Journal article
Quantitative assessment of hand motor function in cervical spinal disorder patients using target tracking tests
Journal of rehabilitation research and development, Vol.53(6), pp.1007-1022
2016
Handle:
https://hdl.handle.net/2376/105041
PMID: 28475202
Abstract
Cervical spondylotic myelopathy (CSM) is a chronic spinal disorder in the neck region. Its prevalence is growing rapidly in developed nations, creating a need for an objective assessment tool. This article introduces a system for quantifying hand motor function using a handgrip device and target tracking test. In those with CSM, hand motor impairment often interferes with essential daily activities. The analytic method applied machine learning techniques to investigate the efficacy of the system in (1) detecting the presence of impairments in hand motor function, (2) estimating the perceived motor deficits of CSM patients using the Oswestry Disability Index (ODI), and (3) detecting changes in physical condition after surgery, all of which were performed while ensuring test-retest reliability. The results based on a pilot data set collected from 30 patients with CSM and 30 nondisabled control subjects produced a c-statistic of 0.89 for the detection of impairments, Pearson r of 0.76 with p < 0.001 for the estimation of ODI, and a c-statistic of 0.82 for responsiveness. These results validate the use of the presented system as a means to provide objective and accurate assessment of the level of impairment and surgical outcomes.
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Details
- Title
- Quantitative assessment of hand motor function in cervical spinal disorder patients using target tracking tests
- Creators
- Sunghoon I Lee - Computer Science Department, University of California Los Angeles (UCLA), Los Angeles, CAAlex Huang - Department of Neurosurgery, UCLA, Los Angeles, CABobak Mortazavi - Department of Neurosurgery, UCLA, Los Angeles, CACharles Li - Computer Science Department, University of California Los Angeles (UCLA), Los Angeles, CAHaydn A Hoffman - Department of Neurosurgery, UCLA, Los Angeles, CAJordan Garst - Department of Neurosurgery, UCLA, Los Angeles, CADerek S Lu - Department of Neurosurgery, UCLA, Los Angeles, CARuth Getachew - Department of Neurosurgery, UCLA, Los Angeles, CAMarie Espinal - Department of Neurosurgery, UCLA, Los Angeles, CAMehrdad Razaghy - Department of Neurosurgery, UCLA, Los Angeles, CANima Ghalehsari - Department of Neurosurgery, UCLA, Los Angeles, CABrian H Paak - Department of Neurosurgery, UCLA, Los Angeles, CAAmir A Ghavam - Department of Neurosurgery, UCLA, Los Angeles, CAMarwa Afridi - Department of Neurosurgery, UCLA, Los Angeles, CAArsha Ostowari - Department of Neurosurgery, UCLA, Los Angeles, CAHassan Ghasemzadeh - Computer Science Department, University of California Los Angeles (UCLA), Los Angeles, CADaniel C Lu - Department of Orthopedic Surgery, UCLA, Los Angeles, CAMajid Sarrafzadeh - Computer Science Department, University of California Los Angeles (UCLA), Los Angeles, CA
- Publication Details
- Journal of rehabilitation research and development, Vol.53(6), pp.1007-1022
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Publisher
- United States
- Identifiers
- 99900546993101842
- Language
- English
- Resource Type
- Journal article