Time Series in mHealth Applications: Task Definition and Data Collection

• Grant number: 23/03069-3
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Effective date (Start): April 01, 2023
• Effective date (End): March 31, 2024
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Diego Furtado Silva
• Grantee: Gabriel da Costa Merlin
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated research grant: 22/03176-1 - Machine learning for time series obtained in mHealth applications, AP.PNGP.PI

Abstract

Monitoring physiological signs, vital signs, and other parameters that can be collected over time from individuals are essential in several tasks in healthcare, such as heart rate estimation and the identification of abnormal heartbeats. However, these time series are obtained by very expensive and usually not portable equipment. On the other hand, with the improvement and miniaturization of sensors capable of transmitting various data types, mobile and wearable devices have increasingly shown themselves as options to support medical decisions. Smartphones and smartwatches have increasingly accurate and diverse sensors, making the World Health Organization consider that mobile health (mHealth) may revolutionize how populations interact with public health systems. This work is part of a project that aims to develop techniques for learning predictive models for this type of data. Specifically, this work consists of defining end tasks and procedures for data collection. Initially, data collection should follow protocols already described in the literature. However, it can be adapted according to practical needs.