ParkinSense: A Novel Approach to Remote Idiopathic Parkinson’s Disease Diagnosis, Severity Profiling, and Telemonitoring via Ensemble Learning and Multimodal Data Fusion on Webcam-Derived Digital Biomarkers

Despite being the fastest growing Neurodegenerative disease in the world, with over ten million cases worldwide, there is no definitive diagnosis method for Parkinson’s Disease currently. Current diagnosis technologies misdiagnose one in four patients, relying on tests and technologies that are subjective to the administrating clinicians. Furthermore, they are inaccessible to billions due to immobility, geographic barriers, or associated costs. With early and accurate diagnosis being vital to effective treatment, a tremendous issue emerges. ParkinSense addresses these issues, serving as an automated web application that contactlessly analyzes three cardinal symptoms of Parkinson’s Disease over a standard webcam and microphone: Hypomimia, Dysarthria, and Bradykinesia. With Ensemble Learning coupled with various classifiers, ParkinSense was able to achieve an accuracy rate of 99.72% when tested on synthetic patients. By examining and combining multiple modalities, ParkinSense boasts accuracy rates higher than many advanced unimodal technologies, as patients often do not substantially display all the symptoms of Parkinson’s Disease, leading to misdiagnosis when relying on one symptom for diagnosis. Furthermore, ParkinSense is able to accurately estimate disease severity for positively diagnosed patients, with a Mean Absolute Error of 9.7 on the Unified Parkinson’s Disease Rating Scale. ParkinSense strongly suggests that an entirely contactless diagnosis of Parkinson’s Disease through digital biomarkers can be effective, and web applications can be utilized for rapid, automated, and remote diagnosis and severity analysis, which can be crucial for those affected by barriers that prevent access to healthcare.