Conference Paper : EMG subspace alignment and visualization for cross-subject hand gesture classification
I presented this work at the workshop Adapting to Change: Reliable Multimodal Learning Across Domains during ECML-PKDD 2023. As the basis of my PhD thesis, this paper highlights the cross-subject issue in hand gesture recognition from EMG signals of the forearm. Due to electrode placement shifts, fatigue, and sweat, the class-conditional distributions of the samples change across users. This causes a drop in accuracy in cross-subject configurations, where a classifier is tested on a subject unseen during training. We show how to reduce the accuracy gap using Subspace Alignment , a simple unsupervised domain adaptation method. Read More Here : Colot, M. , Simar, C., Petieau, M., Cebolla Alvarez, A. M., Cheron, G., & Bontempi, G. EMG subspace alignment and visualization for cross-subject hand gesture classification . In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 416–423). Cham: Springer Na...