Abstract
Nowadays, somatosensory devices were widely used for developing the rehabilitation systems for limb-injured patients. However, professional evaluation was rarely studied in this field. In our study, we presented a novel hybrid deep network combined long short-term memory (LSTM) network and convolutional neural network (CNN) for rehabilitation evaluation referred to Brunnstrom Scale. In the identification task of 3-class Brunnstrom stages (III, IV, V), the mean accuracy of our proposed model was up to 80% (84.1%). The experimental result validated the reliability of our proposed evaluation method. And the comparison result of three machine learning algorithms indicated that the superiority of our hybrid model for Kinect-based 3D data analysis.
Highlights
In many countries, spinal cord injury (SCI) is an extremely pervasive health-care problem, causing death and acquired physical disability [1]
(1) Accuracy rate (ACC): the percentage of successful selections of Brunnstrom stages assessed by Physical Therapist (PT). (2) Receiver operating characteristic (ROC) curves: a plot of test sensitivity as the y coordinate versus its 1-specificity or false positive rate as the x coordinate
Average cross validation (CV) ACCs indicated that the precisions of all algorithms were higher than the random level of 3-class classification (i.e., 33.3%)
Summary
Spinal cord injury (SCI) is an extremely pervasive health-care problem, causing death and acquired physical disability [1]. SCIs lead to partial or full paralysis and result in lasting loss of motor function because damaged axons can not regenerate and the death of a considerable quantity neurons occur in the injured spinal cord [2], [3]. People with such behavioral deficit experiences dramatic pain in performing daily activities such as dressing, washing, and eating. Relevant neuroplasticity studies have indicated that SCI patients with physical deficiencies can be partially recovered through proper physiotherapy rehabilitation [4], [5]. Rehabilitation assessment of BCI patients adopts this combination of filling in scales and professional
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