Abstract
Arm motion in healthy humans is characterized by smooth and relatively short paths. The current study focused on 3D reaching in stroke patients. Sixteen right-hemiparetic stroke patients and 8 healthy adults performed 42 reaching movements towards 3 visual targets located at an extended arm distance. Performance was assessed in terms of spatial and temporal features of the movement; i.e., hand path, arm posture and smoothness. Differences between groups and within subjects were hypothesized for spatial and temporal aspects of reaching under the assumption that both are independent. As expected, upper limb motion of patients was characterized by longer and jerkier hand paths and slower speeds. Assessment of the number of sub-movements within each movement did not clearly discriminate between groups. Principal component analyses revealed specific clusters of either spatial or temporal measures, which accounted for a large proportion of the variance in patients but not in healthy controls. These findings support the notion of a separation between spatial and temporal features of movement. Stroke patients may fail to integrate the two aspects when executing reaching movements towards visual targets.
Original language | English |
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Title of host publication | 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 |
Pages | 5242-5245 |
Number of pages | 4 |
DOIs | |
State | Published - 1 Dec 2010 |
Event | 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 - Buenos Aires, Argentina Duration: 31 Aug 2010 → 4 Sep 2010 |
Conference
Conference | 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 |
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Country/Territory | Argentina |
City | Buenos Aires |
Period | 31/08/10 → 4/09/10 |
Keywords
- Arm kinematics
- Hand reaching
- Movement fragmentation
- Principal component analysis
- Stroke
ASJC Scopus subject areas
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Signal Processing
- Health Informatics