To cultivate professional sports referees, we develop a sports referee training system, which can recognize whether a trainee wearing the Myo armband makes correct judging signals while watching a prerecorded professional game. The system has to correctly recognize a set of gestures related to official referee's signals (ORSs) and another set of gestures used to intuitively interact with the system. These two gesture sets involve both large motion and subtle motion gestures, and the existing sensor-based methods using handcrafted features do not work well on recognizing all kinds of these gestures. In this work, deep belief networks (DBNs) are utilized to learn more representative features for hand gesture recognition, and selective handcrafted features are combined with the DBN features to achieve more robust recognition results. Moreover, a hierarchical recognition scheme is designed to first recognize the input gesture as a large or subtle motion gesture, and the corresponding classifiers for large motion gestures and subtle motion gestures are further used to obtain the final recognition result. Moreover, the Myo armband consists of eight-channel surface electromyography (sEMG) sensors and an inertial measurement unit (IMU), and these heterogeneous signals can be fused to achieve better recognition accuracy. We take basketball as an example to validate the proposed training system, and the experimental results show that the proposed hierarchical scheme considering DBN features of multimodality data outperforms other methods.

In this article, a VR-based basketball training system comprising a standalone VR device and a tablet is proposed. The system is intended to improve the ability of players to understand offensive tactics and practice these tactics correctly. We compare the training effectiveness of various degrees of immersion, including a conventional basketball tactic board, a 2D monitor, and virtual reality. A multi-camera-based human tracking system was designed and built around a real-world basketball court to record and analyze the running trajectory of each player during tactical execution. The accuracy of the running path and hesitation time at each tactical step were evaluated for each participant. Furthermore, we assessed several subjective measurements, including simulator sickness, presence, and sport imagery ability, to conduct a more comprehensive exploration of the feasibility of the proposed VR framework for basketball tactics training. The results indicate that the proposed system is useful for learning complex tactics. Furthermore, high VR immersion training improves athletes’ abilities with regards to strategic imagery.

Decision-making plays an essential role in basketball offense. Offensive players must make effective decisions to score a basket in various defensive situations. Virtual reality (VR) has been widely used in the training of athletes to strengthen their ability to make optimal decisions by creating controllable repeatable training scenarios. In this article, an action-aware offensive decision-making training system for basketball using VR and artificial intelligence is proposed. The proposed system is composed of different virtual defensive scenarios and an offensive action recognition framework. Trainees wearing head-mounted display and a motion capture suit are trained by intuitively interacting with the VR system and receive decision suggestions when a bad one is made. This study changes the training media and methods to create an immersive training environment during the training phase and evaluates the training effectiveness. These training scenarios are a conventional tactics board, the proposed VR system with a prerecorded $360^\circ$ panorama video, and the VR system with computer-simulated virtual content. Results indicate that the training scenario affects the training in terms of decision time.

Vision Training is important for basketball players to effectively search for teammates who has wide-open opportunities to shoot, observe the defenders around the wide-open teammates and quickly choose a proper way to pass the ball to the most suitable one. We develop an immersive virtual reality (VR) system called VisionCoach to simulate the player's viewing perspective and generate three designed systematic vision training tasks to benefit the cultivating procedure. By recording the player's eye gazing and dribbling video sequence, the proposed system can analyze the vision-related behavior to understand the training effectiveness. To demonstrate the proposed VR training system can facilitate the cultivation of vision ability, we recruited 14 experienced players to participate in a 6-week between-subject study, and conducted a study by comparing the most frequently used 2D vision training method called Vision Performance Enhancement (VPE) program with the proposed system. Qualitative experiences and quantitative training results are reported to show that the proposed immersive VR training system can effectively improve player's vision ability in terms of gaze behavior and dribbling stability. Furthermore, training in the VR-VisionCoach Condition can transfer the learned abilities to real scenario more easily than training in the 2D-VPE Condition.

This paper demonstrates a gesture recognition method using multiple Inertial Measurement Unit (IMU) sensors, which can record acceleration and rotation information for hand joints. The proposed gesture recognition method comprises frequency ConvNet and TemporalNet to extract the representative features within a sliding window of IMU signals for recognizing various types of hand gestures. To validate the proposed gesture recognition method, basketball official referee signals (ORSs), which comprise sixty-five types of gestures including both large motion hand movement and subtle motion hand movement, are utilized as the main recognition task to evaluate the proposed method. The evaluation results reveal the proposed recognition model can achieve convinced performance, which outperforms other existing works. In addition, the satisfied performance of the proposed recognition model encourages us to develop a decision-making training (DMT) system for cultivating basketball referees. The results of subjective evaluations by the recruited 20 participants indicate the training system based on the proposed gestures recognition method can efficiently strengthen the decision-making skills of users.