Long-term stable Electromyography classification using Canonical
Correlation Analysis
- URL: http://arxiv.org/abs/2301.09729v1
- Date: Mon, 23 Jan 2023 21:45:00 GMT
- Title: Long-term stable Electromyography classification using Canonical
Correlation Analysis
- Authors: Elisa Donati, Simone Benatti, Enea Ceolini, and Giacomo Indiveri
- Abstract summary: Discrimination of hand gestures based on surface electromyography (sEMG) signals is a well-establish approach for controlling prosthetic devices.
One of the most critical challenges is maintaining high EMG data classification performance across multiple days without retraining the decoding system.
Here we propose a novel statistical method that stabilizes EMG classification performance across multiple days for long-term control of prosthetic devices.
- Score: 5.949779668853555
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Discrimination of hand gestures based on the decoding of surface
electromyography (sEMG) signals is a well-establish approach for controlling
prosthetic devices and for Human-Machine Interfaces (HMI). However, despite the
promising results achieved by this approach in well-controlled experimental
conditions, its deployment in long-term real-world application scenarios is
still hindered by several challenges. One of the most critical challenges is
maintaining high EMG data classification performance across multiple days
without retraining the decoding system. The drop in performance is mostly due
to the high EMG variability caused by electrodes shift, muscle artifacts,
fatigue, user adaptation, or skin-electrode interfacing issues. Here we propose
a novel statistical method based on canonical correlation analysis (CCA) that
stabilizes EMG classification performance across multiple days for long-term
control of prosthetic devices. We show how CCA can dramatically decrease the
performance drop of standard classifiers observed across days, by maximizing
the correlation among multiple-day acquisition data sets. Our results show how
the performance of a classifier trained on EMG data acquired only of the first
day of the experiment maintains 90% relative accuracy across multiple days,
compensating for the EMG data variability that occurs over long-term periods,
using the CCA transformation on data obtained from a small number of gestures.
This approach eliminates the need for large data sets and multiple or periodic
training sessions, which currently hamper the usability of conventional pattern
recognition based approaches
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