A Review of the Current Methods and Challenges of Facial EMG Based Gesture Recognition
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Advances in technology related to the Internet-of-Things and wearable health
technology has lead new research in the field of surface EMG based gesture
recognition in different fields of study such as medical rehabilitation, EMG
controlled prosthetic limbs, and human-computer interaction for people with
disabilities. Current research pertaining to EMG based gesture recognition focuses
on a variety of different muscles across the human body and as such, different
gestures as well. This has made it difficult to evaluate studies against each other
because of the different study set ups used across different fields of research and
the inability to combine datasets or compare them due to these differences.
This thesis aims to study a publicly available Facial EMG dataset to accomplish the
following goals. Compare gesture recognition performance between different
feature sets and classifiers by comparing amplitude time-domain based features
that are used in literature to discrete wavelet transform based time-frequency
features and compare the performance of these two feature sets. Highlight the
challenges involved in creating a facial gesture recognition model that is able to
identify gestures with high accuracy. Compare the results presented in this thesis to
related works and highlight how current research may present overly optimistic
results concerning classification accuracy. Present a new alternative lightweight
classification model, which requires no feature engineering, based on a convolution
neural network, which is supplied discrete wavelet transform coefficients derived
from Facial EMG signals as an input.