title: Pupillometry as a measure of cognitive load during a motor task: Comparing analogy learning to traditional coaching methods

authors: Stone, Tyla

subjects: analogy

abstract: Analogies are thought to reduce cognitive load during motor skill acquisition, yet a reliable and direct measure of cognitive load during movement remains elusive. This thesis seeks to contribute to the existing gap by exploring the use of pupillometry as a potential tool to evaluate cognitive load during a hockey push-pass task. Additionally, the study explores whether analogy instructions contribute to consistency in mental effort and performance compared to discovery and explicit instructional approaches.
Chapter One provides a comprehensive review of relevant literature, setting the stage for the further investigation. In Chapter Two, an experiment examines pupil dilation in dual-task conditions versus single-task conditions during hockey push-passes. Results revealed significant differences in pupil dilation for single task versus dual task performance. The study also evaluates pupillometry and performance across analogy, discovery, and explicit instruction groups, finding no significant differences. 
Chapter Three summarizes key findings and delves into their implications. The results suggest pupillometry's potential as an accurate measure of cognitive load during motor learning, with considerations for further refinement being addressed. The thesis concludes by emphasizing the need for additional research to deepen our understanding of the relationship between pupillometry and cognitive load measurement during a motor task.

text: Pupillometry as a measure of cognitive load during a motor task: Comparing analogy learning to traditional coaching methods Analogies are thought to reduce cognitive load during motor skill acquisition, yet a reliable and direct measure of cognitive load during movement remains elusive. This thesis seeks to contribute to the existing gap by exploring the use of pupillometry as a potential tool to evaluate cognitive load during a hockey push-pass task. Additionally, the study explores whether analogy instructions contribute to consistency in mental effort and performance compared to discovery and explicit instructional approaches.
Chapter One provides a comprehensive review of relevant literature, setting the stage for the further investigation. In Chapter Two, an experiment examines pupil dilation in dual-task conditions versus single-task conditions during hockey push-passes. Results revealed significant differences in pupil dilation for single task versus dual task performance. The study also evaluates pupillometry and performance across analogy, discovery, and explicit instruction groups, finding no significant differences. 
Chapter Three summarizes key findings and delves into their implications. The results suggest pupillometry's potential as an accurate measure of cognitive load during motor learning, with considerations for further refinement being addressed. The thesis concludes by emphasizing the need for additional research to deepen our understanding of the relationship between pupillometry and cognitive load measurement during a motor task.

year: 2023

title: Automation of the landing error scoring system using inertial measurement units

authors: Hamilton, Zane

abstract: The quantification of biomechanics is important for making informed decisions when implementing appropriate training interventions for improving performance and reducing injury risk. This study evaluated the viability of movement data provided by wearable inertial measurement units (IMUs) to automate the scoring of the Landing Error Scoring System (LESS). The LESS is an assessment tool used for identifying high-risk movement patterns in a double leg jump landing; however, the LESS is scored by experts using 2D video recordings, which limits large-scale screening. Movement data provided by three IMUs were used to train several out-of-the-box machine learning models, aiming to predict the result of the 17 LESS scoring items individually. Raw movement data was processed and segmented into the key phases of the movement, where additional features were derived from these segments. A supervised learning approach was taken, using a dataset containing 218 LESS scores derived from 40 participants as the desired output for each model. Comparisons were made between various subsets of features, and the features with the greatest importance on accuracy in the best performing models were extracted. Results showed limited improvements to a ZeroR approach, and features with the greatest importance on the best performing models had minimal relevance to the movement involved. Performance of LESS scoring automation using IMU data may be improved with further developments on this approach.

text: Automation of the landing error scoring system using inertial measurement units The quantification of biomechanics is important for making informed decisions when implementing appropriate training interventions for improving performance and reducing injury risk. This study evaluated the viability of movement data provided by wearable inertial measurement units (IMUs) to automate the scoring of the Landing Error Scoring System (LESS). The LESS is an assessment tool used for identifying high-risk movement patterns in a double leg jump landing; however, the LESS is scored by experts using 2D video recordings, which limits large-scale screening. Movement data provided by three IMUs were used to train several out-of-the-box machine learning models, aiming to predict the result of the 17 LESS scoring items individually. Raw movement data was processed and segmented into the key phases of the movement, where additional features were derived from these segments. A supervised learning approach was taken, using a dataset containing 218 LESS scores derived from 40 participants as the desired output for each model. Comparisons were made between various subsets of features, and the features with the greatest importance on accuracy in the best performing models were extracted. Results showed limited improvements to a ZeroR approach, and features with the greatest importance on the best performing models had minimal relevance to the movement involved. Performance of LESS scoring automation using IMU data may be improved with further developments on this approach.

year: 2024-07-28

title: Maori & Psychology Research Unit annual report 2005

authors: Rua, Mohi

subjects: Maori and Psychology Research Unit

abstract: Annual report of the Maori and Psychology Research Unit (MPRU) 2005. The unit was established in August of 1997. The unit is designed to provide a catalyst and support network for enhancing research concerning the psychological needs, aspirations, and priorities of Maori people. The MPRU is well situated to draw together skilled and experienced interdisciplinary research groups by networking and establishing working relationships with staff and students within the Faculty of Arts and Social Sciences, the University, and the wider community.

text: Maori & Psychology Research Unit annual report 2005 Annual report of the Maori and Psychology Research Unit (MPRU) 2005. The unit was established in August of 1997. The unit is designed to provide a catalyst and support network for enhancing research concerning the psychological needs, aspirations, and priorities of Maori people. The MPRU is well situated to draw together skilled and experienced interdisciplinary research groups by networking and establishing working relationships with staff and students within the Faculty of Arts and Social Sciences, the University, and the wider community.

year: 2005