  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.
