Real-time Gait Pattern Classification Using Artificial Neural Networks-Reference-Cited by-同舟云学术

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Real-time Gait Pattern Classification Using Artificial Neural Networks

Published:2022-05-25 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Workshop on Metrology for Living Environment (MetroLivEn)
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Author:

Robles Diego¹,Benchekroun Mouna²,Lira Andrea³,Taramasco Carla¹,Zalc Vincent²,Irazzoky Igor⁴,Istrate Dan²

Affiliation:

1. Universidad de Valparaíso,Escuela de Ingeniería Civil Informática,Valparaíso,Chile

2. Université de Technologie de Compiègne,Biomechanics and Bioengineering Lab,Compiègne,France

3. Universidad Andrés Bello,Departamento de Morfología,Santiago,Chile

4. Universidad Diego Portales,Escuela de Kinesiología,Santiago,Chile

Funder

Universidad de Valparaíso

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9826870/9826904/09826927.pdf?arnumber=9826927

Reference35 articles.

1. The genetic determinants of cerebral palsy;mcmichael,2016

2. A Wearable Gait Phase Detection System Based on Force Myography Techniques

3. Continuous Hidden Markov Model for Pedestrian Activity Classification and Gait Analysis

4. Classification of Normal and Pathological Gait in Young Children Based on Foot Pressure Data

5. Evidence-based physical diagnosis e-book;mcgee,2021

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Real-Time Gait Anomaly Detection Using 1D-CNN and LSTM;Communications in Computer and Information Science;2024

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