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.

EMG Subspace Alignment Visualization
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 Nature Switzerland, September 2023: https://arxiv.org/pdf/2401.05386

Poster picture

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