Abstract
With the progression of an aging society, early detection of neurodegenerative diseases such as Parkinson’s disease (PD) has become increasingly important. Tremors, a primary symptom of PD, serve as key indicators of disease progression and treatment efficacy. However, the scarcity of tremor-related data poses a significant challenge in developing robust human activity recognition (HAR) models. To address this issue, we propose a data augmentation framework using a conditional variational autoencoder to generate high-quality synthetic data conditioned on activity labels. Additionally, we employ evolutionary computation to optimize hyperparameters for data generation, further improving model performance. Our approach enhances both the diversity and robustness of training datasets, enabling the development of more accurate recognition models. Experimental results demonstrate that our framework significantly improves HAR, particularly in scenarios with limited real-world data. This method provides a scalable solution to advance PD detection and monitoring through multimodal sensor-based HAR. Our model trained with data augmentation achieved an F1 score of 45.6% in three different sets, while the recognition model trained without data augmentation achieved an F1 score of 31.6%.
