ArticleSensors (Basel, Switzerland)2022
Sliding-Window Normalization to Improve the Performance of Machine-Learning Models for Real-Time Motion Prediction Using Electromyography.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Real-time continuous assessment of fatigue from surface electromyography with deep learning for training load regulation in elite cyclists.BMC sports science, medicine & rehabilitation · 2026Article
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- Standardized synchronization and validation pipeline for physiological biomarkers across multiple devices.Behavior research methods · 2026Article
- Real-Time Prediction Method of Shale Gas Fracturing Construction Pressure Based on MS-1DCNN-TFT Neural Network.ACS omega · 2026Article
- Evaluation of Different Controllers for Sensing-Based Movement Intention Estimation and Safe Tracking in a Simulated LSTM Network-Based Elbow Exoskeleton Robot.Sensors (Basel, Switzerland) · 2026Article
- Mitigating the Impact of Electrode Shift on Classification Performance in Electromyography Applications Using Sliding-Window Normalization.Sensors (Basel, Switzerland) · 2025Article
- Deterministic Models for Performance Analysis of Lignocellulosic Biomass Torrefaction.ACS omega · 2025Article
- Label-Free Prediction of Fluorescently Labeled Fibrin Networks.Biomaterials research · 2025Article
- A Multi-Scale CNN for Transfer Learning in sEMG-Based Hand Gesture Recognition for Prosthetic Devices.Sensors (Basel, Switzerland) · 2024Article
- Estimation of Lower Limb Joint Angles Using sEMG Signals and RGB-D Camera.Bioengineering (Basel, Switzerland) · 2024Article
- Hyperspectral and Fluorescence Imaging Approaches for Nondestructive Detection of Rice Chlorophyll.Plants (Basel, Switzerland) · 2024Article
- Article
- Development of a robust induced pluripotent stem cell atrial cardiomyocyte differentiation protocol to model atrial arrhythmia.Stem cell research & therapy · 2023Article
- The use of nonnormalized surface EMG and feature inputs for LSTM-based powered ankle prosthesis control algorithm development.Frontiers in neuroscience · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.