Abstract
In order to promote the practice of sports, several approaches using technology have been employed to gamify and augment the user experience. Following this trend, the research group proposed an approach to encourage the practice of Boccia, while promoting social inclusion and reducing the amount of time it takes for newcomers to the sport to become proficient by gaining knowledge of game tactics. The present work focus on the detection, in real-time, of Boccia gestures for the framework proposed in a previous work by using a wearable device to detect the gestures. To evaluate the correct functioning of the system, several types of tests were carried out. First, the developed machine learning model was evaluated in terms of accuracy, recall, among others. Then, the gesture detection system was tested with 15 participants that executed the different Boccia gestures while using the wearable placed on the wrist. Finally, tests were carried out to integrate the gesture detection module into the framework proposed in a previous work. The tests yielded positive results that allowed the validation of the use of the system in the Boccia game.
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References
World Health Organization: WORLD REPORT ON DISABILITY (2011)
Burchell, A.: The Importance of Sport to the Disabled. The Commonwealth Health Minister’s Book (2006)
Calado, A., Marcutti, S., Silva, V., Vercelli, G., Novais, P., Soares, F.: Towards a virtual coach for boccia: developing a virtual augmented interaction based on a boccia simulator. In: VISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2020). https://doi.org/10.5220/0009142602170224
Weinland, D., Ronfard, R., Boyer, E.: A survey of vision-based methods for action representation, segmentation and recognition. Comput. Vis. Image Underst. 115(2), 224–241 (2011). https://doi.org/10.1016/j.cviu.2010.10.002
Amendola, S., Bianchi, L., Marrocco, G.: Movement detection of human body segments: passive radio-frequency identification and machine-learning technologies. In: IEEE Antennas Propag Mag 57(3), 23–37 (2015). https://doi.org/10.1109/MAP.2015.2437274
Wilson, D., Wilson, A.: Gesture Recognition Using The XWand (2004). http://www.ri.cmu.edu/pub_files/pub4/wilson_daniel_h_2004_1/wilson_daniel_h_2004_1.pdf%5Cn. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.2780
Wu, J., Pan, G., Zhang, D., Qi, G.: Gesture recognition with a 3-d accelerometer. In: Proceedings of the 6th International Conference on Ubiquitous Intelligence and Computing, 5585, pp. 25–38 (2009). https://doi.org/10.1007/978-3-642-02830-4_4
Chen, Y., Luo, B., Chen, Y., Liang, G., Wu, X.: A Real-time Dynamic Hand Gesture Recognition System Using Kinect Sensor, pp. 2026–2030 (2015)
Ludl, D., Gulde, T., Curio, C.: Simple yet efficient real-time pose-based action recognition. In: 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 (2019). https://doi.org/10.1109/ITSC.2019.8917128
Pham, H.H., Khoudour, L., Crouzil, A., Zegers, P., Velastin, S.A.: Exploiting deep residual networks for human action recognition from skeletal data. Computer Vision and Image Understanding (2018). https://doi.org/10.1016/j.cviu.2018.03.003
Silva, V., Soares, F., Sena Esteves, J., Vercelli, G.: Human action recognition using an image-based temporal and spatial representation. In: 2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT, pp. 1–6 (2021). https://doi.org/10.1109/ICUMT51630.2020.9222408
Du, Y., Wang, W., Wang L.: Hierarchical recurrent neural network for skeleton based action recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2015). https://doi.org/10.1109/CVPR.2015.7298714
Li, C., Hou, Y., Wang, P., Li, W.: Joint distance maps based action recognition with convolutional neural networks. In: IEEE Signal Process Lett. 24(5), 624–628 (2017). https://doi.org/10.1109/LSP.2017.2678539
Song, S., Lan, C., Xing, J., Zeng, W., Liu, J.: An end-to-end spatio-temporal attention model for human action recognition from skeleton data. In: 31st AAAI Conference on Artificial Intelligence, AAAI 2017 (2017)
M5StickC ESP32-PICO Mini IoT Development Kit | m5stack-store. https://shop.m5stack.com/collections/m5-controllers/products/stick-c. Accessed 27 Feb 2023
Calado, A., Silva, V., Soares, F., Novais, P.: Ball detection for boccia game analysis. In: 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), pp. 1468–1473 (2019)
Lin, M., Chen, Q., Yan, S.: Network in network. In: 2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings (2014)
OpenCV: Deep Neural Networks (dnn module) (2020). https://docs.opencv.org/master/d2/d58/tutorial_table_of_content_dnn.html. Accessed 8 Apr 2020
Acknowledgments
The authors would like to thank the volunteers who helped test the developed system. This work has been supported by national funds through FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.
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Cruz, J., Silva, V., Esteves, J.S., Soares, F. (2023). An Augmented System Based on Machine Learning for Boccia Assisted Gameplay. In: Burduk, A., Batako, A., Machado, J., Wyczółkowski, R., Antosz, K., Gola, A. (eds) Advances in Production. ISPEM 2023. Lecture Notes in Networks and Systems, vol 790. Springer, Cham. https://doi.org/10.1007/978-3-031-45021-1_20
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