東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Understanding Motor Control for Assi...

Cubillos, Luis Hernan Cubillos,

Linked to FindBook

Google Book

Amazon

博客來

Understanding Motor Control for Assistive Technologies: From Biomechanics to Brain-Machine Interfaces /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Understanding Motor Control for Assistive Technologies: From Biomechanics to Brain-Machine Interfaces // Luis Hernan Cubillos Cubillos.
Author:	Cubillos, Luis Hernan Cubillos,
Description:	1 electronic resource (201 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
Contained By:	Dissertations Abstracts International86-11B.
Subject:	Biomedical engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32092706
ISBN:	9798314875902

Understanding Motor Control for Assistive Technologies: From Biomechanics to Brain-Machine Interfaces /
Cubillos, Luis Hernan Cubillos,

Understanding Motor Control for Assistive Technologies: From Biomechanics to Brain-Machine Interfaces /Luis Hernan Cubillos Cubillos. - 1 electronic resource (201 pages)

Source: Dissertations Abstracts International, Volume: 86-11, Section: B.

Millions of people in the world suffer from disorders that affect their motor control, including conditions like spinal cord injuries, strokes, and neurodegenerative diseases. These impairments can drastically reduce their mobility and independence, creating a need for advanced assistive technologies. This dissertation addresses these challenges by exploring how humans move and learn new motor skills, and how these insights may inform better assistive technologies, especially through brain-machine interfaces. We first focus on the biomechanics of the ankle joint, studying the reliability of its stiffness, viscosity, and inertia while standing and walking. The results show that stiffness and viscosity can be reliably estimated and may be valuable as a clinical metric as well as a goal for biomimetic prosthetic control. Then, we show the development of a new open-source web application for motor learning experiment design and show its validation for replicating previous studies, as well as for investigating how motor learning is transferred between limbs. Our results show that even short periods of rest can greatly improve motor skill learning as well as the existence of interlimb transfer learning. Finally, we apply these insights in the context of brain-machine interfaces, assistive devices that have helped restore motor and speech control by decoding signals from the brain. We first evaluate whether using latent dimensions, called brain and muscle synergies, can help in decoding brain and muscle data by aiding in compression, denoising, and generalizing to other task contexts. We found improvements in data compression, but not in denoising or generalization. Second, we evaluate the trade-offs of using an interpretable algorithmic approach that combined a principled approach with deep-learning for decoding finger movements from brain data. We found that explainability may not need to be sacrificed for good performance, and that an interpretable algorithm can provide insights into motor control as well as future avenues for model improvement. Overall, by integrating these distinct but related areas, this dissertation contributes to a comprehensive understanding of human motor control and how to improve it. Together, these contributions provide a foundation for assistive technologies that can restore mobility, enhance independence, and improve quality of life for individuals living with motor impairments.

English

ISBN: 9798314875902Subjects--Topical Terms:

535387
Biomedical engineering.
Subjects--Index Terms:

Brain-machine interfaces

Understanding Motor Control for Assistive Technologies: From Biomechanics to Brain-Machine Interfaces /
LDR:04030nmm a22004573i 4500 001 2432329
005 20250707081345.5
006 m o d
007 cr|nu||||||||
008 261011s2025 miu||||||m |||||||eng d
020 $a 9798314875902
035 $a (MiAaPQD)AAI32092706
035 $a (MiAaPQD)umichrackham006102
035 $a AAI32092706
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Cubillos, Luis Hernan Cubillos, $e author. $3 3821498
245 1 0 $a Understanding Motor Control for Assistive Technologies: From Biomechanics to Brain-Machine Interfaces / $c Luis Hernan Cubillos Cubillos.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2025
300 $a 1 electronic resource (201 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
500 $a Advisors: Chestek, Cynthia; Krishnan, Chandramouli Committee members: Gillespie, Brent; Rouse, Elliott.
502 $b Ph.D. $c University of Michigan $d 2025.
520 $a Millions of people in the world suffer from disorders that affect their motor control, including conditions like spinal cord injuries, strokes, and neurodegenerative diseases. These impairments can drastically reduce their mobility and independence, creating a need for advanced assistive technologies. This dissertation addresses these challenges by exploring how humans move and learn new motor skills, and how these insights may inform better assistive technologies, especially through brain-machine interfaces. We first focus on the biomechanics of the ankle joint, studying the reliability of its stiffness, viscosity, and inertia while standing and walking. The results show that stiffness and viscosity can be reliably estimated and may be valuable as a clinical metric as well as a goal for biomimetic prosthetic control. Then, we show the development of a new open-source web application for motor learning experiment design and show its validation for replicating previous studies, as well as for investigating how motor learning is transferred between limbs. Our results show that even short periods of rest can greatly improve motor skill learning as well as the existence of interlimb transfer learning. Finally, we apply these insights in the context of brain-machine interfaces, assistive devices that have helped restore motor and speech control by decoding signals from the brain. We first evaluate whether using latent dimensions, called brain and muscle synergies, can help in decoding brain and muscle data by aiding in compression, denoising, and generalizing to other task contexts. We found improvements in data compression, but not in denoising or generalization. Second, we evaluate the trade-offs of using an interpretable algorithmic approach that combined a principled approach with deep-learning for decoding finger movements from brain data. We found that explainability may not need to be sacrificed for good performance, and that an interpretable algorithm can provide insights into motor control as well as future avenues for model improvement. Overall, by integrating these distinct but related areas, this dissertation contributes to a comprehensive understanding of human motor control and how to improve it. Together, these contributions provide a foundation for assistive technologies that can restore mobility, enhance independence, and improve quality of life for individuals living with motor impairments.
546 $a English
590 $a School code: 0127
650 4 $a Biomedical engineering. $3 535387
650 4 $a Robotics. $3 519753
650 4 $a Neurosciences. $3 588700
650 4 $a Biomechanics. $3 548685
653 $a Brain-machine interfaces
653 $a Machine learning
653 $a Motor learning
690 $a 0541
690 $a 0771
690 $a 0800
690 $a 0317
690 $a 0648
710 2 $a University of Michigan. $b Robotics. $e degree granting institution. $3 3821376
720 1 $a Chestek, Cynthia $e degree supervisor.
720 1 $a Krishnan, Chandramouli $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-11B.
790 $a 0127
791 $a Ph.D.
792 $a 2025
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32092706