A Deep Learning Approach to Prevent Problematic Movements of Industrial Workers Based on Inertial Sensors-Reference-Cited by-同舟云学术

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A Deep Learning Approach to Prevent Problematic Movements of Industrial Workers Based on Inertial Sensors

Published:2022-07-18 Issue: Volume: Page:
ISSN:
Container-title:2022 International Joint Conference on Neural Networks (IJCNN)
language:
Short-container-title:

Author:

Fernandes Cristiana¹,Matos Luis Miguel²,Folgado Duarte³,Nunes Maria Lua³,Pereira Joao Rui⁴,Pilastri Andre¹,Cortez Paulo²

Affiliation:

1. CCG ZGDV Institute,EPMQ - IT Engineering Maturity and Quality Lab,Guimarães,Portugal

2. University of Minho,ALGORITMI Center,Dep. Information Systems,Guimarães,Portugal

3. Fraunhofer Portugal Research Association Fraunhofer,Caparica,Portugal

4. Chief Financial Officer PAFIL - Clothing Industry,Vila Nova de Famalicão,Portugal

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9891857/9889787/09892409.pdf?arnumber=9892409

Reference27 articles.

1. LSTM-based Network for Human Gait Stability Prediction in an Intelligent Robotic Rollator

2. Automated Real-time Anomaly Detection in Human Trajectories using Sequence to Sequence Networks

3. Investigation of Orientation Estimation of Multiple IMUs

4. Explaining the Ergonomic Assessment of Human Movement in Industrial Contexts

Cited by 4 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. RTSIMU: Real-Time Simulation tool for IMU sensors;Software Impacts;2023-09

2. Human Activity Prediction Based on Forecasted IMU Activity Signals by Sequence-to-Sequence Deep Neural Networks;Sensors;2023-07-18

3. Powered Smart Textile-Based Exoskeleton for Human Support Movement;Proceedings of the 9th International Ergonomics Conference;2023

4. A Sequence to Sequence Long Short-Term Memory Network for Footwear Sales Forecasting;Intelligent Data Engineering and Automated Learning – IDEAL 2022;2022

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