The swift advancement in wearable sensor technology has made continuous health monitoring accessible, opening doors for myriad health-related applications. Nevertheless, managing and integrating real-time data from multiple wearable sensors remains a notable challenge. This research offers an innovative solution to this issue - a unified cross-platform application capable of integrating real-time time-series data from various wearable sensors, such as the Apple Watch and Empatica E4.

The application, developed using the Flutter framework, streamlines the process of collecting, managing, and analyzing sensor data, thereby significantly easing the task for health professionals and researchers. The application can simultaneously capture and integrate various physiological signals, such as heart rate, acceleration, and skin temperature. Our application ensures compatibility across iOS and Android platforms, extending its accessibility to a broader user base.

In order to competently handle the surge of substantial time-series data, we utilize InfluxDB, a robust time-series database, to serve as the data storage infrastructure. Each recording session's data is stored in a uniquely identified InfluxDB bucket, providing efficient data management and retrieval, even when handling substantial data volumes from multiple sources. Moreover, it enables real-time analytics derived directly from sensor data.

As a significant contribution to the mHealth research field, this research's main output is a robust, scalable, and user-friendly application capable of seamlessly integrating a wide range of sensor data. It leverages the capabilities of a time-series database for effective data management and serves as a practical tool for health researchers and practitioners working with wearable sensor technology.