Comfort Assessment Method of EEG-Based Exoskeleton Walking-Assistive Device

Heyuan Wang, Kaitai Li, Hui Liu, Xuesong Ye, Congcong Zhou

2024

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.

Paper Citation

in Harvard Style

Wang H., Li K., Liu H., Ye X. and Zhou C. (2024). Comfort Assessment Method of EEG-Based Exoskeleton Walking-Assistive Device. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS; ISBN 978-989-758-688-0, SciTePress, pages 675-682. DOI: 10.5220/0012564800003657

in Bibtex Style

@conference{biosignals24,
author={Heyuan Wang and Kaitai Li and Hui Liu and Xuesong Ye and Congcong Zhou},
title={Comfort Assessment Method of EEG-Based Exoskeleton Walking-Assistive Device},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS},
year={2024},
pages={675-682},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012564800003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS
TI - Comfort Assessment Method of EEG-Based Exoskeleton Walking-Assistive Device
SN - 978-989-758-688-0
AU - Wang H.
AU - Li K.
AU - Liu H.
AU - Ye X.
AU - Zhou C.
PY - 2024
SP - 675
EP - 682
DO - 10.5220/0012564800003657
PB - SciTePress