Unsupervised cross-user adaptation in taste sensation recognition based on surface electromyography with conformal prediction and domain regularized component analysis

TL;DR

This study enhances taste sensation recognition from surface electromyography by applying domain regularized component analysis and conformal prediction, improving cross-user model generalization and addressing data distribution differences.

Contribution

It introduces a novel combination of DRCA and CPSC methods for cross-user adaptation in taste recognition using sEMG data, demonstrating improved accuracy.

Findings

DRCA significantly improved classification accuracy across six subjects.

CPSC alone did not guarantee accuracy improvement.

Combining DRCA and CPSC yielded statistically significant accuracy gains.

Abstract

Human taste sensation can be qualitatively described with surface electromyography. However, the pattern recognition models trained on one subject (the source domain) do not generalize well on other subjects (the target domain). To improve the generalizability and transferability of taste sensation models developed with sEMG data, two methods were innovatively applied in this study: domain regularized component analysis (DRCA) and conformal prediction with shrunken centroids (CPSC). The effectiveness of these two methods was investigated independently in an unlabeled data augmentation process with the unlabeled data from the target domain, and the same cross-user adaptation pipeline were conducted on six subjects. The results show that DRCA improved the classification accuracy on six subjects (p < 0.05), compared with the baseline models trained only with the source domain data;, while CPSC did not guarantee the accuracy improvement. Furthermore, the combination of DRCA and CPSC presented statistically significant improvement (p < 0.05) in classification accuracy on six subjects. The proposed strategy combining DRCA and CPSC showed its effectiveness in addressing the cross-user data distribution drift in sEMG-based taste sensation recognition application. It also shows the potential in more cross-user adaptation applications.