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Adapting Manifold-Based Analyses to ...
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Hirsch, Steven Mark.
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Adapting Manifold-Based Analyses to Describe How Personal and Task Constraints Impact Lower Extremity Movement Variability During Exercise.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Adapting Manifold-Based Analyses to Describe How Personal and Task Constraints Impact Lower Extremity Movement Variability During Exercise./
作者:
Hirsch, Steven Mark.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:
186 p.
附註:
Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
Contained By:
Dissertations Abstracts International85-01B.
標題:
Biomechanics. -
電子資源:
https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30247230
ISBN:
9798379766085
Adapting Manifold-Based Analyses to Describe How Personal and Task Constraints Impact Lower Extremity Movement Variability During Exercise.
Hirsch, Steven Mark.
Adapting Manifold-Based Analyses to Describe How Personal and Task Constraints Impact Lower Extremity Movement Variability During Exercise.
- Ann Arbor : ProQuest Dissertations & Theses, 2023 - 186 p.
Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
Thesis (Ph.D.)--University of Toronto (Canada), 2023.
Despite the recent emphasis in the motor control literature quantifying the structure of movement variability, there is a paucity of research applying these concepts in exercise contexts. Measuring the structure of variability surrounding dynamic valgus motion, and how it is impacted by personal and task constraints, can be achieved by adapting Uncontrolled Manifold (UCM) analyses. This thesis aimed to build a foundation for a complementary approach to evaluating the execution of exercise tasks that considers both average movements and movement variability by addressing four main gaps in the literature. First, it is unclear which methods of computing dynamic valgus motion are most related to external knee abduction and external rotation moments. Second, it is unclear how many repetitions are required to monitor the variability surrounding dynamic valgus motion using adapted UCM analyses. Third, although ankle dorsiflexion range of motion (ROM) restrictions can influence the average dynamic valgus motion, it is unclear whether it also impacts the structure of movement variability. Fourth, research has demonstrated that average dynamic valgus motion is more strongly correlated between single-leg exercises. But, no studies have examined these relationships for the structure of movement variability. In Chapter 3, the data demonstrated that computing dynamic valgus motion as the orthogonal distance of the knee joint center from the hip-foot plane (OD) was a relatively better kinematic proxy of external knee abduction and external rotation moments in comparison to Cardan-Euler angles. In Chapter 4, UCM methods were adapted for use with OD. In Chapter 5, the data showed that participants should perform at least five repetitions to obtain saturated variability data for between-participant analyses and at least eight repetitions for within-participant analyses. Chapter 6 demonstrated that individuals' ankle dorsiflexion ROM had little influence on the structure of movement variability. Chapter 7 found that the task constraints imposed during exercise strongly influence the structure of movement variability. In summary, data from this thesis provide a platform for future research and practice to expand upon a complementary approach to evaluating the execution of exercise tasks by leveraging movement variability data.
ISBN: 9798379766085Subjects--Topical Terms:
548685
Biomechanics.
Subjects--Index Terms:
Movement assessment
Adapting Manifold-Based Analyses to Describe How Personal and Task Constraints Impact Lower Extremity Movement Variability During Exercise.
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Despite the recent emphasis in the motor control literature quantifying the structure of movement variability, there is a paucity of research applying these concepts in exercise contexts. Measuring the structure of variability surrounding dynamic valgus motion, and how it is impacted by personal and task constraints, can be achieved by adapting Uncontrolled Manifold (UCM) analyses. This thesis aimed to build a foundation for a complementary approach to evaluating the execution of exercise tasks that considers both average movements and movement variability by addressing four main gaps in the literature. First, it is unclear which methods of computing dynamic valgus motion are most related to external knee abduction and external rotation moments. Second, it is unclear how many repetitions are required to monitor the variability surrounding dynamic valgus motion using adapted UCM analyses. Third, although ankle dorsiflexion range of motion (ROM) restrictions can influence the average dynamic valgus motion, it is unclear whether it also impacts the structure of movement variability. Fourth, research has demonstrated that average dynamic valgus motion is more strongly correlated between single-leg exercises. But, no studies have examined these relationships for the structure of movement variability. In Chapter 3, the data demonstrated that computing dynamic valgus motion as the orthogonal distance of the knee joint center from the hip-foot plane (OD) was a relatively better kinematic proxy of external knee abduction and external rotation moments in comparison to Cardan-Euler angles. In Chapter 4, UCM methods were adapted for use with OD. In Chapter 5, the data showed that participants should perform at least five repetitions to obtain saturated variability data for between-participant analyses and at least eight repetitions for within-participant analyses. Chapter 6 demonstrated that individuals' ankle dorsiflexion ROM had little influence on the structure of movement variability. Chapter 7 found that the task constraints imposed during exercise strongly influence the structure of movement variability. In summary, data from this thesis provide a platform for future research and practice to expand upon a complementary approach to evaluating the execution of exercise tasks by leveraging movement variability data.
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