Online detection and classification of in-corrected played strokes in table tennis using IR depth camera
| dc.Affiliation | October University for modern sciences and Arts (MSA) | |
| dc.contributor.author | Hegazy, Habiba | |
| dc.contributor.author | Abdelsalam, Mohamed | |
| dc.contributor.author | Hussien, Moustafa | |
| dc.contributor.author | Elmosalamy, Seif | |
| dc.contributor.author | IHassan, Yomna M. | |
| dc.contributor.author | Nabil, Ayman M | |
| dc.date.accessioned | 2020-06-05T12:27:21Z | |
| dc.date.available | 2020-06-05T12:27:21Z | |
| dc.date.issued | 2020-05 | |
| dc.description | Scopus | en_US |
| dc.description.abstract | 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 | en_US |
| dc.description.sponsorship | 11th International Conference on Ambient Systems, Networks and Technologies, ANT 2020 / 3rd International Conference on Emerging Data and Industry 4.0, EDI40 2020 / Affiliated Workshops; Warsaw; Poland; 6 April 2020 through 9 April 2020; Code 159934 | en_US |
| dc.description.uri | https://www.scimagojr.com/journalsearch.php?q=19700182801&tip=sid&clean=0 | |
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| dc.identifier.doi | https://doi.org/10.1016/j.procs.2020.03.125 | |
| dc.identifier.issn | 18770509 | |
| dc.identifier.other | https://doi.org/10.1016/j.procs.2020.03.125 | |
| dc.identifier.uri | https://t.ly/kH4g | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Elsevier B.V. | en_US |
| dc.relation.ispartofseries | Procedia Computer Science;Volume 170, 2020, Pages 555-562 | |
| dc.subject | Table tennis | en_US |
| dc.subject | stroke detection | en_US |
| dc.subject | stroke classification | en_US |
| dc.subject | hand gestures | en_US |
| dc.subject | IR depth camera | en_US |
| dc.title | Online detection and classification of in-corrected played strokes in table tennis using IR depth camera | en_US |
| dc.type | Article | en_US |