Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning

In recent years, deep learning algorithms have become increasingly more prominent for their unparalleled ability to automatically learn discriminant features from large amounts of data. However, within the field of electromyographybased gesture recognition, deep learning algorithms are seldom employ

Project Title

Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning

Project Area of Specialization

Biomedical Engineering

Project Summary

In recent years, deep learning algorithms have become increasingly more prominent for their unparalleled ability to automatically learn discriminant features from large amounts of data. However, within the field of electromyographybased gesture recognition, deep learning algorithms are seldom employed as they require an unreasonable amount of effort from a single person, to generate tens of thousands of examples. This work’s hypothesis is that general, informative features can be learned from the large amounts of data generated by aggregating the signals of multiple users, thus reducing the recording burden while enhancing gesture recognition. Consequently, this paper proposes applying transfer learning on aggregated data from multiple users, while leveraging the capacity of deep learning algorithms to learn discriminant features from large datasets. Two datasets comprised of 19 and 17 able-bodied participants respectively (the first one is employed for pre-training) were recorded for this work, using the Myo Armband. A third Myo Armband dataset was taken from the NinaPro database and is comprised of 10 able-bodied participants. Three different deep learning networks employing three different modalities as input (raw EMG, Spectrograms and Continuous Wavelet Transform (CWT)) are tested on the second and third dataset. The proposed transfer learning scheme is shown to systematically and significantly enhance the performance for all three networks on the two datasets, achieving an offline accuracy of 98.31% for 7 gestures over 17 participants for the CWT-based ConvNet and 68.98% for 18 gestures over 10 participants for the raw EMG-based ConvNet. Finally, a use-case study employing eight able-bodied participants suggests that real-time feedback allows users to adapt their muscle activation strategy which reduces the degradation in accuracy normally experienced over time.

Project Objectives

The main contribution of this work is to present a new TL scheme employing a convolutional network (ConvNet) to leverage inter-user data within the context of sEMGbased gesture recognition.

Project Implementation Method

One of the major contributions of this article is to provide a new, publicly available, sEMG-based hand gesture recognition dataset, referred to as the Myo Dataset. This dataset contains two distinct sub-datasets with the first one serving as the pretraining dataset and the second as the evaluation dataset. The former, which is comprised of 19 able-bodied participants, should be employed to build, validate and optimize classification techniques. The latter, comprised of 17 able-bodied participants, is utilized only for the final testing. To the best of our knowledge, this is the largest dataset published utilizing the commercially available Myo Armband (Thalmic Labs) and it is our hope that it will become a useful tool for the sEMGbased hand gesture classification community.

Benefits of the Project

As expected, reducing the amount of training cycles systematically degraded the performances of all tested methods , with the non-TL ConvNets being the most affected on the Myo Dataset.

Technical Details of Final Deliverable

, the proposed classifier achieved an average accuracy of 68.98% over 10 participants on a single Myo Armband. This dataset showed that the proposed TL algorithm learns sufficiently general features to significantly enhance the performance of ConvNets on out-ofsample gestures. Showing that deep learning algorithms can be efficiently trained, within the inherent constraints of sEMGbased hand gesture recognition, offers exciting new research avenues for this field.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Health

Other Industries

Core Technology

Wearables and Implantables

Other Technologies

Sustainable Development Goals

Good Health and Well-Being for People

Required Resources

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