Authors:
Heyuan Wang
1
;
Kaitai Li
1
;
Hui Liu
2
;
Xuesong Ye
1
and
Congcong Zhou
3
Affiliations:
1
College of Biomedical Engineering & Instrument Science, Biosensor National Special Laboratory, Zhejiang University, Hangzhou, Zhejiang, 310027, China
;
2
Cognitive Systems Lab, University of Bremen, 28359 Bremen, Germany
;
3
Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, National Engineering Research Center for Innovation and Application of Minimally Invasive Devices, East Qingchun Road, Hangzhou, Zhejiang, 310016, China
Keyword(s):
Biosignal Processing, Comfort Assessment, EEG, Deep Learning, Exoskeleton Walking-Assistive.
Abstract:
The study of wearable exoskeleton robotics has garnered significant attention, amidst a rapidly expanding
corpus of scholarly work aimed at the empirical evaluation of the performance characteristics of robotic
exoskeletons. However, quantifying comfort performance is still a significant and challenging task. This study
aimed to perform comfort assessment based on EEG (Electroencephalography) signals and classical machine
learning models as well as deep learning model. It involved collecting EEG data from users wearing lower
limb exoskeleton walking-assistive devices for comfort assessment during walking experiments. The
subjective evaluation labels of comfort were obtained using a semantic differential scale, providing comfort
labels data for each participant in each trial. This study conducted a comparative analysis of three classical
ML (Machine Learning) models, Naive Bayes, K-Nearest Neighbors, and Support Vector Machine models,
with DL (Deep Learning) model, LSTM (Long
Short-Term Memory), in terms of their accuracy for comfort
assessment. The results of the analysis showed that the deep learning model, LSTM, outperformed the
classical machine learning models, in terms of accuracy for evaluating comfort. Specifically, we get an
accuracy of 0.91±0.12 on the LSTM model. The LSTM model demonstrated higher accuracy and better
performance in capturing complex patterns and relationships within the EEG data, leading to the potential of
more accurate predictions of comfort levels.
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