Using Wearable Sensors and a Convolutional Neural Network for Catch Detection in American Football

Hollaus, Bernhard; Stabinger, Sebastian; Mehrle, Andreas; Raschner, Christian

doi:10.3390/s20236722

Cited by 14 publications

(15 citation statements)

References 44 publications

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“…For example, in large sports such as basketball and soccer, statistics of players' running distance and trajectory and analysis of athletes' human posture in swimming and diving can help coaches and athletes improve the strength of the team to a certain extent [ 2 ]. The demand for analysis and understanding of sports game videos is increasing, but with the explosive growth in the number of sports game videos, it has been difficult for the traditional manual annotation-based sports game video analysis methods to meet this expanding demand due to their high cost and many limitations [ 3 ]. The target detection technique can detect the position of athletes, the target tracking technique can count the athletes' motion trajectory, and the athlete pose estimation can identify the athletes' pose.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] A Study of Athlete Pose Estimation Techniques in Sports Game Videos Combining Multiresidual Module Convolutional Neural Networks

Liu

2021

Computational Intelligence and Neuroscience

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In this paper, we propose a multiresidual module convolutional neural network-based method for athlete pose estimation in sports game videos. The network firstly designs an improved residual module based on the traditional residual module. Firstly, a large perceptual field residual module is designed to learn the correlation between the athlete components in the sports game video within a large perceptual field. A multiscale residual module is designed in the paper to better solve the inaccuracy of the pose estimation due to the problem of scale change of the athlete components in the sports game video. Secondly, these three residual modules are used as the building blocks of the convolutional neural network. When the resolution is high, the large perceptual field residual module and the multiscale residual module are used to capture information in a larger range as well as at each scale, and when the resolution is low, only the improved residual module is used. Finally, four multiresidual module convolutional neural networks are used to form the final multiresidual module stacked convolutional neural network. The neural network model proposed in this paper achieves high accuracy of 89.5% and 88.2% on the upper arm and lower arm, respectively, so the method in this paper reduces the influence of occlusion on the athlete’s posture estimation to a certain extent. Through the experiments, it can be seen that the proposed multiresidual module stacked convolutional neural network-based method for athlete pose estimation in sports game videos further improves the accuracy of athlete pose estimation in sports game videos.

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Section: Introductionmentioning

confidence: 99%

[Retracted] A Study of Athlete Pose Estimation Techniques in Sports Game Videos Combining Multiresidual Module Convolutional Neural Networks

Liu

2021

Computational Intelligence and Neuroscience

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“…To enable shot detection in tennis, a platform to gather data of the shots is needed and should provide data containing information on the shot type. Other sports have used wearables successfully to gather such data [ 20 , 21 , 22 ]. We also used this approach using wearables in this paper.…”

Section: Methodsmentioning

confidence: 99%

“…The wearable used for recording the dataset was the SensorTile development kit (STEVAL-STLKT01V1) of STMicroelectronics, Geneva, Switzerland, which is illustrated in Figure 2 and includes the sensors mentioned in Table 2 . The development kit is chosen for the tennis shot detection task since it has already proved its abilities in a catch detection application for American Football [ 20 ]. Additionally, the sensor kit comprises all relevant sensors to monitor motion, pressure, and audio in satisfying sample rates and ranges, which is key for a later classification.…”

Section: Methodsmentioning

confidence: 99%

Classification of Tennis Shots with a Neural Network Approach

Ganser

Hollaus

Stabinger³

2021

Sensors

Self Cite

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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 (CNN). In this article, we demonstrate the development of a reliable shot detection trigger and a deep neural network that classifies tennis shots into three and five shot types. We generate a dataset for the training of neural networks with the help of a sensor wristband, which recorded 11 signals, including an inertial measurement unit (IMU). The final dataset included 5682 labelled shots of 16 players of age 13–70 years, predominantly at an amateur level. Two state-of-the-art architectures for time series classification (TSC) are compared, namely a fully convolutional network (FCN) and a residual network (ResNet). Recent advances in the field of machine learning, like the Mish activation function and the Ranger optimizer, are utilized. Training with the rather inhomogeneous dataset led to an F1 score of 96% in classification of the main shots and 94% for the expansion. Consequently, the study yielded a solid base for more complex tennis analysis tools, such as the indication of success rates per shot type.

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“…[ 110,231 ] In this regard, three types of data analyzing algorithms are commonly applied, such are artificial neural networks (ANNs), convolutional neural networks (CNNs) and long short‐term memory (LSTM) networks augmented recurrent neural network (RNNs). [ 110,232–237 ] For example, an ANN algorithm was trained to classify human subjects’ physiological states by extracting and analyzing collected EP signals by a set of skin‐friendly soft wearable sensors. [ 110 ] The accuracy of the classification performances by the trained ANN was successfully validated in two aspects: 1) analyzed outcomes by the trained ANN matched well with human volunteers’ feelings, and 2) the trained ANN changed its output correspondingly along with the changed physiological states of the human subject.…”

Section: Closed‐loop Sensing and Therapy Systemsmentioning

confidence: 99%

Soft Wearable Healthcare Materials and Devices

Lyu

Gong

Yin

et al. 2021

Adv Healthcare Materials

106

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In spite of advances in electronics and internet technologies, current healthcare remains hospital‐centred. Disruptive technologies are required to translate state‐of‐art wearable devices into next‐generation patient‐centered diagnosis and therapy. In this review, recent advances in the emerging field of soft wearable materials and devices are summarized. A prerequisite for such future healthcare devices is the need of novel materials to be mechanically compliant, electrically conductive, and biologically compatible. It is begun with an overview of the two viable design strategies reported in the literatures, which is followed by description of state‐of‐the‐art wearable healthcare devices for monitoring physical, electrophysiological, chemical, and biological signals. Self‐powered wearable bioenergy devices are also covered and sensing systems, as well as feedback‐controlled wearable closed‐loop biodiagnostic and therapy systems. Finally, it is concluded with an overall summary and future perspective.

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Using Wearable Sensors and a Convolutional Neural Network for Catch Detection in American Football

Cited by 14 publications

References 44 publications

[Retracted] A Study of Athlete Pose Estimation Techniques in Sports Game Videos Combining Multiresidual Module Convolutional Neural Networks

[Retracted] A Study of Athlete Pose Estimation Techniques in Sports Game Videos Combining Multiresidual Module Convolutional Neural Networks

Classification of Tennis Shots with a Neural Network Approach

Soft Wearable Healthcare Materials and Devices

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