Ai Enabled Cloud Based Continuous Monitoring System For Early Detection Of Parkinsons Disease Motor System
DOI:
https://doi.org/10.29284/37vhmd33Keywords:
Parkinson's disease, motor dysfunction, artificial intelligence, cloud computing, wearable sensors, remote monitoring, deep learning, neural networks, digital biomarkers, telemedicine.Abstract
Parkinson's disease (PD) affects approximately 8.5 million individuals globally, with motor system dysfunction constituting a critical diagnostic marker. Early detection of motor abnormalities enables timely therapeutic intervention and improved patient outcomes. This study presents a novel AI-enabled, cloud-based continuous monitoring framework integrating multimodal sensor arrays, deep learning algorithms, and real-time cloud analytics for non-invasive, remote detection of PD-related motor dysfunction. Our system employs wearable inertial measurement units (IMU) combined with video-based pose estimation and acoustic analysis, synchronized through a secure cloud infrastructure. A convolutional neural network (CNN) ensemble architecture with attention mechanisms processes temporal motor patterns, achieving 94.7% sensitivity and 92.1% specificity in discriminating early-stage PD from healthy controls across a cohort of 487 participants (mean age 58.3±9.2 years). Longitudinal analysis over 18 months demonstrated 89.3% correlation with clinical Motor Unified Parkinson's Disease Rating Scale (UPDRS-III) scores. Cloud-based infrastructure enables real-time biomarker computation, predictive analytics, and automated clinical alert generation. This framework represents a paradigm shift in distributed neurodegenerative disease monitoring, providing scalable, cost-effective early detection infrastructure applicable to resource-limited healthcare settings. Integration with electronic health records and development of smartphone-compatible applications further enhances accessibility and clinical utility.
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