The latest research progress of Professor Mohamad Sawan’s team: Approaches and Beyond of Transfer Learning on Electromyography (EMG) Tasks

Titled “Transfer Learning on Electromyography (EMG) Tasks: Approaches and Beyond”, this review paper has been published in the IEEE Transactions on Neural Systems and Rehabilitation Engineering. In this publication, we introduced the muscles’ physiological structure, the EMG generating mechanism, and the recording of EMG to provide biological insights behind existing transfer learning approaches.

Congratulations to Di Wu and to this paper’s co-authors for this excellent achievement.

Reference

D. Wu, J. Yang and M. Sawan, "Transfer Learning on Electromyography (EMG) Tasks: Approaches and Beyond," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 3015-3034, 2023, doi: 10.1109/TNSRE.2023.3295453.

Abstract

Machine learning on electromyography (EMG) has recently achieved remarkable success on various tasks, while such success relies heavily on the assumption that the training and future data must be of the same data distribution. However, this assumption may not hold in many real-world applications. Model calibration is required via data re-collection and label annotation, which is generally very expensive and time-consuming. To address this issue, transfer learning (TL), which aims to improve target learners’ performance by transferring knowledge from related source domains, is emerging as a new paradigm to reduce the amount of calibration effort. This survey assesses the eligibility of more than fifty published peer-reviewed representative transfer learning approaches for EMG applications. Unlike previous surveys on purely transfer learning or EMG-based machine learning, this survey aims to provide insight into the biological foundations of existing transfer learning methods on EMG-related analysis. Specifically, we first introduce the muscles’ physiological structure, the EMG generating mechanism, and the recording of EMG to provide biological insights behind existing transfer learning approaches. Further, we categorize existing research endeavors into data based, model based, training scheme based, and adversarial based. This survey systematically summarizes and categorizes existing transfer learning approaches for EMG related machine learning applications. In addition, we discuss possible drawbacks of existing works and point out the future direction of better EMG transfer learning algorithms to enhance practicality for real-world applications.

Fig. 1: Illustration of electrode variation. The left-hand side shows an EMG acquisition armband put on the forearm of a subject. (a), (b) and (c) are the placement of the armband and the corresponding skin underneath. Colored circles represent electrodes, with two vertically placed electrodes being one bipolar channel. (a) demonstrates the original placement of an eight-channel bi-polar EMG collecting armband on the surface of the skin. (b) shows a shifted placement of the electrodes on the skin compared to (a). (c) is the case where electrode placement is the same as (a), but some channels are missing due to any reason.

Fig. 2: Overview of categorization of transfer learning on EMG analysis

More information can be found at the following link:

https://ieeexplore.ieee.org/abstract/document/10184170?signout=success