Lightmyography Based Decoding of Human Intention Using Temporal Multi-Channel Transformers

• Main Authors: Godoy, Ricardo V., Dwivedi, Anany, Shahmohammadi, Mojtaba, Liarokapis, Minas
• Format: Conference Proceeding
• Language: English
• Subjects: Adaptation models; Data models; Deep learning; Signal processing; Signal processing algorithms; Training; Transformers
• ISSN: 2153-0866
• DOI: 10.1109/IROS47612.2022.9981514

For the development of muscle-machine interfaces (MuMIs), researchers have relied mainly on Electromyography (EMG) signals. However, these signals require complex hardware systems, as well as specialized signal processing and feature extraction methods. To overcome these issues, in our previous work, we proposed a novel MuMI for decoding human intention and motion, called Lightmyography (LMG). To improve the performance of this interface even further, in this work, we employ two novel deep learning techniques called Temporal Multi-Channel Transformer (TMC-T) and Temporal Multi-Channel Vision Transformer (TMC-ViT) for the classification of hand gestures based on the LMG data. The performance of these two Transformer-based methods is evaluated and compared with other well-known deep learning and classical machine learning methods. This work also addresses the influence of varying parameters defined during the training phase of decoding models, such as the size and shape of the input data packet. A series of data augmentation techniques were also employed to generate synthetic data and increase the dataset size so as to train deep learning models more efficiently.