being used as alternate input methods in Human-Computer Interaction applications. Airwriting recognition using EMG signals recorded from forearm muscles is therefore a viable solution. Since the user does not need to learn any new gestures and a large range of words can be formed by concatenating these letters, it is generalizable to a wider population. There has been limited work in recognition of airwriting using EMG signals and forms the core idea of the current work. The SurfMyoAiR dataset comprising of EMG signals recorded during writing English uppercase alphabets is constructed. Several different time-domain features to construct EMG envelope and two different time-frequency image representations: Short-Time Fourier Transform and Continuous Wavelet Transform were explored to form the input to a deep learning model for airwriting recognition. Several different deep learning architectures were exploited for this task. Additionally, the effect of various parameters such as signal length, window length and interpolation techniques on the recognition performance is comprehensively explored. The best-achieved accuracy was 78.50% and 62.19% in user-dependent and independent scenarios respectively by using Short-Time Fourier Transform in conjunction with a 2D Convolutional Neural Network based classifier. Airwriting has great potential as a user-friendly modality to be used as an alternate input method in Human-Computer Interaction applications.