Recognizing Normal and Unusual Activities for Parkinson’s Disease using Multimodal Sensor Data

Abstract

Early detection of Parkinson’s disease (PD) motor symptoms is crucial for improving clinical outcomes. This study presents a convolutional neural network-long short-term memory (CNN-LSTM) model for classifying PD-related activities using accelerometer data, developed as part of the ABC Challenge 2024. Our approach integrates a sliding window strategy with signal mutation detection to address data alignment challenges, combined with time-frequency feature extraction and temporal pattern modeling. Evaluated on a dataset of 9 subjects performing 10 activities, the model achieves an F1 score of 80.2% through leave-one-subject-out cross-validation. The results demonstrate the framework’s capability for continuous monitoring of symptoms such as tremors and freezing of gait. Future work will focus on expanding the dataset and optimizing the model architecture for real-world deployment.