Pose-based Tremor Type and Level Analysis for Parkinson's Disease from
  Video
Haozheng Zhang Edmond S. L. Ho Xiatian Zhang Silvia Del Din Hubert P. H. Shum
Purpose:Current methods for diagnosis of PD rely on clinical examination. The
accuracy of diagnosis ranges between 73% and 84%, and is influenced by the
experience of the clinical assessor. Hence, an automatic, effective and
interpretable supporting system for PD symptom identification would support
clinicians in making more robust PD diagnostic decisions. Methods: We propose
to analyze Parkinson's tremor (PT) to support the analysis of PD, since PT is
one of the most typical symptoms of PD with broad generalizability. To realize
the idea, we present SPA-PTA, a deep learning-based PT classification and
severity estimation system that takes consumer-grade videos of front-facing
humans as input. The core of the system is a novel attention module with a
lightweight pyramidal channel-squeezing-fusion architecture that effectively
extracts relevant PT information and filters noise. It enhances modeling
performance while improving system interpretability. Results:We validate our
system via individual-based leave-one-out cross-validation on two tasks: the PT
classification task and the tremor severity rating estimation task. Our system
presents a 91.3% accuracy and 80.0% F1-score in classifying PT with non-PT
class, while providing a 76.4% accuracy and 76.7% F1-score in more complex
multiclass tremor rating classification task. Conclusion: Our system offers a
cost-effective PT classification and tremor severity estimation results as
warning signs of PD for undiagnosed patients with PT symptoms. In addition, it
provides a potential solution for supporting PD diagnosis in regions with
limited clinical resources.