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Improved front-view tracking of human skeleton from Kinect data for rehabilitation support in Multiple Sclerosis

dc.creatorSosa, German D.spa
dc.creatorSanchez-Romero, Juanita-Irina
dc.creatorFranco, Hugospa
dc.description.abstractMultiple Sclerosis (MS) is an auto-immune, inflammatory disease of the Central Nervous System (CNS), consisting in the progressive demyelinization of axonal fibers. Given its degenerative nature, MS treatment faces complex challenges for both pharmaceutical and therapeutic interventions. Indeed, patients with diagnostic of multiple sclerosis will require continuous rehabilitation for life in order to reduce the progression rate of the disease. However, to assess the actual impact of the particular therapeutic intervention is a difficult process with important implications in the choice of a treatment pathway. Objective measurements (response time, velocity, range of motion -ROM-, etc.) of patient performance could enhance the patient state information available for therapeutic decision making, in a continuous evaluation scenario. This work presents a first step in the design of a low-cost computer-vision based framework (using KinectTM) for the objective measurement of multiple sclerosis patient functional performance1. A real-time front-view detection of human skeleton was implemented to track patient motion and measure the ROM for neck, shoulder, elbow, hip and knee joints. Preliminary measurements on four control subjects (two males, two females) were performed and the system accuracy was evaluated against traditional static diagnostic measurements (goniometer). © 2015 IEEE.eng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.spa
dc.relation.citationTitle2015 20th Symposium on Signal Processing Images and Computer Vision STSIVA 2015 - Conference Proceedings
dc.relation.ispartof2015 20th Symposium on Signal Processing, Images and Computer Vision, STSIVA 2015 - Conference Proceedings, ISSN:2015,(2015)spa
dc.rights.accesoAbierto (Texto Completo)spa
dc.source.instnameinstname:Universidad del Rosariospa
dc.source.reponamereponame:Repositorio Institucional EdocURspa
dc.subject.keywordComputer visionspa
dc.subject.keywordDecision makingspa
dc.subject.keywordImage processingspa
dc.subject.keywordJoints (anatomy)spa
dc.subject.keywordMedical computingspa
dc.subject.keywordMusculoskeletal systemspa
dc.subject.keywordSignal processingspa
dc.subject.keywordCentral nervous systemsspa
dc.subject.keywordDiagnostic measurementsspa
dc.subject.keywordInflammatory diseasespa
dc.subject.keywordMultiple sclerosisspa
dc.subject.keywordObjective measurementspa
dc.subject.keywordRange of motionsspa
dc.subject.keywordSystem accuracyspa
dc.subject.keywordTherapeutic interventionspa
dc.subject.keywordPatient rehabilitationspa
dc.titleImproved front-view tracking of human skeleton from Kinect data for rehabilitation support in Multiple Sclerosisspa
dc.type.spaDocumento de conferenciaspa