151. Singular Spectrum Analysis of rehabilitative assessment data
- Author
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Jeremy Lim, Sharon S. W. Gan, Tracey K. M. Lee, and Saeid Sanei
- Subjects
Rehabilitation ,business.industry ,Computer science ,medicine.medical_treatment ,GRASP ,Machine learning ,computer.software_genre ,Accelerometer ,Assessment data ,medicine ,Artificial intelligence ,business ,computer ,Singular spectrum analysis - Abstract
Proper clinical assessment of a patient's progress in rehabilitation will determine the regimen and intensity of therapy. At present many objective assessments require therapists to subjectively grade various tasks patients perform. The administration of these assessments are onerous and error prone. By embedding sensors into objects used in the assessments, we are able to accurately measure and assess the ability of a patient to perform various tasks, in this case, grasping and reaching actions. We also are able to capture the nuances of movements which are not easily obtainable by other means. We focus on the use of accelerometers in these tests and their use in detecting abnormalities in grasp and reach and the benefit of Singular Spectrum Analysis in frequency based analysis.
- Published
- 2013
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