Cargando…
Sliding-Window Normalization to Improve the Performance of Machine-Learning Models for Real-Time Motion Prediction Using Electromyography
Many researchers have used machine learning models to control artificial hands, walking aids, assistance suits, etc., using the biological signal of electromyography (EMG). The use of such devices requires high classification accuracy. One method for improving the classification performance of machi...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269700/ https://www.ncbi.nlm.nih.gov/pubmed/35808500 http://dx.doi.org/10.3390/s22135005 |
_version_ | 1784744285065707520 |
---|---|
author | Tanaka, Taichi Nambu, Isao Maruyama, Yoshiko Wada, Yasuhiro |
author_facet | Tanaka, Taichi Nambu, Isao Maruyama, Yoshiko Wada, Yasuhiro |
author_sort | Tanaka, Taichi |
collection | PubMed |
description | Many researchers have used machine learning models to control artificial hands, walking aids, assistance suits, etc., using the biological signal of electromyography (EMG). The use of such devices requires high classification accuracy. One method for improving the classification performance of machine learning models is normalization, such as z-score. However, normalization is not used in most EMG-based motion prediction studies because of the need for calibration and fluctuation of reference value for calibration (cannot re-use). Therefore, in this study, we proposed a normalization method that combines sliding-window and z-score normalization that can be implemented in real-time processing without need for calibration. The effectiveness of this normalization method was confirmed by conducting a single-joint movement experiment of the elbow and predicting its rest, flexion, and extension movements from the EMG signal. The proposed method achieved 77.7% accuracy, an improvement of 21.5% compared to the non-normalization (56.2%). Furthermore, when using a model trained by other people’s data for application without calibration, the proposed method achieved 63.1% accuracy, an improvement of 8.8% compared to the z-score (54.4%). These results showed the effectiveness of the simple and easy-to-implement method, and that the classification performance of the machine learning model could be improved. |
format | Online Article Text |
id | pubmed-9269700 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92697002022-07-09 Sliding-Window Normalization to Improve the Performance of Machine-Learning Models for Real-Time Motion Prediction Using Electromyography Tanaka, Taichi Nambu, Isao Maruyama, Yoshiko Wada, Yasuhiro Sensors (Basel) Article Many researchers have used machine learning models to control artificial hands, walking aids, assistance suits, etc., using the biological signal of electromyography (EMG). The use of such devices requires high classification accuracy. One method for improving the classification performance of machine learning models is normalization, such as z-score. However, normalization is not used in most EMG-based motion prediction studies because of the need for calibration and fluctuation of reference value for calibration (cannot re-use). Therefore, in this study, we proposed a normalization method that combines sliding-window and z-score normalization that can be implemented in real-time processing without need for calibration. The effectiveness of this normalization method was confirmed by conducting a single-joint movement experiment of the elbow and predicting its rest, flexion, and extension movements from the EMG signal. The proposed method achieved 77.7% accuracy, an improvement of 21.5% compared to the non-normalization (56.2%). Furthermore, when using a model trained by other people’s data for application without calibration, the proposed method achieved 63.1% accuracy, an improvement of 8.8% compared to the z-score (54.4%). These results showed the effectiveness of the simple and easy-to-implement method, and that the classification performance of the machine learning model could be improved. MDPI 2022-07-02 /pmc/articles/PMC9269700/ /pubmed/35808500 http://dx.doi.org/10.3390/s22135005 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Tanaka, Taichi Nambu, Isao Maruyama, Yoshiko Wada, Yasuhiro Sliding-Window Normalization to Improve the Performance of Machine-Learning Models for Real-Time Motion Prediction Using Electromyography |
title | Sliding-Window Normalization to Improve the Performance of Machine-Learning Models for Real-Time Motion Prediction Using Electromyography |
title_full | Sliding-Window Normalization to Improve the Performance of Machine-Learning Models for Real-Time Motion Prediction Using Electromyography |
title_fullStr | Sliding-Window Normalization to Improve the Performance of Machine-Learning Models for Real-Time Motion Prediction Using Electromyography |
title_full_unstemmed | Sliding-Window Normalization to Improve the Performance of Machine-Learning Models for Real-Time Motion Prediction Using Electromyography |
title_short | Sliding-Window Normalization to Improve the Performance of Machine-Learning Models for Real-Time Motion Prediction Using Electromyography |
title_sort | sliding-window normalization to improve the performance of machine-learning models for real-time motion prediction using electromyography |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269700/ https://www.ncbi.nlm.nih.gov/pubmed/35808500 http://dx.doi.org/10.3390/s22135005 |
work_keys_str_mv | AT tanakataichi slidingwindownormalizationtoimprovetheperformanceofmachinelearningmodelsforrealtimemotionpredictionusingelectromyography AT nambuisao slidingwindownormalizationtoimprovetheperformanceofmachinelearningmodelsforrealtimemotionpredictionusingelectromyography AT maruyamayoshiko slidingwindownormalizationtoimprovetheperformanceofmachinelearningmodelsforrealtimemotionpredictionusingelectromyography AT wadayasuhiro slidingwindownormalizationtoimprovetheperformanceofmachinelearningmodelsforrealtimemotionpredictionusingelectromyography |