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The proposed approach" /> <meta property="article:author" content="https://gadz.academia.edu/FakhreddineAbabsa" /> <meta name="description" content="This paper addresses two problems: 3d dynamic gesture recognition and gesture misallocation. In order to solve these problems, we propose a new approach which combines Hidden Markov Models (HMM) and Dynamic Time Warping (DTW). The proposed approach" /> <title>(PDF) Dynamic Gesture Recognition for Natural Human System Interaction | Fakhreddine Ababsa - Academia.edu</title> <link rel="canonical" href="https://www.academia.edu/111609808/Dynamic_Gesture_Recognition_for_Natural_Human_System_Interaction" /> <script async src="https://www.googletagmanager.com/gtag/js?id=G-5VKX33P2DS"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-5VKX33P2DS', { cookie_domain: 'academia.edu', send_page_view: false, }); gtag('event', 'page_view', { 'controller': "single_work", 'action': "show", 'controller_action': 'single_work#show', 'logged_in': 'false', 'edge': 'unknown', // Send nil if there is no A/B test bucket, in case some records get logged // with missing data - that way we can distinguish between the two cases. // ab_test_bucket should be of the form <ab_test_name>:<bucket> 'ab_test_bucket': null, }) </script> <script> var $controller_name = 'single_work'; var $action_name = "show"; var $rails_env = 'production'; var $app_rev = '93b7474171e6a4b1bbf2c2315cd7570fbc58ca00'; var $domain = 'academia.edu'; var $app_host = "academia.edu"; var $asset_host = "academia-assets.com"; var $start_time = new Date().getTime(); var $recaptcha_key = "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB"; var $recaptcha_invisible_key = "6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj"; var $disableClientRecordHit = false; </script> <script> window.require = { config: function() { return function() {} } } </script> <script> window.Aedu = window.Aedu || {}; window.Aedu.hit_data = null; window.Aedu.serverRenderTime = new Date(1733267907000); window.Aedu.timeDifference = new Date().getTime() - 1733267907000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"This paper addresses two problems: 3d dynamic gesture recognition and gesture misallocation. In order to solve these problems, we propose a new approach which combines Hidden Markov Models (HMM) and Dynamic Time Warping (DTW). The proposed approach has two main phases; first, recognizing gestures using a hidden Markov model. Second, avoiding misallocation by rejecting gestures based on a threshold computed using DTW. Our database includes many samples of five gestures obtained with a Kinect and described by depth information only. The results show that our approach yields good gesture classification without any misallocation and it is robust against environmental constraints.","author":[{"@context":"https://schema.org","@type":"Person","name":"Fakhreddine Ababsa"}],"contributor":[],"dateCreated":"2023-12-16","datePublished":"2016-01-01","headline":"Dynamic Gesture Recognition for Natural Human System Interaction","image":"https://attachments.academia-assets.com/109098683/thumbnails/1.jpg","inLanguage":"en","keywords":["Computer Science","Artificial Intelligence","Human Computer Interaction","Gesture","Gesture Recognition","Kinect","hidden Markov model","Dynamic Time Warping","Archaeology of Natural Places","dynamic gesture 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window.loswp.shouldShowBulkDownload = true; window.loswp.showSignupCaptcha = false window.loswp.willEdgeCache = false; window.loswp.work = {"work":{"id":111609808,"created_at":"2023-12-16T23:55:56.906-08:00","from_world_paper_id":245980105,"updated_at":"2023-12-17T00:22:03.427-08:00","_data":{"abstract":"This paper addresses two problems: 3d dynamic gesture recognition and gesture misallocation. In order to solve these problems, we propose a new approach which combines Hidden Markov Models (HMM) and Dynamic Time Warping (DTW). The proposed approach has two main phases; first, recognizing gestures using a hidden Markov model. Second, avoiding misallocation by rejecting gestures based on a threshold computed using DTW. Our database includes many samples of five gestures obtained with a Kinect and described by depth information only. The results show that our approach yields good gesture classification without any misallocation and it is robust against environmental constraints.","publication_date":"2016,,"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Dynamic Gesture Recognition for Natural Human System Interaction","broadcastable":false,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [263837202]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; window.loswp.useOptimizedScribd4genScript = false; window.loswp.appleClientId = 'edu.academia.applesignon';</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":109098683,"attachmentType":"pdf"}"><img alt="First page of “Dynamic Gesture Recognition for Natural Human System Interaction”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/109098683/mini_magick20231217-1-sc3rnj.png?1702800183" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/assets/single_work_splash/adobe.icon-574afd46eb6b03a77a153a647fb47e30546f9215c0ee6a25df597a779717f9ef.