#ACCURATE 4 FULL MANUAL#
There are various manual methods for evaluating abnormal posture of PD, including clinical scales, inclinometer, wall goniometer and photo-based measurement. Early recognition and standardized management will be helpful to delay the progression of postural abnormalities in PD to avoid worse outcomes. At present, its pathogenesis is unclear and there is no recognized objective and quantitative assessment method. Abnormal postures cause pain and balance dysfunction, aggravating walking difficulties with important impacts on life quality.
Common postural abnormalities in PD include sagittal abnormalities: camptocormia and anterocollis coronal abnormalities: Pisa syndrome and scoliosis. A cross-sectional study involving 811 PD patients showed the prevalence of postural abnormalities reached 21.5%. Postural abnormalities are frequent and disabling complications of PD. Parkinson’s disease (PD) is the second most common chronic neurodegenerative disease after Alzheimer’s disease, characterized by motor impairments with tremor, rigidity and akinesia/bradykinesia as cardinal symptoms. This study demonstrates the practicability of our proposed method in the clinical scenario to help making the medical decision about PD. We developed an intelligent evaluation system to provide accurate and automated assessment of trunk postural abnormalities in PD patients. Besides, the decision tree classifier performed outstandingly, reaching 90.0% of accuracy, 95.7% of specificity and 89.1% of sensitivity in rating postural severity. The intraclass correlation coefficient (ICC) between the machine’s and doctors’ score was 0.940 (95%CI, 0.905–0.962), meaning the machine was highly consistent with the doctors’ judgement. The automated grading of postural abnormalities for PD patients was realized with only six selected features. An objective function was implanted to further improve the human–machine consistency. The decision tree classifier was carried out over a data set established by the collected features and the corresponding doctors’ MDS-UPDRS-III 3.13 (the 13th item of the third part of Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale) scores. The collected images were processed to extract three-dimensional body joints, which were then converted to two-dimensional body joints to obtain eight quantified coronal and sagittal features (F1-F8) of the trunk. Kinect was used to collect the postural images from 70 PD patients. The combination of depth camera and machine learning makes this purpose possible. Automated and accurate assessment for postural abnormalities is necessary to monitor the clinical progress of Parkinson’s disease (PD).
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