Skeleton-based comparison of throwing motion for handball players

Elaoud, Amani; Barhoumi, Walid; Zagrouba, Ezzeddine; Agrebi, Brahim

doi:10.1007/s12652-019-01301-6

Cited by 22 publications

(13 citation statements)

References 47 publications

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“…A hierarchical model for the whole multiplayer scene is proposed, which describes human behaviour from multiple levels of detail, including social role relationship models [ 10 ]. A conditional random field is proposed to model interactions between roles and person-specific social descriptors, and since social roles are described by interactions between people in an event, group behaviour recognition is performed through easy-to-process variational reasoning to simultaneously reason [ 11 , 12 ].…”

Section: Related Workmentioning

confidence: 99%

Behaviour Detection and Recognition of College Basketball Players Based on Multimodal Sequence Matching and Deep Neural Networks

Zhang

2022

Computational Intelligence and Neuroscience

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This study fuses multimodal sequence matching with a deep neural network algorithm for college basketball player behavior detection and recognition to conduct in-depth research and analysis, analyzing the basic components of basketball technical action videos by studying the practical application of technical actions in professional games and teaching videos from self-published authors of short video platforms. The characteristics of the dataset are also analyzed through literature research related to the basketball action dataset. On the established basketball technical action dataset, combined with the SSD target detection algorithm, the video images with cropping of human motion regions to reduce the size of image frames, generating a basketball technical action dataset based on cropped frames, reduces the amount of network training and improves the efficiency of subsequent action recognition training. In this study, by analyzing the characteristics of basic camera motion, a univariate global motion model is proposed to introduce a quadratic term to accurately express the shaking transformation, while the horizontal and vertical motion are independently represented to reduce the model complexity. Comparative experimental results show that the proposed model achieves a good balance between complexity and accuracy of global motion representation. It is suitable for global motion modeling in behavior recognition applications, laying the foundation for global and local motion estimation. On this basis, the visual feature change pattern of the key area of the scene (basketball area) is combined with the group behavior recognition based on motion patterns and the success-failure classification based on key visual information to achieve basketball semantic event recognition. The experimental results at NCAA show that the fusion of global and local motion patterns can effectively improve group behavior recognition performance. The semantic event recognition algorithm combining motion patterns and video key visual information achieves the best performance.

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Section: Related Workmentioning

confidence: 99%

Behaviour Detection and Recognition of College Basketball Players Based on Multimodal Sequence Matching and Deep Neural Networks

Zhang

2022

Computational Intelligence and Neuroscience

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“…The application of motion capture (MoCap) in sports provides a way to solve the above problems in teaching traditional Chinese sports in PE in Chinese universities. In the past 10 years, researchers have developed various systems for different sports on motion data captured by MoCap [6,7]. These systems measure the motions of users in real-time on MoCap, analyse the captured motion data, detect errors in the motions, and give feedback to the users to help them recognise and correct their motions.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Sequence-Based Classification Methods for Motion Assessment and Recognition in a Traditional Chinese Sport (Baduanjin)

Hai

Khoo

Yap

2022

IJERPH

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This study aimed to assess the motion accuracy of Baduanjin and recognise the motions of Baduanjin based on sequence-based methods. Motion data of Baduanjin were measured by the inertial sensor measurement system (IMU). Fifty-four participants were recruited to capture motion data. Based on the motion data, various sequence-based methods, namely dynamic time warping (DTW) combined with classifiers, hidden Markov model (HMM), and recurrent neural networks (RNNs), were applied to assess motion accuracy and recognise the motions of Baduanjin. To assess motion accuracy, the scores for motion accuracies from teachers were used as the standard to train the models on the different sequence-based methods. The effectiveness of Baduanjin motion recognition with different sequence-based methods was verified. Among the methods, DTW + k-NN had the highest average accuracy (83.03%) and shortest average processing time (3.810 s) during assessing. In terms of motion reorganisation, three methods (DTW + k-NN, DTW + SVM, and HMM) had the highest accuracies (over 99%), which were not significantly different from each other. However, the processing time of DTW + k-NN was the shortest (3.823 s) compared to the other two methods. The results show that the motions of Baduanjin could be recognised, and the accuracy can be assessed through an appropriate sequence-based method with the motion data captured by IMU.

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“…In recent years, motion capture (MoCap) has been applied in sports to monitor the motions of learners in real time. Researchers developed the system for specific sports items, using the motion data obtained by MoCap [6,7]. The system is able to check the motions and give the information of the errors in the motions in real time to the exercisers to assist them in detecting and correcting their inaccurate motions.…”

Section: Introductionmentioning

confidence: 99%

Motion Classification and Features Recognition of a Traditional Chinese Sport (Baduanjin) Using Sampled-Based Methods

Yap

Khoo

2021

Applied Sciences

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This study recognized the motions and assessed the motion accuracy of a traditional Chinese sport (Baduanjin), using the data from the inertial sensor measurement system (IMU) and sampled-based methods. Fifty-three participants were recruited in two batches to participate in the study. Motion data of participants practicing Baduanjin were captured by IMU. By extracting features from motion data and benchmarking with the teacher’s assessment of motion accuracy, this study verifies the effectiveness of assessment on different classifiers for motion accuracy of Baduanjin. Moreover, based on the extracted features, the effectiveness of Baduanjin motion recognition on different classifiers was verified. The k-Nearest Neighbor (k-NN), as a classifier, has advantages in accuracy (more than 85%) and a short average processing time (0.008 s) during assessment. In terms of recognizing motions, the classifier One-dimensional Convolutional Neural Network (1D-CNN) has the highest accuracy among all verified classifiers (99.74%). The results show, using the extracted features of the motion data captained by IMU, that selecting an appropriate classifier can effectively recognize the motions and, hence, assess the motion accuracy of Baduanjin.

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Skeleton-based comparison of throwing motion for handball players

Cited by 22 publications

References 47 publications

Behaviour Detection and Recognition of College Basketball Players Based on Multimodal Sequence Matching and Deep Neural Networks

Behaviour Detection and Recognition of College Basketball Players Based on Multimodal Sequence Matching and Deep Neural Networks

Implementation of Sequence-Based Classification Methods for Motion Assessment and Recognition in a Traditional Chinese Sport (Baduanjin)

Motion Classification and Features Recognition of a Traditional Chinese Sport (Baduanjin) Using Sampled-Based Methods

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