The thesis presents the design, development, and verification of a communication and data management architecture for IoT-based assistive systems for the remote tracking of physiological signals. The motivation is the increasing demand for reliable, secure, and privacy-preserving healthcare applications, whereby remote tracking of patients can contribute to the early detection of anomalies, elderly care, and the clinician's decision support. The architecture deployed within this system is structured across three top-level layers: data acquisition, communication and processing, and storage and visualization. Physiological measures are detected with a wearable sensor and collected with a Raspberry Pi that acts as a local gateway. The local gateway runs custom programs that serialize the sensor readings in JSON format, add metadata fields, and publish the information both with a local broker and with a remote broker.