The integration of wearable devices into computing environments represents a significant advancement in digital healthcare and fitness monitoring. This paper presents the design and implementation of a lightweight system for real-time acquisition and processing of heart rate data from Samsung Galaxy smartwatches running WearOS. The solution consists of two applications: a Kotlin-based application on the smartwatch for sensor data collection and a Python-based application on the computer for data reception, visualization, and storage using the Firestore cloud platform. Experiments confirmed that the system operates reliably in real-world conditions, achieving end-to-end latency below 200 ms, mean absolute percentage error (MAPE) below 5% across resting, walking, and jogging activities, packet loss under 2%, and battery consumption of approximately 3–4% per hour. These results demonstrate that the system provides both accurate and efficient monitoring while maintaining low complexity and resource requirements. The contributions of this work include seamless PC integration without requiring a smartphone intermediary, real-time cloud synchronization, and user-friendly visualization and export functionalities. While the current implementation is limited to heart rate monitoring and one smartwatch model, it highlights the potential of wearable–cloud integration for scalable health monitoring solutions. Future directions involve extending support to additional sensors and devices, improving energy efficiency, enhancing security mechanisms, and conducting multi-user and clinical evaluations. This study demonstrates that combining WearOS devices with cloud services can enable practical, lightweight, and scalable health monitoring systems applicable in personal fitness, elderly care, and clinical healthcare contexts.