東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back to Search results for [ null ]

Switch To: Labeled | MARC Mode | ISBD

Evaluating EEG-EMG Fusion-Based Clas...

Tryon, Jacob G.

Linked to FindBook

Google Book

Amazon

博客來

Evaluating EEG-EMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Evaluating EEG-EMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation./
Author:	Tryon, Jacob G.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	424 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
Subject:	Load. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30737631
ISBN:	9798381029086

Evaluating EEG-EMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation.
Tryon, Jacob G.

Evaluating EEG-EMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 424 p.

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

Thesis (Ph.D.)--The University of Western Ontario (Canada), 2023.

Musculoskeletal disorders are the biggest cause of disability worldwide, and wearable mechatronic rehabilitation devices have been proposed for treatment. However, before widespread adoption, improvements in user control and system adaptability are required. User intention should be detected intuitively, and user-induced changes in system dynamics should be unobtrusively identified and corrected. Developments often focus on model-dependent nonlinear control theory, which is challenging to implement for wearable devices.One alternative is to incorporate bioelectrical signal-based machine learning into the system, allowing for simpler controller designs to be augmented by supplemental brain (electroencephalography/EEG) and muscle (electromyography/EMG) information. To extract user intention better, sensor fusion techniques have been proposed to combine EEG and EMG; however, further development is required to enhance the capabilities of EEG-EMG fusion beyond basic motion classification. To this end, the goals of this thesis were to investigate expanded methods of EEG-EMG fusion and to develop a novel control system based on the incorporation of EEG-EMG fusion classifiers.A dataset of EEG and EMG signals were collected during dynamic elbow flexion-extension motions and used to develop EEG-EMG fusion models to classify task weight, as well as motion intention. A variety of fusion methods were investigated, such as a Weighted Average decisionlevel fusion (83.01 ± 6.04% accuracy) and Convolutional Neural Network-based input-level fusion (81.57 ± 7.11% accuracy), demonstrating that EEG-EMG fusion can classify more indirect tasks.A novel control system, referred to as a Task Weight Selective Controller (TWSC), was implemented using a Gain Scheduling-based approach, dictated by external load estimations from an EEG-EMG fusion classifier. To improve system stability, classifier prediction debouncing was also proposed to reduce misclassifications through filtering. Performance of the TWSC was evaluated using a developed upper-limb brace simulator. Due to simulator limitations, no significant difference in error was observed between the TWSC and PID control. However, results did demonstrate the feasibility of prediction debouncing, showing it provided smoother device motion. Continued development of the TWSC, and EEG-EMG fusion techniques will ultimately result in wearable devices that are able to adapt to changing loads more effectively, serving to improve the user experience during operation.

ISBN: 9798381029086Subjects--Topical Terms:

3562902
Load.

Evaluating EEG-EMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation.
LDR:03645nmm a2200337 4500 001 2399438
005 20240916065441.5
006 m o d
007 cr#unu||||||||
008 251215s2023 ||||||||||||||||| ||eng d
020 $a 9798381029086
035 $a (MiAaPQ)AAI30737631
035 $a (MiAaPQ)oaiirlibuwocaetd12347
035 $a AAI30737631
040 $a MiAaPQ $c MiAaPQ
100 1 $a Tryon, Jacob G. $3 3769406
245 1 0 $a Evaluating EEG-EMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 424 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
500 $a Advisor: Trejos, Ana Luisa.
502 $a Thesis (Ph.D.)--The University of Western Ontario (Canada), 2023.
520 $a Musculoskeletal disorders are the biggest cause of disability worldwide, and wearable mechatronic rehabilitation devices have been proposed for treatment. However, before widespread adoption, improvements in user control and system adaptability are required. User intention should be detected intuitively, and user-induced changes in system dynamics should be unobtrusively identified and corrected. Developments often focus on model-dependent nonlinear control theory, which is challenging to implement for wearable devices.One alternative is to incorporate bioelectrical signal-based machine learning into the system, allowing for simpler controller designs to be augmented by supplemental brain (electroencephalography/EEG) and muscle (electromyography/EMG) information. To extract user intention better, sensor fusion techniques have been proposed to combine EEG and EMG; however, further development is required to enhance the capabilities of EEG-EMG fusion beyond basic motion classification. To this end, the goals of this thesis were to investigate expanded methods of EEG-EMG fusion and to develop a novel control system based on the incorporation of EEG-EMG fusion classifiers.A dataset of EEG and EMG signals were collected during dynamic elbow flexion-extension motions and used to develop EEG-EMG fusion models to classify task weight, as well as motion intention. A variety of fusion methods were investigated, such as a Weighted Average decisionlevel fusion (83.01 ± 6.04% accuracy) and Convolutional Neural Network-based input-level fusion (81.57 ± 7.11% accuracy), demonstrating that EEG-EMG fusion can classify more indirect tasks.A novel control system, referred to as a Task Weight Selective Controller (TWSC), was implemented using a Gain Scheduling-based approach, dictated by external load estimations from an EEG-EMG fusion classifier. To improve system stability, classifier prediction debouncing was also proposed to reduce misclassifications through filtering. Performance of the TWSC was evaluated using a developed upper-limb brace simulator. Due to simulator limitations, no significant difference in error was observed between the TWSC and PID control. However, results did demonstrate the feasibility of prediction debouncing, showing it provided smoother device motion. Continued development of the TWSC, and EEG-EMG fusion techniques will ultimately result in wearable devices that are able to adapt to changing loads more effectively, serving to improve the user experience during operation.
590 $a School code: 0784.
650 4 $a Load. $3 3562902
650 4 $a Co authorship. $3 3687004
650 4 $a Electrodes. $3 629151
650 4 $a Neural networks. $3 677449
650 4 $a Signal processing. $3 533904
650 4 $a Robotics. $3 519753
650 4 $a Electrical engineering. $3 649834
690 $a 0771
690 $a 0800
690 $a 0544
710 2 $a The University of Western Ontario (Canada). $3 1017622
773 0 $t Dissertations Abstracts International $g 85-06B.
790 $a 0784
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30737631