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

Linked to FindBook

Google Book

Amazon

博客來

A Systematic Study of the Muscle Force-Deformation Relationship at the Human Elbow: Toward Physiology-Aware Assistive Device Control and Noninvasive Muscle Force Sensing.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Systematic Study of the Muscle Force-Deformation Relationship at the Human Elbow: Toward Physiology-Aware Assistive Device Control and Noninvasive Muscle Force Sensing./
Author:	Hallock, Laura.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	150 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
Contained By:	Dissertations Abstracts International83-12B.
Subject:	Robotics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28718064
ISBN:	9798438767480

A Systematic Study of the Muscle Force-Deformation Relationship at the Human Elbow: Toward Physiology-Aware Assistive Device Control and Noninvasive Muscle Force Sensing.
Hallock, Laura.

A Systematic Study of the Muscle Force-Deformation Relationship at the Human Elbow: Toward Physiology-Aware Assistive Device Control and Noninvasive Muscle Force Sensing. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 150 p.

Source: Dissertations Abstracts International, Volume: 83-12, Section: B.

Thesis (Ph.D.)--University of California, Berkeley, 2021.

This item must not be sold to any third party vendors.

While there exist a number of mechanically sophisticated exoskeletons, prostheses, and assistive robots, with articulations similar to those of the intact human arm and hand, these devices remain limited in their ability to augment human dexterity and safely interact with human users and collaborators. In particular, due to the limits of conventional sensing, robots remain locked in industrial cages, prosthesis users can often modulate only a single degree of freedom, and when human-device interactions do occur, we have almost no understanding of the resulting physiological impacts on the user's musculoskeletal system.This thesis begins to address these dual problems of device capability and safety by leveraging a novel signal class-muscle deformation, as measured via ultrasound-to probe individual muscle forces, which cannot in general be measured noninvasively but are key to understanding musculoskeletal dynamics during dexterous motion. Using the elbow flexion motion as a case study, this document describes first, preliminary characterization of this deformation and its relationship to output force; second, proof-of-concept use of this signal for a trajectory tracking task; third, collection of a comprehensive 3D deformation data set to systematically examine the force-deformation relationship; fourth, initial geometric analysis of this data set and a proposed framework for future modeling; and lastly, a discussion of the open-source release of code and data associated with this project, preliminary work on alternative sensing modalities, and speculation on future applications in robotics and medicine. This dissertation work-collectively released as the OpenArm project for collaborative research use at https://simtk.org/projects/openarm-paves the way for future investigation on both the extraction of multiple independent signals for high-dimensional device control and enhanced overall understanding of the joint human-machine dynamical system.

ISBN: 9798438767480Subjects--Topical Terms:

519753
Robotics.
Subjects--Index Terms:

deformation modeling

A Systematic Study of the Muscle Force-Deformation Relationship at the Human Elbow: Toward Physiology-Aware Assistive Device Control and Noninvasive Muscle Force Sensing.
LDR:03220nmm a2200349 4500 001 2348079
005 20220906075200.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798438767480
035 $a (MiAaPQ)AAI28718064
035 $a AAI28718064
040 $a MiAaPQ $c MiAaPQ
100 1 $a Hallock, Laura. $3 3687399
245 1 0 $a A Systematic Study of the Muscle Force-Deformation Relationship at the Human Elbow: Toward Physiology-Aware Assistive Device Control and Noninvasive Muscle Force Sensing.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 150 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
500 $a Advisor: Bajcsy, Ruzena.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a While there exist a number of mechanically sophisticated exoskeletons, prostheses, and assistive robots, with articulations similar to those of the intact human arm and hand, these devices remain limited in their ability to augment human dexterity and safely interact with human users and collaborators. In particular, due to the limits of conventional sensing, robots remain locked in industrial cages, prosthesis users can often modulate only a single degree of freedom, and when human-device interactions do occur, we have almost no understanding of the resulting physiological impacts on the user's musculoskeletal system.This thesis begins to address these dual problems of device capability and safety by leveraging a novel signal class-muscle deformation, as measured via ultrasound-to probe individual muscle forces, which cannot in general be measured noninvasively but are key to understanding musculoskeletal dynamics during dexterous motion. Using the elbow flexion motion as a case study, this document describes first, preliminary characterization of this deformation and its relationship to output force; second, proof-of-concept use of this signal for a trajectory tracking task; third, collection of a comprehensive 3D deformation data set to systematically examine the force-deformation relationship; fourth, initial geometric analysis of this data set and a proposed framework for future modeling; and lastly, a discussion of the open-source release of code and data associated with this project, preliminary work on alternative sensing modalities, and speculation on future applications in robotics and medicine. This dissertation work-collectively released as the OpenArm project for collaborative research use at https://simtk.org/projects/openarm-paves the way for future investigation on both the extraction of multiple independent signals for high-dimensional device control and enhanced overall understanding of the joint human-machine dynamical system.
590 $a School code: 0028.
650 4 $a Robotics. $3 519753
650 4 $a Biomechanics. $3 548685
653 $a deformation modeling
653 $a Human musculoskeletal modeling
653 $a Human-robot interaction
653 $a Ultrasound imaging
690 $a 0771
690 $a 0648
710 2 $a University of California, Berkeley. $b Electrical Engineering & Computer Sciences. $3 1671057
773 0 $t Dissertations Abstracts International $g 83-12B.
790 $a 0028
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28718064