title: The 'DEEP' Landing Error Scoring System

authors: Hébert-Losier, Kim

subjects: Science & Technology

abstract: The Landing Error Scoring System (LESS) is an injury-risk screening tool used in sports; but scoring is time consuming, clinician-dependent, and generally inaccessible outside of elite sports. Our aim is to evidence that LESS scores can be automated using deep-learning-based computer vision combined with machine learning and compare the accuracy of LESS predictions using different video cropping and machine learning methods. Two-dimensional videos from 320 double-leg drop-jump landings with known LESS scores were analysed in OpenPose. Videos were cropped to key frames manually (clinician) and automatically (computer vision), and 42 kinematic features were extracted. A series of 10 × 10-fold cross-validation experiments were applied on full and balanced datasets to predict LESS scores. Random forest for regression outperformed linear and dummy regression models, yielding the lowest mean absolute error (1.23) and highest correlation (r = 0.63) between manual and automated scores. Sensitivity (0.82) and specificity (0.77) were reasonable for risk categorization (high-risk LESS ≥ 5 errors). Experiments using either a balanced (versus unbalanced) dataset or manual (versus automated) cropping method did not improve predictions. Further research on the automation would enhance the strength of the agreement between clinical and automated scores beyond its current levels, enabling quasi real-time scoring.

text: The 'DEEP' Landing Error Scoring System The Landing Error Scoring System (LESS) is an injury-risk screening tool used in sports; but scoring is time consuming, clinician-dependent, and generally inaccessible outside of elite sports. Our aim is to evidence that LESS scores can be automated using deep-learning-based computer vision combined with machine learning and compare the accuracy of LESS predictions using different video cropping and machine learning methods. Two-dimensional videos from 320 double-leg drop-jump landings with known LESS scores were analysed in OpenPose. Videos were cropped to key frames manually (clinician) and automatically (computer vision), and 42 kinematic features were extracted. A series of 10 × 10-fold cross-validation experiments were applied on full and balanced datasets to predict LESS scores. Random forest for regression outperformed linear and dummy regression models, yielding the lowest mean absolute error (1.23) and highest correlation (r = 0.63) between manual and automated scores. Sensitivity (0.82) and specificity (0.77) were reasonable for risk categorization (high-risk LESS ≥ 5 errors). Experiments using either a balanced (versus unbalanced) dataset or manual (versus automated) cropping method did not improve predictions. Further research on the automation would enhance the strength of the agreement between clinical and automated scores beyond its current levels, enabling quasi real-time scoring.

year: 2020

title: Book review: Issues in Contemporary Documentary

authors: Hight, Craig

subjects: book review

abstract: This article reviews the book “Issues in Contemporary Documentary” by Jane Chapman.

text: Book review: Issues in Contemporary Documentary This article reviews the book “Issues in Contemporary Documentary” by Jane Chapman.

year: 2010

title: A critical race analysis of Māori representation in university strategic documents in Aotearoa New Zealand

authors: Waitoki, Waikaremoana

subjects: critical race theory

abstract: Following the recent claims lodged at two universities in Aotearoa New Zealand alleging the existence of racism, there has been scepticism towards the professed commitments by universities to create an inclusive and safe environment for Indigenous Māori. As a Kaupapa Māori-informed study, we (a group of Māori and Tauiwi scholars) employed tenets of Critical Race Theory to examine how the representation of Māori is racialised and subordinated in university strategic documents. We located five predominant discourses portraying different mechanisms that reify whiteness in university practices such as the selective interpretation of Te Tiriti articles, targeted recruitment of Māori, framing of Māori as dependent on the Crown to succeed, commodification of mātauranga Māori, and avoidance of conversations about structural racism, colonisation, and racial equity. Our findings suggest that university strategic goal statements need to incorporate a critical race analysis, or else risk perpetuating practices that fall short of challenging the status quo.

text: A critical race analysis of Māori representation in university strategic documents in Aotearoa New Zealand Following the recent claims lodged at two universities in Aotearoa New Zealand alleging the existence of racism, there has been scepticism towards the professed commitments by universities to create an inclusive and safe environment for Indigenous Māori. As a Kaupapa Māori-informed study, we (a group of Māori and Tauiwi scholars) employed tenets of Critical Race Theory to examine how the representation of Māori is racialised and subordinated in university strategic documents. We located five predominant discourses portraying different mechanisms that reify whiteness in university practices such as the selective interpretation of Te Tiriti articles, targeted recruitment of Māori, framing of Māori as dependent on the Crown to succeed, commodification of mātauranga Māori, and avoidance of conversations about structural racism, colonisation, and racial equity. Our findings suggest that university strategic goal statements need to incorporate a critical race analysis, or else risk perpetuating practices that fall short of challenging the status quo.

year: 2024