On the Metrics and Adaptation Methods for Domain Divergences of sEMG-based Gesture Recognition
This work addresses domain adaptation challenges in time-series classification for biomedical signal processing, but it appears incremental as it builds on existing probability distribution-based metrics and RNN architectures.
The authors tackled the problem of domain divergence in sEMG-based gesture recognition by proposing a new metric and a domain adaptation method, achieving competitive performance with state-of-the-art models on inter-session and inter-subject shifts.
We propose a new metric to measure domain divergence and a new domain adaptation method for time-series classification. The metric belongs to the class of probability distributions-based metrics, is transductive, and does not assume the presence of source data samples. The 2-stage method utilizes an improved autoregressive, RNN-based architecture with deep/non-linear transformation. We assess our metric and the performance of our model in the context of sEMG/EMG-based gesture recognition under inter-session and inter-subject domain shifts.