2018
DOI: 10.1080/02640414.2018.1521769
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Abstract: 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 d… Show more

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Cited by 177 publications
(129 citation statements)
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References 126 publications
(355 reference statements)
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“…At the first level of classification, determining if the dancer was jumping or lifting their leg, using all six sensors and not including transitions, the model developed in this study performed superiorly to previously developed HAR algorithms in sport [10,12,17,33], with an average degree of accuracy of 98.2%. Convolutional neural networks have previously been applied to a single wearable sensor's accelerometer output to identify 10 different specific strikes in beach volleyball at a single level of classification with a lower classification accuracy of 83.2% [33].…”
Section: Discussionmentioning
confidence: 90%
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“…At the first level of classification, determining if the dancer was jumping or lifting their leg, using all six sensors and not including transitions, the model developed in this study performed superiorly to previously developed HAR algorithms in sport [10,12,17,33], with an average degree of accuracy of 98.2%. Convolutional neural networks have previously been applied to a single wearable sensor's accelerometer output to identify 10 different specific strikes in beach volleyball at a single level of classification with a lower classification accuracy of 83.2% [33].…”
Section: Discussionmentioning
confidence: 90%
“…Recently, more sophisticated machine learning techniques have been developed, such as deep learning for HAR [16,17]. Deep learning models are able to automatically learn features from raw data and are often able to achieve better performance than traditional machine learning because their added complexity allows the models to take greater advantage of larger and more complex training datasets [16].…”
Section: Introductionmentioning
confidence: 99%
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“…Inspired by the phenomenon of the human-visual-cortex system, CNNs extract high-level features from an image. Image analysis has more subfields like pattern recognition, digital geometry, medical imaging, and computer vision [57][58][59][60]. These subfields cover various modern-day applications in astronomy, defence, filtering, microscopy, remote sensing, robotics, and machine vision [61,62].…”
Section: Image Analysismentioning
confidence: 99%
“…Smart sensors are fast becoming key tools in performance analysis for decreasing the time of direct observation with optimal validity [13,16]. The symbiosis between both instruments (performance analysis and smart devices), has focused its development in high-performance sport, mainly [11] because of the desire for performance improvement and control of the training load in many sports, e.g., soccer [17,18], football [19], basketball [20], rugby [21,22], and tennis [3,13,23].…”
Section: Introductionmentioning
confidence: 99%