Wearable Motion Sensor Based Analysis of Swing Sports

Anand, Akash; Sharma, Manish; Srivastava, Rupika; Kaligounder, Lakshmi; Prakash, Divya

doi:10.1109/icmla.2017.0-149

Cited by 46 publications

(54 citation statements)

References 16 publications

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“…One study used an unsupervised discriminative analysis approach for detection and classification of tennis strokes (Kos & Kramberger, 2017). Five IMU-based study investigated a deep learning approach including using Convolutional Neural Networks (CNN) (Anand et al, 2017;Jiao et al, 2018;Kautz et al, 2017;Rassem et al, 2017) and Long Short Term Memory (LSTM) (Hochreiter & Schmidhuber, 1997) architectures (Rassem et al, 2017;. In order to assess the effectiveness of the various classifiers from each study, model performance measures quantify and visualise the predictive performance as reported in the following section.…”

Section: Vision Capture Specificationmentioning

confidence: 99%

“…Movement detection methods were specifically reported in 16 studies (Adelsberger & 347 Anand et al, 2017;Connaghan et al, 2011;Groh et al, 2016, Jensen et 348 al., 2013Kautz et al, 2017;Kelly et al, 2012;Kos & Kramberger, 2017;Ó Conaire et al, 349 2010;Rindal et al, 2018;Salman et al, 2017;Schuldhaus et al, 2015;Whiteside, Cant, Connolly, the dimensionality of the feature vector was used in 11 studies. Both feature extraction and selection (n = 16) (Adelsberger & Tröster, 2013;Buckley et al, 2017; 360 Buthe et al, 2016;Groh et al, 2016Groh et al, , 2017Groh et al, , 2015Jensen et al, 2016;Kautz et al, 2017;Kelly et al, 361 2012;Ó Conaire et al, 2010;Pernek et al, 2015;Salman et al, 2017;Schuldhaus et al, 2015; 362 Whiteside et al, 2017).…”

mentioning

confidence: 99%

“…Convolutional Neural Networks (CNN) (Anand et al, 2017;Jiao et al, 2018; 372 Kautz et al, 2017;Rassem et al, 2017) and Long Short Term Memory (LSTM) (Hochreiter & 373 Schmidhuber, 1997) architectures (Rassem et al, 2017;Sharma, Srivastava, Anand, Prakash, & 374 Kaligounder, 2017). In order to assess the effectiveness of the various classifiers from each study,…”

mentioning

confidence: 99%

“…Anand et al, 2017;Buckley et al, 2017;Connaghan et al, 2011;Groh et al, 2015;Jensen et al, 2013;Jiao et al, 2018;Kobsar et al, 2014;Kos & Kramberger, 2017; M. A. O'Reilly et al, 2017a; Ó Conaire et al, 2010; Pernek et al, 2015; Qaisar et al, 2013; Rindal et al, 2018;…”

mentioning

confidence: 99%

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Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance

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Sweeting

Ball

et al. 2018

Journal of Sports Sciences

219

148

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Objective assessment of an athlete's performance is of importance in elite sports to facilitate detailed analysis. The implementation of automated detection and recognition of sport-specific movements overcomes the limitations associated with manual performance analysis methods. The object of this study was to systematically review the literature on machine and deep learning for sport-specific movement recognition using inertial measurement unit (IMU) and, or computer vision data inputs. A search of multiple databases was undertaken. Included studies must have investigated a sportspecific movement and analysed via machine or deep learning methods for model development. A total of 52 studies met the inclusion and exclusion criteria. Data preprocessing, processing, model development and evaluation methods varied across the studies. Model development for movement recognition were predominantly undertaken using supervised classification approaches. A kernel form of the Support Vector Machine algorithm was used in 53% of IMU and 50% of vision-based studies. Twelve studies used a deep learning method as a form of Convolutional Neural Network algorithm and one study also adopted a Long Short Term Memory architecture in their model. The adaptation of experimental setup , data pre-processing, and model development methods are best considered in relation to the characteristics of the targeted sports movement(s).

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Section: Vision Capture Specificationmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance

Cust

Sweeting

Ball

et al. 2018

Journal of Sports Sciences

219

148

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“…Os trabalhos de [Anand et al 2018] e [Kasiri-Bidhendi et al 2015] estudam esportes individuais, como tênis (classificação de jogadas) e boxe (identificação de golpes) respectivamente, porém focados no esporte em si, e não na melhoria ou aperfeiçoamento do atleta como objetivo principal.…”

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Sport Analytics A Systematic Review

Bogo

Oliveira

Lima

et al. 2019

Anais Do XVI Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2019)

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Ciência de Dados é uma das áreas da Tecnologia de Informação em maior desenvolvimento, sendo usada nos mais diversos ramos, impactando fortemente a competitividade das empresas e a forma como as pessoas interagem. O seu uso nos esportes é designado como Sport Analytics, sendo usado para entender padrões, definir táticas/treinamentos e prever resultados. Este artigo procura resumir as pesquisas que estão sendo realizadas neste campo, bem como entender o conhecimento gerado e como ele está sendo aplicado.

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Sports analytics review: Artificial intelligence applications, emerging technologies, and algorithmic perspective

Ghosh

Ramamurthy

Chakma

et al. 2023

WIREs Data Min & Knowl

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The rapid and impromptu interest in the coupling of machine learning (ML) algorithms with wearable and contactless sensors aimed at tackling real‐world problems warrants a pedagogical study to understand all the aspects of this research direction. Considering this aspect, this survey aims to review the state‐of‐the‐art literature on ML algorithms, methodologies, and hypotheses adopted to solve the research problems and challenges in the domain of sports. First, we categorize this study into three main research fields: sensors, computer vision, and wireless and mobile‐based applications. Then, for each of these fields, we thoroughly analyze the systems that are deployable for real‐time sports analytics. Next, we meticulously discuss the learning algorithms (e.g., statistical learning, deep learning, reinforcement learning) that power those deployable systems while also comparing and contrasting the benefits of those learning methodologies. Finally, we highlight the possible future open‐research opportunities and emerging technologies that could contribute to the domain of sports analytics.This article is categorized under: Technologies > Machine Learning Technologies > Artificial Intelligence Technologies > Internet of Things

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Wearable Motion Sensor Based Analysis of Swing Sports

Cited by 46 publications

References 16 publications

Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance

Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance

Sport Analytics A Systematic Review

Sports analytics review: Artificial intelligence applications, emerging technologies, and algorithmic perspective

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