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Wearable Data Synthesis for Portable...

Pearl, Owen Douglas.

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Wearable Data Synthesis for Portable Biomechanics.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Wearable Data Synthesis for Portable Biomechanics./
Author:	Pearl, Owen Douglas.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	130 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
Subject:	Biomechanics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31143053
ISBN:	9798382833057

Wearable Data Synthesis for Portable Biomechanics.
Pearl, Owen Douglas.

Wearable Data Synthesis for Portable Biomechanics. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 130 p.

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Thesis (Ph.D.)--Carnegie Mellon University, 2024.

.

Biomechanical analysis and musculoskeletal simulation techniques have been developed in laboratory settings to help us understand the body and the mechanisms of motion with enough precision to evaluate health and suggest rehabilitative treatments that aim to improve musculoskeletal function. To achieve this same result outside of a laboratory, portable sensors must be made available that can monitor the motion of the body, kinematics, the contact interactions between the body and the surrounding environment, kinetics, and the forces generated by the body that enable locomotion, muscle dynamics. While many portable sensing options have been developed in recent years, the accuracy of portable biomechanics monitoring techniques has yet to match the accuracy of traditional laboratory-based tools and remains insufficient for many rehabilitative applications. One cause for insufficient accuracy is that portable sensing techniques are often explored in isolation and unable to overcome their unique limitations. Another cause is that many possible alternative portable sensing approaches developed outside of the biomechanics field have yet to be fully investigated for their potential to serve as biomechanics monitoring tools in combination with existing portable techniques.Here, I show how developments in inertial sensing and computer vision techniques can be intelligently synthesized using either kinematics equations of motion or rigid body dynamics equations of motion to enable more accurate portable predictions of body kinematics than approaches which utilize only inertial sensors or computer vision (Chapter 2). I also show how nuance exists in the choice of fusion approach depending on the quality of inertial sensing data and computer vision estimates. A prominent trade-off exists when adding rigid body dynamics into the synthesis paradigm, and adding dynamics is helpful so long as the dynamics equations provide more accurate estimates of angular velocities and accelerations than inertial sensing data, which is likely to occur during real-world applications due to soft-tissue motion artifacts.Next, I show how capacitive sensing, a sensing technique that has been understudied in biomechanics, can be adapted for use as a customizable, comfortable, lightweight, and sensitive biomechanics monitoring wearable sensor that enables muscle-activity measurements with the fidelity of gold-standard laboratory-based techniques (Chapter 3). Capacitive sensing muscle-activity measurements can then be synthesized with inertial sensors to enable full-body kinematics, kinetics, and muscle dynamics predictions with comparable accuracy to that of marker-based motion capture. Altogether, these findings show the importance of extensively validating and incorporating new sensing approaches into biomechanics monitoring tools that seamlessly integrate with other sensors to cover their weaknesses. I suggest that future biomechanics monitoring emphasize more nuanced applications, where multiple sensing modalities are fused intelligently and optimized for specific applications to maximize monitoring accuracy and intervention efficacy in each local domain, rather than sacrificing local accuracy to reach for a one-size-fits-all solution. In the future, I envision the development of a list of locally optimized biomechanics monitoring best practices, where specific sensor combinations with precise placements and parameterized computational algorithms are tuned to maximize monitoring accuracy for use on specific clinical populations and pathologies. I believe this more nuanced approach to biomechanics monitoring will enable the development of the next generation of rehabilitative strategies to improve and sustain more widespread musculoskeletal well-being.

ISBN: 9798382833057Subjects--Topical Terms:

548685
Biomechanics.
Subjects--Index Terms:

Capacitive sensing

Wearable Data Synthesis for Portable Biomechanics.
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502 $a Thesis (Ph.D.)--Carnegie Mellon University, 2024.
506 $a .
520 $a Biomechanical analysis and musculoskeletal simulation techniques have been developed in laboratory settings to help us understand the body and the mechanisms of motion with enough precision to evaluate health and suggest rehabilitative treatments that aim to improve musculoskeletal function. To achieve this same result outside of a laboratory, portable sensors must be made available that can monitor the motion of the body, kinematics, the contact interactions between the body and the surrounding environment, kinetics, and the forces generated by the body that enable locomotion, muscle dynamics. While many portable sensing options have been developed in recent years, the accuracy of portable biomechanics monitoring techniques has yet to match the accuracy of traditional laboratory-based tools and remains insufficient for many rehabilitative applications. One cause for insufficient accuracy is that portable sensing techniques are often explored in isolation and unable to overcome their unique limitations. Another cause is that many possible alternative portable sensing approaches developed outside of the biomechanics field have yet to be fully investigated for their potential to serve as biomechanics monitoring tools in combination with existing portable techniques.Here, I show how developments in inertial sensing and computer vision techniques can be intelligently synthesized using either kinematics equations of motion or rigid body dynamics equations of motion to enable more accurate portable predictions of body kinematics than approaches which utilize only inertial sensors or computer vision (Chapter 2). I also show how nuance exists in the choice of fusion approach depending on the quality of inertial sensing data and computer vision estimates. A prominent trade-off exists when adding rigid body dynamics into the synthesis paradigm, and adding dynamics is helpful so long as the dynamics equations provide more accurate estimates of angular velocities and accelerations than inertial sensing data, which is likely to occur during real-world applications due to soft-tissue motion artifacts.Next, I show how capacitive sensing, a sensing technique that has been understudied in biomechanics, can be adapted for use as a customizable, comfortable, lightweight, and sensitive biomechanics monitoring wearable sensor that enables muscle-activity measurements with the fidelity of gold-standard laboratory-based techniques (Chapter 3). Capacitive sensing muscle-activity measurements can then be synthesized with inertial sensors to enable full-body kinematics, kinetics, and muscle dynamics predictions with comparable accuracy to that of marker-based motion capture. Altogether, these findings show the importance of extensively validating and incorporating new sensing approaches into biomechanics monitoring tools that seamlessly integrate with other sensors to cover their weaknesses. I suggest that future biomechanics monitoring emphasize more nuanced applications, where multiple sensing modalities are fused intelligently and optimized for specific applications to maximize monitoring accuracy and intervention efficacy in each local domain, rather than sacrificing local accuracy to reach for a one-size-fits-all solution. In the future, I envision the development of a list of locally optimized biomechanics monitoring best practices, where specific sensor combinations with precise placements and parameterized computational algorithms are tuned to maximize monitoring accuracy for use on specific clinical populations and pathologies. I believe this more nuanced approach to biomechanics monitoring will enable the development of the next generation of rehabilitative strategies to improve and sustain more widespread musculoskeletal well-being.
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653 $a Machine learning
653 $a Portable sensing
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