{"id":{"repo_id":"njit","oai_identifier":"oai:digitalcommons.njit.edu:theses-1131"},"canonical_url":"https://search.dev.ndltd.org/etd/njit/oai:digitalcommons.njit.edu:theses-1131","repository":{"repo_id":"njit","name":"NJIT","base_url":"https://digitalcommons.njit.edu/do/oai/"},"display":{"title":"Quantifying prehension in persons with stroke post rehabilitation","abstract":"This study describes the analysis of reaching and grasping abilities of the hemiparetic arm and hand of patients post stroke after a series of interactive virtual reality (VR) simulated training sessions and conventional physical therapy of similar intensity. Six subjects participated in VR training and five subjects in clinical rehabilitation for two weeks. Subjects' finger joint angles were measured during a kinematic reach to grasp test using CyberGlove and arm joint angles were measured using the trackSTARTM system prior to training and after training. Downward force applied to the object during grasping was assessed using Nano17TM, a force/torque sensor system that is added to the reach to grasp test paradigm for the VR trained subjects. Results from total movement time, grasping time, and average applied force show that subjects significantly decreased their average kinematic times and force applied to object during reaching and grasping tasks. Classification of hand postures using Linear Discriminant Analysis (LDA) during the reaching phase of movement shows an improvement in subjects' accuracies and abilities to preshape their fingers post training in both groups. A system utilizing magnetic trackers, a data glove, and a force sensor is sensitive to changes in motor performance elicited by a robotically facilitated, virtually simulated motor intervention and physical therapy of similar intensity.","abstract_html":"This study describes the analysis of reaching and grasping abilities of the hemiparetic arm and hand of patients post stroke after a series of interactive virtual reality (VR) simulated training sessions and conventional physical therapy of similar intensity. Six subjects participated in VR training and five subjects in clinical rehabilitation for two weeks. Subjects' finger joint angles were measured during a kinematic reach to grasp test using CyberGlove and arm joint angles were measured using the trackSTARTM system prior to training and after training. Downward force applied to the object during grasping was assessed using Nano17TM, a force/torque sensor system that is added to the reach to grasp test paradigm for the VR trained subjects. Results from total movement time, grasping time, and average applied force show that subjects significantly decreased their average kinematic times and force applied to object during reaching and grasping tasks. Classification of hand postures using Linear Discriminant Analysis (LDA) during the reaching phase of movement shows an improvement in subjects' accuracies and abilities to preshape their fingers post training in both groups. A system utilizing magnetic trackers, a data glove, and a force sensor is sensitive to changes in motor performance elicited by a robotically facilitated, virtually simulated motor intervention and physical therapy of similar intensity.","abstract_has_math":false,"creators":["Puthenveettil, Saumya Sara"],"institution":null,"degree_name":"Master of Science in Biomedical Engineering - (M.S.)","degree_level":null,"degree_discipline":"Biomedical Engineering","degree_department":null,"school":null,"contributors":["Sergei Adamovich","Richard A. Foulds","Max Roman"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012-05-31T07:00:00Z","date_published":"2012-05-31T07:00:00Z","updated_at":"2026-07-24T03:22:26Z","subjects":["Prehension","Virtual reality simulated training","Physical therapy","Stroke rehabilitation","Biomedical Engineering and Bioengineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.njit.edu/theses/132","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sergei Adamovich","Richard A. 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Six subjects participated in VR training and five subjects in clinical rehabilitation for two weeks. Subjects' finger joint angles were measured during a kinematic reach to grasp test using CyberGlove and arm joint angles were measured using the trackSTARTM system prior to training and after training. Downward force applied to the object during grasping was assessed using Nano17TM, a force/torque sensor system that is added to the reach to grasp test paradigm for the VR trained subjects. Results from total movement time, grasping time, and average applied force show that subjects significantly decreased their average kinematic times and force applied to object during reaching and grasping tasks. Classification of hand postures using Linear Discriminant Analysis (LDA) during the reaching phase of movement shows an improvement in subjects' accuracies and abilities to preshape their fingers post training in both groups. A system utilizing magnetic trackers, a data glove, and a force sensor is sensitive to changes in motor performance elicited by a robotically facilitated, virtually simulated motor intervention and physical therapy of similar intensity."]},{"key":"dc:title","label":"Title","values":["Quantifying prehension in persons with stroke post rehabilitation"]}]}],"canonical_facts":{"dc:contributor":["Sergei Adamovich","Richard A. Foulds","Max Roman"],"dc:creator":["Puthenveettil, Saumya Sara"],"dc:description.abstract":["This study describes the analysis of reaching and grasping abilities of the hemiparetic arm and hand of patients post stroke after a series of interactive virtual reality (VR) simulated training sessions and conventional physical therapy of similar intensity. Six subjects participated in VR training and five subjects in clinical rehabilitation for two weeks. Subjects' finger joint angles were measured during a kinematic reach to grasp test using CyberGlove and arm joint angles were measured using the trackSTARTM system prior to training and after training. Downward force applied to the object during grasping was assessed using Nano17TM, a force/torque sensor system that is added to the reach to grasp test paradigm for the VR trained subjects. Results from total movement time, grasping time, and average applied force show that subjects significantly decreased their average kinematic times and force applied to object during reaching and grasping tasks. Classification of hand postures using Linear Discriminant Analysis (LDA) during the reaching phase of movement shows an improvement in subjects' accuracies and abilities to preshape their fingers post training in both groups. A system utilizing magnetic trackers, a data glove, and a force sensor is sensitive to changes in motor performance elicited by a robotically facilitated, virtually simulated motor intervention and physical therapy of similar intensity."],"dc:identifier":["https://digitalcommons.njit.edu/theses/132"],"dc:subject":["Prehension","Virtual reality simulated training","Physical therapy","Stroke rehabilitation","Biomedical Engineering and Bioengineering"],"dc:title":["Quantifying prehension in persons with stroke post rehabilitation"],"dc:type":["Thesis"],"thesis:degree_discipline":["Biomedical Engineering"],"thesis:degree_name":["Master of Science in Biomedical Engineering - (M.S.)"]},"updated_at":"2026-07-24T03:22:26Z"}