  Parkinson's disease (PD) is a progressive neurodegenerative disorder that
results in a variety of motor dysfunction symptoms, including tremors,
bradykinesia, rigidity and postural instability. The diagnosis of PD mainly
relies on clinical experience rather than a definite medical test, and the
diagnostic accuracy is only about 73-84% since it is challenged by the
subjective opinions or experiences of different medical experts. Therefore, an
efficient and interpretable automatic PD diagnosis system is valuable for
supporting clinicians with more robust diagnostic decision-making. To this end,
we propose to classify Parkinson's tremor since it is one of the most
predominant symptoms of PD with strong generalizability. Different from other
computer-aided time and resource-consuming Parkinson's Tremor (PT)
classification systems that rely on wearable sensors, we propose SPAPNet, which
only requires consumer-grade non-intrusive video recording of camera-facing
human movements as input to provide undiagnosed patients with low-cost PT
classification results as a PD warning sign. For the first time, we propose to
use a novel attention module with a lightweight pyramidal
channel-squeezing-fusion architecture to extract relevant PT information and
filter the noise efficiently. This design aids in improving both classification
performance and system interpretability. Experimental results show that our
system outperforms state-of-the-arts by achieving a balanced accuracy of 90.9%
and an F1-score of 90.6% in classifying PT with the non-PT class.
