Browsing by Author "Elmosalamy, Seif"

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    IPingPong: A Real-time Performance Analyzer System for Table Tennis Stroke’s Movements
    (Elsevier Ltd, 2020-01) Hegazy, Habiba; Abdelsalam, Mohamed; Hussien, Moustafa; Elmosalamy, Seif; M.I Hassan, Yomna; Nabil, Ayman M.; Atia, Ayman
    Assisting table tennis coaching using modern technologies is one of the most trending researches in the sports field. In this paper, we present a methodology to identify and recognize the wrong strokes executed by players to improve the training experience by the usage of an IR depth camera. The proposed system focuses mainly on the errors in table tennis player’s strokes and evaluating them efficiently and based on the analysis and classification of the data obtained from an IR depth camera using multiple algorithms. This paper is a continuation of our previous work [10], focusing more on identifying common wrong strokes in table tennis by utilizing IR depth camera classification algorithms. The classification of the mistakes that took place while playing can be classified based on each player dependently or independently for all players.
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    Online detection and classification of in-corrected played strokes in table tennis using IR depth camera
    (Elsevier B.V., 2020-05) Hegazy, Habiba; Abdelsalam, Mohamed; Hussien, Moustafa; Elmosalamy, Seif; IHassan, Yomna M.; Nabil, Ayman M
    Table tennis is a complex sport with a distinctive style of play. Due to the rising interest in this sport the past years, attempts have been targeted towards enhancing the training experience and quality through various techniques. Technology has been used to support training sessions for table tennis players before, with a focus on players’ performance measures rather than technique. In this paper, we propose a methodology based on IR depth camera for detecting and classifying the efficiency of strokes performed by players in order to enhance the training experience. Our system is to based on analyzing depth data collected from IR depth camera and recognized using fastDTW algorithm. The results show an average accuracy of 88% - 100%. This is the first paper to address the usage of IR depth camera on the table tennis player to detect and classify the strokes played