{"id":4,"date":"2011-12-08T11:55:34","date_gmt":"2011-12-08T11:55:34","guid":{"rendered":"http:\/\/project.inria.fr\/template1\/?page_id=4"},"modified":"2016-06-09T18:29:32","modified_gmt":"2016-06-09T16:29:32","slug":"home","status":"publish","type":"page","link":"https:\/\/project.inria.fr\/persobalance\/","title":{"rendered":"Presentation"},"content":{"rendered":"<p><a href=\"http:\/\/project.inria.fr\/persobalance\/files\/2011\/12\/PersoBalance.png\"><img loading=\"lazy\" decoding=\"async\" class=\" size-medium wp-image-71 alignright\" src=\"http:\/\/project.inria.fr\/persobalance\/files\/2011\/12\/PersoBalance-300x81.png\" alt=\"PersoBalance\" width=\"300\" height=\"81\" srcset=\"https:\/\/project.inria.fr\/persobalance\/files\/2011\/12\/PersoBalance-300x81.png 300w, https:\/\/project.inria.fr\/persobalance\/files\/2011\/12\/PersoBalance-250x68.png 250w, https:\/\/project.inria.fr\/persobalance\/files\/2011\/12\/PersoBalance-150x41.png 150w, https:\/\/project.inria.fr\/persobalance\/files\/2011\/12\/PersoBalance.png 377w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-80  alignnone\" src=\"http:\/\/project.inria.fr\/persobalance\/files\/2011\/12\/PersoBalancePage.ai_-1024x585.jpg\" alt=\"PersoBalancePage.ai\" width=\"393\" height=\"316\" \/><\/p>\n<h1>Latest News<\/h1>\n<h2>June 1st 2016: 1.0 is finished !<\/h2>\n<p>Right on time, it features:<\/p>\n<ul>\n<li>Kinect2 support<\/li>\n<li>Much improved overall performance\u00a0(real-time identification\/stability analysis)<\/li>\n<li>Auto-registration of the force-plate during identification<\/li>\n<li>Improved identification phase\u00a0with convexity\u00a0constraints, optional symmetry constraints and much more<\/li>\n<li>Automatic support polygon computation during stability phase<\/li>\n<li>Easily extensible through python<\/li>\n<\/ul>\n<p>We won&#8217;t release any code\/binary at this stage, but a technical paper will be published soon. Scroll down for\u00a0two videos showcasing the current build.<\/p>\n<h1>Model Identification<\/h1>\n<p>Here is a live video capture of our software, in which we estimate a subject mass distribution using low-cost devices (here, a Nintendo Wii BalanceBoard and a Microsoft Kinect2):<\/p>\n<p><iframe loading=\"lazy\" width=\"900\" height=\"506\" src=\"https:\/\/www.youtube.com\/embed\/ut2WKM0PCWk?feature=oembed\" frameborder=\"0\" allowfullscreen><\/iframe><\/p>\n<p>The skeleton parts turn green as more information is gathered. The estimated Center of Mass (CoM) is displayed as a blue sphere.<\/p>\n<p>The force plate position and orientation are also estimated (white frame), and one can observe the difference between the measured CoM projection (red spot) and estimated (small blue spot). The error steadily decreases over time.<\/p>\n<h1>Dynamic Stability<\/h1>\n<p>Here is a live video capture from our software, in which we estimate a subject dynamic stability from low-cost capture devices (here a Microsoft Kinect2):<\/p>\n<p><iframe loading=\"lazy\" width=\"900\" height=\"675\" src=\"https:\/\/www.youtube.com\/embed\/stUQdW5s3ss?feature=oembed\" frameborder=\"0\" allowfullscreen><\/iframe><\/p>\n<p>After the subject mass distribution has been identified (see <a href=\"https:\/\/youtu.be\/ut2WKM0PCWk\">here<\/a>), we compute the ground forces from the Center of Mass (CoM, here in blue) data. We derive a stability index from the position of the Zero Rate of Angular Momentum (ZRAM, here in pink) point relative to the support polygon (red). The skeleton color changes according to the stability index (green = stable, red = unstable).<\/p>","protected":false},"excerpt":{"rendered":"<p>Latest News June 1st 2016: 1.0 is finished ! Right on time, it features: Kinect2 support Much improved overall performance\u00a0(real-time identification\/stability analysis) Auto-registration of the force-plate during identification Improved identification phase\u00a0with convexity\u00a0constraints, optional symmetry constraints and much more Automatic support polygon computation during stability phase Easily extensible through python We\u2026<\/p>\n<p> <a class=\"continue-reading-link\" href=\"https:\/\/project.inria.fr\/persobalance\/\"><span>Continue reading<\/span><i class=\"crycon-right-dir\"><\/i><\/a> <\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"open","template":"","meta":{"footnotes":""},"class_list":["post-4","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/project.inria.fr\/persobalance\/wp-json\/wp\/v2\/pages\/4","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/project.inria.fr\/persobalance\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/project.inria.fr\/persobalance\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/persobalance\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/persobalance\/wp-json\/wp\/v2\/comments?post=4"}],"version-history":[{"count":17,"href":"https:\/\/project.inria.fr\/persobalance\/wp-json\/wp\/v2\/pages\/4\/revisions"}],"predecessor-version":[{"id":86,"href":"https:\/\/project.inria.fr\/persobalance\/wp-json\/wp\/v2\/pages\/4\/revisions\/86"}],"wp:attachment":[{"href":"https:\/\/project.inria.fr\/persobalance\/wp-json\/wp\/v2\/media?parent=4"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}