{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T01:33:34Z","timestamp":1775612014609,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T00:00:00Z","timestamp":1672185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Automatic key gesture detection and recognition are difficult tasks in Human\u2013Computer Interaction due to the need to spot the start and the end points of the gesture of interest. By integrating Hidden Markov Models (HMMs) and Deep Neural Networks (DNNs), the present research provides an autonomous technique that carries out hand gesture spotting and prediction simultaneously with no time delay. An HMM can be used to extract features, spot the meaning of gestures using a forward spotting mechanism with varying sliding window sizes, and then employ Deep Neural Networks to perform the recognition process. Therefore, a stochastic strategy for creating a non-gesture model using HMMs with no training data is suggested to accurately spot meaningful number gestures (0\u20139). The non-gesture model provides a confidence measure, which is utilized as an adaptive threshold to determine where meaningful gestures begin and stop in the input video stream. Furthermore, DNNs are extremely efficient and perform exceptionally well when it comes to real-time object detection. According to experimental results, the proposed method can successfully spot and predict significant motions with a reliability of 94.70%.<\/jats:p>","DOI":"10.3390\/informatics10010001","type":"journal-article","created":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T05:28:24Z","timestamp":1672205304000},"page":"1","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Forward Hand Gesture Spotting and Prediction Using HMM-DNN Model"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8750-1366","authenticated-orcid":false,"given":"Mahmoud","family":"Elmezain","sequence":"first","affiliation":[{"name":"Computer Science Department, Faculty of Science, Tanta University, Tanta 31527, Egypt"},{"name":"College of Computer Science and Engineering, Taibah University, Yanbu 966144, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6430-9806","authenticated-orcid":false,"given":"Majed M.","family":"Alwateer","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Taibah University, Yanbu 966144, Saudi Arabia"}]},{"given":"Rasha","family":"El-Agamy","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Science, Tanta University, Tanta 31527, Egypt"},{"name":"College of Computer Science and Engineering, Taibah University, Yanbu 966144, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4728-590X","authenticated-orcid":false,"given":"Elsayed","family":"Atlam","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Science, Tanta University, Tanta 31527, Egypt"},{"name":"College of Computer Science and Engineering, Taibah University, Yanbu 966144, Saudi Arabia"}]},{"given":"Hani M.","family":"Ibrahim","sequence":"additional","affiliation":[{"name":"Mathematics & Computer Science Department, Faculty of Science, Menoufiya University, Menoufia 32511, Egypt"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Wang, W., and Wang, Y. 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