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Advancing the Real-World Assessment of Movement Quality Using Low-Cost Wearables and Advanced Data Analytics / THOMAS SWAIN

Swansea University Author: THOMAS SWAIN

  • E-Thesis under embargo until: 11th November 2029

DOI (Published version): 10.23889/SUThesis.68887

Abstract

Movement quality, an important, yet overlooked component of physical activity, is challenging to assess in real-world settings. This thesis aimed to advance understanding of the assessment and enhancement of movement quality among the general, recreationally active, population using wearable devices...

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Published: Swansea University, Wales, UK 2024
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
Supervisor: Mackintosh, Kelly A. ; McNarry, Melitta A.
URI: https://cronfa.swan.ac.uk/Record/cronfa68887
first_indexed 2025-02-13T17:04:19Z
last_indexed 2025-05-10T08:16:31Z
id cronfa68887
recordtype RisThesis
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spelling 2025-05-09T14:16:33.1348932 v2 68887 2025-02-13 Advancing the Real-World Assessment of Movement Quality Using Low-Cost Wearables and Advanced Data Analytics 2adaf05654ca3fb9d04c40d13c665026 THOMAS SWAIN THOMAS SWAIN true false 2025-02-13 Movement quality, an important, yet overlooked component of physical activity, is challenging to assess in real-world settings. This thesis aimed to advance understanding of the assessment and enhancement of movement quality among the general, recreationally active, population using wearable devices and other consumer technologies.Chapter 3 highlighted the improved efficacy of multi-sensor wearables for capturing comprehensive movement data, while showing that multiple devices better accommodate holistic movements. Chapter 3 identified two approaches for analysing movement quality: i) motion-based measurements; and ii) and classification methods. A commercial-grade angular rate, and gravity (MARG) sensor was validated in Chapter 4 for measuring device orientation using sensor-fusion. The sensor’s utility was confirmed under static conditions, though errors increased proportionally with higher angular velocities. Thereafter, Chapter 5 demonstrated the potential of commercial-grade MARG sensors to increase real-world applicability and accessibility of complex biomechanical models, but accentuated the requirement for strategic sensor placement and refined processing methods. Chapter 6 introduced a data augmentation technique that improved class sensitivity and mitigated classification bias in a support vector machine used to determine movement competency. The method addressed the challenges of small, imbalanced datasets, though the implications of subjective data labelling remain. Chapters 7 and 8 collectively highlighted the importance of accessible, user-centric technologies to support movement quality, with wearable-experts (Chapter 7) and the general population (Chapter 8) emphasising the need for convenient technologies and tailored multimodal feedback to address diverse user needs.This thesis demonstrated the potential of commercial-grade wearables to enhance movement quality, dependent on sensor placement, data processing, and feedback. While revealing current limitations in direct motion measurements, the potential for machine learning, supported by data augmentation, was highlighted to facilitate real- world assessments among the recreationally-active population. Multimodal feedback, centred around visualisations, was specifically promoted to maximise movement- quality assessment efficacy and user engagement. E-Thesis Swansea University, Wales, UK motor control, motor skill, kinematics, inertial measurement unit, pattern recognition, signal processing, biofeedback, movement proficiency 11 11 2024 2024-11-11 10.23889/SUThesis.68887 ORCiD identifier: https://orcid.org/0000-0003-3142-2399 COLLEGE NANME COLLEGE CODE Swansea University Mackintosh, Kelly A. ; McNarry, Melitta A. Doctoral Ph.D Polar Electro Oy Polar Electro Oy 2025-05-09T14:16:33.1348932 2025-02-13T16:45:22.5071895 Faculty of Science and Engineering School of Engineering and Applied Sciences - Sport and Exercise Sciences THOMAS SWAIN 1 Under embargo Under embargo 2025-02-13T17:00:22.1154176 Output 10267387 application/pdf E-Thesis true 2029-11-11T00:00:00.0000000 Copyright: The Author, Thomas Alexander Swain, 2024 true eng
title Advancing the Real-World Assessment of Movement Quality Using Low-Cost Wearables and Advanced Data Analytics
spellingShingle Advancing the Real-World Assessment of Movement Quality Using Low-Cost Wearables and Advanced Data Analytics
THOMAS SWAIN
title_short Advancing the Real-World Assessment of Movement Quality Using Low-Cost Wearables and Advanced Data Analytics
title_full Advancing the Real-World Assessment of Movement Quality Using Low-Cost Wearables and Advanced Data Analytics
title_fullStr Advancing the Real-World Assessment of Movement Quality Using Low-Cost Wearables and Advanced Data Analytics
title_full_unstemmed Advancing the Real-World Assessment of Movement Quality Using Low-Cost Wearables and Advanced Data Analytics
title_sort Advancing the Real-World Assessment of Movement Quality Using Low-Cost Wearables and Advanced Data Analytics
author_id_str_mv 2adaf05654ca3fb9d04c40d13c665026
author_id_fullname_str_mv 2adaf05654ca3fb9d04c40d13c665026_***_THOMAS SWAIN
author THOMAS SWAIN
author2 THOMAS SWAIN
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institution Swansea University
doi_str_mv 10.23889/SUThesis.68887
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Sport and Exercise Sciences{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Sport and Exercise Sciences
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description Movement quality, an important, yet overlooked component of physical activity, is challenging to assess in real-world settings. This thesis aimed to advance understanding of the assessment and enhancement of movement quality among the general, recreationally active, population using wearable devices and other consumer technologies.Chapter 3 highlighted the improved efficacy of multi-sensor wearables for capturing comprehensive movement data, while showing that multiple devices better accommodate holistic movements. Chapter 3 identified two approaches for analysing movement quality: i) motion-based measurements; and ii) and classification methods. A commercial-grade angular rate, and gravity (MARG) sensor was validated in Chapter 4 for measuring device orientation using sensor-fusion. The sensor’s utility was confirmed under static conditions, though errors increased proportionally with higher angular velocities. Thereafter, Chapter 5 demonstrated the potential of commercial-grade MARG sensors to increase real-world applicability and accessibility of complex biomechanical models, but accentuated the requirement for strategic sensor placement and refined processing methods. Chapter 6 introduced a data augmentation technique that improved class sensitivity and mitigated classification bias in a support vector machine used to determine movement competency. The method addressed the challenges of small, imbalanced datasets, though the implications of subjective data labelling remain. Chapters 7 and 8 collectively highlighted the importance of accessible, user-centric technologies to support movement quality, with wearable-experts (Chapter 7) and the general population (Chapter 8) emphasising the need for convenient technologies and tailored multimodal feedback to address diverse user needs.This thesis demonstrated the potential of commercial-grade wearables to enhance movement quality, dependent on sensor placement, data processing, and feedback. While revealing current limitations in direct motion measurements, the potential for machine learning, supported by data augmentation, was highlighted to facilitate real- world assessments among the recreationally-active population. Multimodal feedback, centred around visualisations, was specifically promoted to maximise movement- quality assessment efficacy and user engagement.
published_date 2024-11-11T05:25:29Z
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score 11.089572

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