{"id":3399,"date":"2025-11-19T13:44:12","date_gmt":"2025-11-19T12:44:12","guid":{"rendered":"https:\/\/www.asm.tf.fau.de\/?p=3399"},"modified":"2025-11-19T13:57:07","modified_gmt":"2025-11-19T12:57:07","slug":"joint-reconstruction-from-upper-limb-motion-capture-markers-data","status":"publish","type":"post","link":"https:\/\/www.asm.tf.fau.de\/en\/2025\/11\/19\/joint-reconstruction-from-upper-limb-motion-capture-markers-data\/","title":{"rendered":"Joint Reconstruction from Upper-Limb Motion-Capture Markers Data"},"content":{"rendered":"<p>Upper-limb motion is mechanically complex, yet the nervous system performs these movements with effortless coordination. To understand this behavior and enable human-like control in robotics or neurorehabilitation, upper-limb movement datasets are essential. One of the interesting dataset available with extensive modalities is the U-Limb dataset. It consists of a database collected across six European universities, including healthy and post-stroke participants. It contains detailed upper-limb motion-capture marker trajectories, EMG recordings, EEG and additional sensor modalities. However, the raw motion-capture markers alone do not directly provide anatomical joint positions or orientations. A processing pipeline is required to interpret the marker cluster positions and reconstruct meaningful joint kinematics enabling further analysis.<\/p>\n<p>This is an ongoing project, no applications are accepted for it at the moment.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Upper-limb motion is mechanically complex, yet the nervous system performs these movements with effortless coordination. To understand this behavior and enable human-like control in robotics or neurorehabilitation, upper-limb movement datasets are essential. One of the interesting dataset available with extensive modalities is the U-Limb dataset. It consists of a database collected across six European universities, [&hellip;]<\/p>\n","protected":false},"author":5581,"featured_media":3407,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_rrze_cache":"enabled","_rrze_multilang_single_locale":"en_US","_rrze_multilang_single_source":"https:\/\/www.asm.tf.fau.de\/?p=3396","footnotes":""},"categories":[75],"tags":[],"workflow_usergroup":[],"class_list":["post-3399","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ongoing-project","en-US"],"_links":{"self":[{"href":"https:\/\/www.asm.tf.fau.de\/wp-json\/wp\/v2\/posts\/3399","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.asm.tf.fau.de\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.asm.tf.fau.de\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.asm.tf.fau.de\/wp-json\/wp\/v2\/users\/5581"}],"replies":[{"embeddable":true,"href":"https:\/\/www.asm.tf.fau.de\/wp-json\/wp\/v2\/comments?post=3399"}],"version-history":[{"count":3,"href":"https:\/\/www.asm.tf.fau.de\/wp-json\/wp\/v2\/posts\/3399\/revisions"}],"predecessor-version":[{"id":3406,"href":"https:\/\/www.asm.tf.fau.de\/wp-json\/wp\/v2\/posts\/3399\/revisions\/3406"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.asm.tf.fau.de\/wp-json\/wp\/v2\/media\/3407"}],"wp:attachment":[{"href":"https:\/\/www.asm.tf.fau.de\/wp-json\/wp\/v2\/media?parent=3399"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.asm.tf.fau.de\/wp-json\/wp\/v2\/categories?post=3399"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.asm.tf.fau.de\/wp-json\/wp\/v2\/tags?post=3399"},{"taxonomy":"workflow_usergroup","embeddable":true,"href":"https:\/\/www.asm.tf.fau.de\/wp-json\/wp\/v2\/workflow_usergroup?post=3399"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}