An Activity Graph-Based Deep Convolutional Neural Network Framework in Symptom Severity Diagnosis Towards Parkinson's Disease Using Inertial Sensors

Parkinson's disease (PD) is a neurodegenerative disorder diagnosed and assessed primarily through the subjective Hoehn and Yahr (H-Y) staging system, which can be limited by doctor's subjectivity, particularly in classifying subtle motor symptoms, leading to potential misclassification. Previous research has predominantly relied on machine learning algorithms that incorporated handcrafted feature extraction techniques. However, these approaches are constrained by domain-specific knowledge, which restricts the complexity of feature extraction, subsequently impacting algorithmic performance. To address these challenges, we propose a novel approach: a PD diagnosis assistance framework based on convolutional neural networks (CNNs) for automatic feature extraction and PD severity classification. In this paper, we collaborated with the First People's Hospital of Yunnan Province to collect motor data from 70 PD patients using wearable sensors equipped with an accelerometer and gyroscope. Neurologists assessed the PD severity on the Unified Parkinson's Disease Rating Scale (UPDRS) from simultaneously recorded video footages. The measured time data were transformed into activity graphs using recurrence transform, and two-dimensional images were constructed for training the network. The CNN model was trained by convolving images representing H-Y staging with kernels. The proposed symptom severity diagnosis of PD framework based on CNN was compared to previously studied machine learning algorithms and found to outperform them (accuracy = 84.52, recall = 80.18, f1-score = 80.69).