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">Dynamic Gesture Recognition for Natural Human System Interaction</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="263837202" href="https://gadz.academia.edu/FakhreddineAbabsa"><img alt="Profile image of Fakhreddine Ababsa" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/263837202/115339392/104625007/s65_fakhreddine.ababsa.png" />Fakhreddine Ababsa</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2016</p></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">This paper addresses two problems: 3d dynamic gesture recognition and gesture misallocation. In order to solve these problems, we propose a new approach which combines Hidden Markov Models (HMM) and Dynamic Time Warping (DTW). The proposed approach has two main phases; first, recognizing gestures using a hidden Markov model. Second, avoiding misallocation by rejecting gestures based on a threshold computed using DTW. Our database includes many samples of five gestures obtained with a Kinect and described by depth information only. The results show that our approach yields good gesture classification without any misallocation and it is robust against environmental constraints.</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":109098683,"attachmentType":"pdf","workUrl":"https://www.academia.edu/111609808/Dynamic_Gesture_Recognition_for_Natural_Human_System_Interaction"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":109098683,"attachmentType":"pdf","workUrl":"https://www.academia.edu/111609808/Dynamic_Gesture_Recognition_for_Natural_Human_System_Interaction"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div></div><div data-auto_select="false" data-client_id="331998490334-rsn3chp12mbkiqhl6e7lu2q0mlbu0f1b" data-doc_id="109098683" data-landing_url="https://www.academia.edu/111609808/Dynamic_Gesture_Recognition_for_Natural_Human_System_Interaction" data-login_uri="https://www.academia.edu/registrations/google_one_tap" data-moment_callback="onGoogleOneTapEvent" id="g_id_onload"></div><div class="ds-top-related-works--grid-container"><div class="ds-related-content--container ds-top-related-works--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="0" data-entity-id="2844368" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/2844368/Probability_based_Dynamic_Time_Warping_for_Gesture_Recognition_on_RGB_D_data">Probability-based Dynamic Time Warping for Gesture Recognition on RGB-D data</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="181897" href="https://upc.academia.edu/CecilioAngulo">Cecilio Angulo</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Abstract. 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data-collection-position="7" data-entity-id="9800765" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/9800765/Robust_Gesture_Recognition_Using_Feature_Pre_Processing_and_Weighted_Dynamic_Time_Warping">Robust Gesture Recognition Using Feature Pre-Processing and Weighted Dynamic Time Warping</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="659521" href="https://sehir.academia.edu/AliSelmanAyd%C4%B1n">Ali Selman Aydın</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Gesture recognition is a technology often used in human-computer interaction applications. Dynamic time warping (DTW) is one of the techniques used in gesture recognition to find an optimal alignment between two sequences. Oftentimes a pre-processing of sequences is required to remove variations due to different camera or body orientations or due to different skeleton sizes between the reference gesture sequences and the test gesture sequences. We discuss a set of pre-processing methods to make the gesture recognition mechanism robust to these variations. DTW computes a dissimilarity measure by time-warping the sequences on a per sample basis by using the distance between the current reference and test sequences. However, all body joints involved in a gesture are not equally important in computing the distance between two sequence samples. We propose a weighted DTW method that weights joints by optimizing a discriminant ratio. 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