Leveraging Machine Learning for Gait Phase Classification with Varied Training Methods-Reference-Cited by-同舟云学术

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Leveraging Machine Learning for Gait Phase Classification with Varied Training Methods

Published:2024-07-11 Issue: Volume:1 Page:582-587
ISSN:
Container-title:2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP)
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Author:

Mekni Amal¹,Narayan Jyotindra²,Gritli Hassène³

Affiliation:

1. Higher Institute of Information and Communication Technologies, University of Carthage, RISC Laboratory (LR16ES07), Borj Cedria,Tunis,Tunisia,1164

2. Imperial College,Department of Computing,London,UK,SW7 2AZ

3. University of Tunis El Manar, Le Belvédère,Laboratory of Robotics, Informatics and Complex Systems, (RISC Lab - LR16ES07), National Engineering School of Tunis,Tunis,Tunisia,1002

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10638790/10638839/10638961.pdf?arnumber=10638961

Reference19 articles.

1. Development of Active Lower Limb Robotic-Based Orthosis and Exoskeleton Devices: A Systematic Review

2. A Spatiotemporal Deep Learning Approach for Automatic Pathological Gait Classification

3. Gait Phase Detection in Walking and Stairs Using Machine Learning

4. Gait Phase Recognition Using Deep Convolutional Neural Network with Inertial Measurement Units

5. A comparative performance analysis of backpropagation training optimizers to estimate clinical gait mechanics

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