  Accurate decoding of surface electromyography (sEMG) is pivotal for
muscle-to-machine-interfaces (MMI) and their application for e.g.
rehabilitation therapy. sEMG signals have high inter-subject variability, due
to various factors, including skin thickness, body fat percentage, and
electrode placement. Therefore, obtaining high generalization quality of a
trained sEMG decoder is quite challenging. Usually, machine learning based sEMG
decoders are either trained on subject-specific data, or at least recalibrated
for each user, individually. Even though, deep learning algorithms produced
several state of the art results for sEMG decoding,however, due to the limited
amount of availability of sEMG data, the deep learning models are prone to
overfitting. Recently, transfer learning for domain adaptation improved
generalization quality with reduced training time on various machine learning
tasks. In this study, we investigate the effectiveness of transfer learning
using weight initialization for recalibration of two different pretrained deep
learning models on a new subjects data, and compare their performance to
subject-specific models. To the best of our knowledge, this is the first study
that thoroughly investigated weight-initialization based transfer learning for
sEMG classification and compared transfer learning to subject-specific
modeling. We tested our models on three publicly available databases under
various settings. On average over all settings, our transfer learning approach
improves 5~\%-points on the pretrained models without fine-tuning and
12~\%-points on the subject-specific models, while being trained on average
22~\% fewer epochs. Our results indicate that transfer learning enables faster
training on fewer samples than user-specific models, and improves the
performance of pretrained models as long as enough data is available.
