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Purpose:Current methods for diagnosis of PD rely on clinical examination. Theaccuracy of diagnosis ranges between 73% and 84%, and is influenced by theexperience of the clinical assessor. Hence, an automatic, effective andinterpretable supporting system for PD symptom identification would supportclinicians in making more robust PD diagnostic decisions. Methods: We proposeto analyze Parkinson's tremor (PT) to support the analysis of PD, since PT isone of the most typical symptoms of PD with broad generalizability. To realizethe idea, we present SPA-PTA, a deep learning-based PT classification andseverity estimation system that takes consumer-grade videos of front-facinghumans as input. The core of the system is a novel attention module with alightweight pyramidal channel-squeezing-fusion architecture that effectivelyextracts relevant PT information and filters noise. It enhances modelingperformance while improving system interpretability. Results:We validate oursystem via individual-based leave-one-out cross-validation on two tasks: the PTclassification task and the tremor severity rating estimation task. Our systempresents a 91.3% accuracy and 80.0% F1-score in classifying PT with non-PTclass, while providing a 76.4% accuracy and 76.7% F1-score in more complexmulticlass tremor rating classification task. Conclusion: Our system offers acost-effective PT classification and tremor severity estimation results aswarning signs of PD for undiagnosed patients with PT symptoms. In addition, itprovides a potential solution for supporting PD diagnosis in regions withlimited clinical resources.

Pose-based Tremor Type and Level Analysis for Parkinson's Disease from Video