<> "The repository administrator has not yet configured an RDF license."^^ . <> . . "Classification of Human Motion with Applications in Gesture Recognition and Treatment of Major Depressive Disorder"^^ . "In this thesis we are investigating the question of classification of human motion from three different perspectives:\r\nFirstly, we develop a methodology that allows the assessment of human muscle signatures to differentiate between gestures they originate from under consideration of natural shifts of those signatures in time with each motion.\r\nWe can show that our proposed method can leverage Dynamic Time Warping to successfully classify hand gestures based on muscle signals with an accuracy corresponding to or exceeding the current state of the art.\r\nIn addition to that, we can show that our proposed method allows insight into the underlying mechanisms this classification results are based on and why they are so accurate.\r\nWe can show that this insight allows for the derivation of intelligent improvements and modifications to the method to improve accuracy even further or to adapt it to specific circumstances.\r\nSecondly, we investigate the automated, but understandable derivation of classification procedures for the differentiation between motor patterns inside and outside of intervals of stressful rumination for patients diagnosed with major depressive disorder.\r\nWe can not only demonstrate that an automated tailoring of a classification procedure is possible, but also that an appropriate splitting between parts of the methodology allows for an assessment of impact for each involved component, yielding the option of intelligent choice for the underlying problem.\r\nAs a consequence, we can propose a method that is tailored specifically to the underlying structure of the foundational patient data that was obtained within the project and is capable of yielding a lightweight, optimized classification procedure that is also suited for medical applications due to its understandability.\r\nThirdly, we investigate the question of optimal sensor location, to derive an optimal sensor layout for specific or comparable applications under consideration of application-specific constraints and requirements.\r\nWe propose a method based on an exemplary reconstruction problem that allows the automated assessment of quality of a specific sensor layout.\r\nThis assessment is then shown to be useful as a basis on which an automated procedure can derive the optimal sensor layout for a specific application.\r\nIn addition to that, we can show that this method is capable to quantify the sensitivity of the underlying problem to properties of the sensor hardware in use, allowing for a quantitative assessment of the necessity of constraints on the sensor hardware used."^^ . "2022" . . . . . . . "Jonas Christopher"^^ . "Große Sundrup"^^ . "Jonas Christopher Große Sundrup"^^ . . . . . . "Classification of Human Motion with Applications in Gesture Recognition and Treatment of Major Depressive Disorder (PDF)"^^ . . . "dissertation_jonas_grosse_sundrup_digital.pdf"^^ . . . "Classification of Human Motion with Applications in Gesture Recognition and Treatment of Major Depressive Disorder (Other)"^^ . . . . . . "indexcodes.txt"^^ . . . "Classification of Human Motion with Applications in Gesture Recognition and Treatment of Major Depressive Disorder (Other)"^^ . . . . . . "lightbox.jpg"^^ . . . "Classification of Human Motion with Applications in Gesture Recognition and Treatment of Major Depressive Disorder (Other)"^^ . . . . . . "preview.jpg"^^ . . . "Classification of Human Motion with Applications in Gesture Recognition and Treatment of Major Depressive Disorder (Other)"^^ . . . . . . "medium.jpg"^^ . . . "Classification of Human Motion with Applications in Gesture Recognition and Treatment of Major Depressive Disorder (Other)"^^ . . . . . . "small.jpg"^^ . . "HTML Summary of #32550 \n\nClassification of Human Motion with Applications in Gesture Recognition and Treatment of Major Depressive Disorder\n\n" . "text/html" . .