Temporal-constrained parallel graph neural networks for recognizing motion patterns and gait phases in class-imbalanced scenarios
Authors:
- 20 January 2025
- journal article
- research article
- Published by Elsevier in Engineering Applications of Artificial Intelligence
- Vol. 143
Abstract
No abstract availableKeywords
Funding Information
- Hubei Province Natural Science Foundation
This publication has 36 references indexed in Scilit:
- Exploration of deep learning-driven multimodal information fusion frameworks and their application in lower limb motion recognitionBiomedical Signal Processing and Control, 2024
- A portable SSVEP-BCI system for rehabilitation exoskeleton in augmented reality environmentBiomedical Signal Processing and Control, 2023
- A low-cost framework for the recognition of human motion gait phases and patterns based on multi-source perception fusionEngineering Applications of Artificial Intelligence, 2023
- A fuzzy convolutional attention-based GRU network for human activity recognitionEngineering Applications of Artificial Intelligence, 2022
- Gait phase recognition of lower limb exoskeleton system based on the integrated network modelBiomedical Signal Processing and Control, 2022
- Simultaneous exercise recognition and evaluation in prescribed routines: Approach to virtual coachesExpert Systems with Applications, 2022
- Multi-modal bioelectrical signal fusion analysis based on different acquisition devices and scene settings: Overview, challenges, and novel orientationInformation Fusion, 2022
- Design of upper limb prosthesis using real-time motion detection method based on EMG signal processingBiomedical Signal Processing and Control, 2021
- A comparison of neural networks algorithms for EEG and sEMG features based gait phases recognitionBiomedical Signal Processing and Control, 2021
- Gait phase detection and discrimination between walking–jogging activities using hidden Markov models applied to foot motion data from a gyroscopeGait & Posture, 2012