Deep Learning Models for Stable Gait Prediction Applied to Exoskeleton Reference Trajectories for Children With Cerebral Palsy
Author:
Affiliation:
1. School of Engineering, University of Kent, Canterbury, U.K
2. School of Engineering, Technology and Design, Canterbury Christ Church University, Canterbury, U.K
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10058948.pdf?arnumber=10058948
Reference46 articles.
1. A robot-based gait training therapy for pediatric population with cerebral palsy: goal setting, proposal and preliminary clinical implementation
2. Intrasubject repeatability of gait analysis data in normal and spastic children
3. Designing Exoskeletons for Children
4. Effects of Individualized Gait Rehabilitation Robotics for Gait Training on Hemiplegic Patients: Before-After Study in the Same Person
5. WAKE-Up Exoskeleton to Assist Children With Cerebral Palsy: Design and Preliminary Evaluation in Level Walking
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1. 3D gait analysis in children using wearable sensors: feasibility of predicting joint kinematics and kinetics with personalized machine learning models and inertial measurement units;Frontiers in Bioengineering and Biotechnology;2024-03-20
2. 3D Gait Analysis in Children Using Wearable Sensors: Feasibility of Predicting Joint Kinematics and Kinetics with Personalized Machine Learning Models and Inertial Measurement Units;2024-01-29
3. Abnormal Gait Classification in Children With Cerebral Palsy Using ConvLSTM Hybrid Model and GAN;IEEE Access;2024
4. TempoNet: Empowering Long-Term Knee Joint Angle Prediction with Dynamic Temporal Attention in Exoskeleton Control;2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids);2023-12-12
5. The Learning Trajectory Based on STEM of Elementary School Pupils’ in Solving Proportion Material: Didactical Design-Research;Participatory Educational Research;2023-11-01
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