Recall (R), precision (P), and F 1 score (F 1 ) for FCN and ResNet for the respective shot types of the best model.

Recall (R), precision (P), and F 1 score (F 1 ) for FCN and ResNet for the respective shot types of the best model.

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Data analysis plays an increasingly valuable role in sports. The better the data that is analysed, the more concise training methods that can be chosen. Several solutions already exist for this purpose in the tennis industry; however, none of them combine data generation with a wristband and classification with a deep convolutional neural network (...

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... normalized confusion matrices of the respectively best iterations in Figure 12 indicate strong diagonals. Table 3 shows the results in more detail. The F 1 score for all shot types is in the range of 94-97%. ...

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... Accordingly, the continued development of these models in sport can benefit coaches and sports medicine staff to monitor athlete training loads and record sport-specific event data. In tennis, wearable sensors positioned on the hitting arm or racquet utilise accurate machine learning models for automated stroke detection [6,7] and present more affordable and accessible technological approaches to monitoring tennis training. However, their placement precludes quantification of runningbased movement [7], which is also a critical component of tennis training and match-play profiles [8]. ...
... This highlights an advantage compared to wrist-worn or racquet-mounted sensors, traditionally used in tennis, that report stroke events but provide limited insight into the locomotor demands of the sport. Whilst emerging evidence in tennis has utilised wrist-worn sensors and classify movement as "sprinting", "running", "walking" and "standing" activities [6], their validity is currently unavailable in the literature. Regardless, exploration of prototype machine learning algorithms from a single commercial cervically mounted wearable sensor to determine both stroke and movement events is currently missing. ...
... Despite this possible limitation, it remains likely that high accuracy classification rates for major strokes remain indicative of the unique trunk rotation and lateral flexion signatures registered from the gyroscope and accelerometer. This could also explain the low (≤3%) false positive rates from the present algorithm and further highlights its suitability for tennis stroke detection given the similarities with results from studies using wrist-worn devices [6]. ...
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... One of the more recent papers, a paper titled Classification of Tennis Shots with a Neural Network Approach was published in 2021. This research paper discusses the use of Neural Networks to classify tennis shots [11]. This paper uses data collected from accelerometers, gyroscopes, magnetometers and audio signals to classify tennis shots in five categories: forehand topspin, forehand slice, backhand topspin, backhand slice, and serve. ...
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