Motion Detection and Analysis Using Multimaterial Fiber Sensors-Reference-Cited by-同舟云学术

Motion Detection and Analysis Using Multimaterial Fiber Sensors

Published:2023-09 Issue:9 Volume:70 Page:3522-3533
ISSN:1549-8328
Container-title:IEEE Transactions on Circuits and Systems I: Regular Papers
language:
Short-container-title:IEEE Trans. Circuits Syst. I

Author:

Ozon Magali¹^ORCID,Frasie Antoine²^ORCID,Gagnon-Turcotte Gabriel¹^ORCID,Roudjane Mourad³^ORCID,Bouyer Laurent⁴^ORCID,Gagnon Ghyslain⁵^ORCID,Messaddeq Younès³^ORCID,Gosselin Benoit⁶^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Université Laval, Quebec City, Canada

2. Center for Interdisciplinary Research in Rehabilitation and Social Integration (CIRRIS), Université Laval, Quebec City, Canada

3. Physics Department, Center for Optics, Photonics and Laser (COPL), Université Laval, Quebec City, Canada

4. Department of Rehabilitation Engineering, Center for Interdisciplinary Research in Rehabilitation and Social Integration (CIRRIS), Université Laval, Quebec City, Canada

5. Department of Electrical Engineering, École de technologie supérieure, Montréal, Canada

6. Department of Electrical and Computer Engineering, the Center for Interdisciplinary Research in Rehabilitation and Social Integration (CIRRIS), and the Center for Optics, Photonics and Laser (COPL), Physics Department, Université Laval, Quebec City, Canada

Funder

Natural Sciences and Engineering Research Council of Canada

Canada Research Chair in Smart Biomedical Microsystems, and the Microsystems Strategic Alliance of Quebec

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Hardware and Architecture

Link

http://xplorestaging.ieee.org/ielx7/8919/10233917/10186214.pdf?arnumber=10186214

Reference27 articles.

1. FPI-POFBG Angular Movement Sensor Inscribed in CYTOP Fibers With Dynamic Angle Compensator

2. Application of Seven Core Fiber-Based Sensor on Torsion Angle Measurement and Vital Signs Monitoring

3. MyoNet: A Transfer-Learning-Based LRCN for Lower Limb Movement Recognition and Knee Joint Angle Prediction for Remote Monitoring of Rehabilitation Progress From sEMG

4. Wearable Fiber Optic Sensors for Biomechanical Sensing via Joint Angle Detection

5. Comparison of vision-based and sensor-based systems for joint angle gait analysis