Data-driven Gait based Severity Classification for Parkinson's Disease using Duo Spatiotemporal Convoluted Kernel Boosted ResNet model - M, Arogia Victor Paul; Shankar, Sharmila (International Journal of Electrical and Computer Engineering Systems, 2024)
Parkinson’s disease (PD) is one of the reformed brain syndromes that results in unintended stiffness and difficulty with balance and dexterity. To detect PD in medical scenery, physicians commonly use experimental indicators like motorized and non-motor symptoms and the severity rating depends on the unified PD Rating Scale (UPDRS). However, these medical assessments highly rely on expertized clinicians and lead to inter-variability discrepancies. Nowadays, gait sensor data assists doctors in diagnosing PD and estimates the severity level of gait abnormalities in patients. However, the gait sensor data increases the dimensionality issues and is subjected to high non-linear complexity. Hence, this study suggests an innovative deep learning (DL) technique for accurate PD analysis using gait patterns. Initially, the gait sensor data is preprocessed by performing data cleaning, and decimal scaling normalization (DS- Norm) to enhance the data quality. The Hoehn and Yahr (H&Y) scale is a commonly used rating scale for measuring the progression of Parkinson's disease symptoms. It's typically used to assess motor symptoms like tremors, rigidity, and bradykinesia. The scale ranges from 0 to 5, with higher numbers indicating more severe symptoms and disability. The preprocessed data are then fed into the proposed Duo spatiotemporal convoluted kernel boosted ResNet (DSCK-RNet) model for classifying the PD severity rating by learning the gait spatiotemporal features. The developed method is processed and scrutinized via the Python platform and a publicly available Physio- Net dataset is utilized for the simulation process. Various assessment measures like accuracy, precision, sensitivity, specificity, PPV, FPR, and MCC are examined and compared with traditional studies. In the experimental section, the developed DSCK-RNet model achieved an accuracy of 100%, 99.6%, 99%, and 99.64% for different classes like healthy, severity-2, severity-2.5, and severity-3 respectively. Compared to the conventional techniques, our suggested approach performs better. The experimental findings demonstrate the clinical significance of the suggested approach for the impartial evaluation of gait motor impairment in PD patients.