{"id":3896,"date":"2026-07-01T10:21:52","date_gmt":"2026-07-01T08:21:52","guid":{"rendered":"https:\/\/www.asm.tf.fau.de\/?p=3896"},"modified":"2026-07-21T16:13:40","modified_gmt":"2026-07-21T14:13:40","slug":"emg-control-of-a-robotic-hand-based-on-kinematic-and-muscle-synergies","status":"publish","type":"post","link":"https:\/\/www.asm.tf.fau.de\/en\/2026\/07\/01\/emg-control-of-a-robotic-hand-based-on-kinematic-and-muscle-synergies\/","title":{"rendered":"EMG control of a robotic hand based on kinematic and muscle synergies"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Human hand control involves many biomechanical degrees of freedom, making direct EMG-based control of robotic and prosthetic hands difficult. Previous work has therefore investigated continuous decoding of hand and finger kinematics from surface EMG signals using regression models and neural networks [1]. In parallel, research on muscle and kinematic synergies has shown that hand movements can be represented in a low-dimensional latent space rather than through independent control of each joint. Early studies by Santello et al. demonstrated that natural grasp postures can be reconstructed using only a few principal components [2]. More recent work combines EMG decoding with latent-space and deep-learning approaches to enable smoother and more biologically plausible continuous prosthetic hand control [3].<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Building on these developments, this thesis investigates continuous EMG-driven robotic hand control through a low-dimensional synergy representation.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Project goals<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Conduct a literature review on continuous robotic hand control<\/li>\n\n\n\n<li>Design a pipeline which reconstructs finger joint angles from forearm emg signals using muscle and kinematic synergies<\/li>\n\n\n\n<li>Implement a controller for a robotic hand that uses the developed pipeline and produces realistic motion<\/li>\n\n\n\n<li>Test in simulation and on real robotic hand and evaluate motion reconstruction accuracy<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Requirements<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Student of Medical Engineering, Mechatronics, Autonomy Technology, Electrical Engineering or Computational Engineering<\/li>\n\n\n\n<li>Programming skills in Python<\/li>\n\n\n\n<li>Understanding of robotic hand kinematics and biosignal processing<\/li>\n\n\n\n<li>Interest in robotic hands and human motor control<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">References<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">[1] R. J. Smith <em>et al.<\/em>, \u201cContinuous decoding of finger position from surface EMG signals for the control of powered prostheses,\u201d <em>Proc. IEEE EMBS<\/em>, pp. 197\u2013200, 2008.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">[2] M. Santello, M. Flanders, and J. F. Soechting, \u201cPostural hand synergies for tool use,\u201d <em>Journal of Neuroscience<\/em>, vol. 18, no. 23, pp. 10105\u201310115, 1998.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">[3] E. Krasoulis, S. Vijayakumar, and K. Nazarpour, \u201cEffect of user practice on prosthetic finger control with an intuitive myoelectric decoder,\u201d <em>Frontiers in Neuroscience<\/em>, vol. 13, 2019.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Contact<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">marius.kindermann@fau.de<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Human hand control involves many biomechanical degrees of freedom, making direct EMG-based control of robotic and prosthetic hands difficult. Previous work has therefore investigated continuous decoding of hand and finger kinematics from surface EMG signals using regression models and neural networks [1]. In parallel, research on muscle and kinematic synergies has shown that hand movements [&hellip;]<\/p>\n","protected":false},"author":5581,"featured_media":0,"comment_status":"open","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=3895","_faue_teaser_image_id":0,"footnotes":""},"categories":[1,75],"tags":[],"workflow_usergroup":[],"class_list":["post-3896","post","type-post","status-publish","format-standard","hentry","category-allgemein","category-ongoing-project","en-US"],"faue_teaser_image_url":"","_links":{"self":[{"href":"https:\/\/www.asm.tf.fau.de\/wp-json\/wp\/v2\/posts\/3896","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=3896"}],"version-history":[{"count":1,"href":"https:\/\/www.asm.tf.fau.de\/wp-json\/wp\/v2\/posts\/3896\/revisions"}],"predecessor-version":[{"id":3898,"href":"https:\/\/www.asm.tf.fau.de\/wp-json\/wp\/v2\/posts\/3896\/revisions\/3898"}],"wp:attachment":[{"href":"https:\/\/www.asm.tf.fau.de\/wp-json\/wp\/v2\/media?parent=3896"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.asm.tf.fau.de\/wp-json\/wp\/v2\/categories?post=3896"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.asm.tf.fau.de\/wp-json\/wp\/v2\/tags?post=3896"},{"taxonomy":"workflow_usergroup","embeddable":true,"href":"https:\/\/www.asm.tf.fau.de\/wp-json\/wp\/v2\/workflow_usergroup?post=3896"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}