  Machine learning on electromyography (EMG) has recently achieved remarkable
success on a variety of 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 problem, transfer learning (TL), which aims to improve target learners'
performance by transferring the knowledge from related source domains, is
emerging as a new paradigm to reduce the amount of calibration effort. In this
survey, we assess 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 an insight into the biological foundations of
existing transfer learning methods on EMG-related analysis. In specific, we
first introduce the physiological structure of the muscles and 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.
