# A Novel Personalized Federated Learning Method for Privacy-Preserving Smart Mobile Health Monitoring  

## Dataset


We have uploaded the preprocessed versions of the Oxford Parkinson’s Telemonitoring Dataset and the CrossCheck Dataset, where each file corresponds to one client in the federated learning setting. Due to copyright and data usage restrictions, the PPMI dataset cannot be redistributed; interested researchers may apply for access and download it independently through the official website. For completeness, the download links to the original versions of all three datasets are provided below for reference.

- **CrossCheck Dataset**  
  - Download: [https://www.mh4mh.org/eureka-data](https://www.mh4mh.org/eureka-data)  
  - Description: The CrossCheck dataset encompasses six categories of behaviors: physical behavior, dialogue behavior, mobile phone call behavior, displacement behavior, text message behavior, and mobile phone use behavior. 

- **Oxford Parkinson's Telemonitoring Dataset**  
  - Download: [https://archive.ics.uci.edu/dataset/189/parkinsons+telemonitoring](https://archive.ics.uci.edu/dataset/189/parkinsons+telemonitoring)  
  - Description: The Oxford dataset focuses on the vocal characteristics of Parkinson’s disease (PD) patients, encompassing features such as pitch, volume, periodic variation, and nonlinear dynamics.

- **PPMI (Parkinson's Progression Markers Initiative) Dataset**  
  - Download: [https://www.ppmi-info.org/access-data-specimens/data](https://www.ppmi-info.org/access-data-specimens/data)  
  - Description:The PPMI dataset is a comprehensive medical database encompassing multimodal data, including clinical records, medical imaging, multi-omics analyses, genetic information, sensor monitoring data, and biomarker measurements. In this study, to align with mobile health monitoring scenarios, we specifically extracted physiological data of Parkinson’s disease patients collected via wearable devices. These data cover key dimensions such as motor function metrics, heart rate variability parameters, and sleep behavior patterns.
