{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T20:11:54Z","timestamp":1771877514843,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,18]],"date-time":"2021-12-18T00:00:00Z","timestamp":1639785600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recently, several computer applications provided operating mode through pointing fingers, waving hands, and with body movement instead of a mouse, keyboard, audio, or touch input such as sign language recognition, robot control, games, appliances control, and smart surveillance. With the increase of hand-pose-based applications, new challenges in this domain have also emerged. Support vector machines and neural networks have been extensively used in this domain using conventional RGB data, which are not very effective for adequate performance. Recently, depth data have become popular due to better understating of posture attributes. In this study, a multiple parallel stream 2D CNN (two-dimensional convolution neural network) model is proposed to recognize the hand postures. The proposed model comprises multiple steps and layers to detect hand poses from image maps obtained from depth data. The hyper parameters of the proposed model are tuned through experimental analysis. Three publicly available benchmark datasets: Kaggle, First Person, and Dexter, are used independently to train and test the proposed approach. The accuracy of the proposed method is 99.99%, 99.48%, and 98% using the Kaggle hand posture dataset, First Person hand posture dataset, and Dexter dataset, respectively. Further, the results obtained for F1 and AUC scores are also near-optimal. Comparative analysis with state-of-the-art shows that the proposed model outperforms the previous methods.<\/jats:p>","DOI":"10.3390\/s21248469","type":"journal-article","created":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T02:40:32Z","timestamp":1639968032000},"page":"8469","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Hand Pose Recognition Using Parallel Multi Stream CNN"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4683-4839","authenticated-orcid":false,"given":"Iram","family":"Noreen","sequence":"first","affiliation":[{"name":"Department of Computer Science, Lahore Campus, Bahria University, Islamabad 54000, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2440-6596","authenticated-orcid":false,"given":"Muhammad","family":"Hamid","sequence":"additional","affiliation":[{"name":"Department of Statistics and Computer Science, University of Veterinary and Animal Sciences (UVAS), Lahore 54000, Pakistan"}]},{"given":"Uzma","family":"Akram","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Lahore Campus, Bahria University, Islamabad 54000, Pakistan"}]},{"given":"Saadia","family":"Malik","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Faculty of Computing and Information Technology-Rabigh, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7283-0036","authenticated-orcid":false,"given":"Muhammad","family":"Saleem","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1109\/TSMCA.2010.2093883","article-title":"Wearable sensor-based hand gesture and daily activity recognition for robot-assisted living","volume":"41","author":"Zhu","year":"2011","journal-title":"IEEE Trans. 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