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Unsupervised Domain Adaptation for Inter-Session Re-Calibration of Ultrasound-Based HMIs.

Authors :
Lykourinas, Antonios
Rottenberg, Xavier
Catthoor, Francky
Skodras, Athanassios
Source :
Sensors (14248220). Aug2024, Vol. 24 Issue 15, p5043. 21p.
Publication Year :
2024

Abstract

Human–Machine Interfaces (HMIs) have gained popularity as they allow for an effortless and natural interaction between the user and the machine by processing information gathered from a single or multiple sensing modalities and transcribing user intentions to the desired actions. Their operability depends on frequent periodic re-calibration using newly acquired data due to their adaptation needs in dynamic environments, where test–time data continuously change in unforeseen ways, a cause that significantly contributes to their abandonment and remains unexplored by the Ultrasound-based (US-based) HMI community. In this work, we conduct a thorough investigation of Unsupervised Domain Adaptation (UDA) algorithms for the re-calibration of US-based HMIs during within-day sessions, which utilize unlabeled data for re-calibration. Our experimentation led us to the proposal of a CNN-based architecture for simultaneous wrist rotation angle and finger gesture prediction that achieves comparable performance with the state-of-the-art while featuring 87.92 % less trainable parameters. According to our findings, DANN (a Domain-Adversarial training algorithm), with proper initialization, offers an average 24.99 % classification accuracy performance enhancement when compared to no re-calibration setting. However, our results suggest that in cases where the experimental setup and the UDA configuration may differ, observed enhancements would be rather small or even unnoticeable. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14248220
Volume :
24
Issue :
15
Database :
Academic Search Index
Journal :
Sensors (14248220)
Publication Type :
Academic Journal
Accession number :
178950106
Full Text :
https://doi.org/10.3390/s24155043