VSAC: um framework de compressão adaptativa para dados de saúde em cidades inteligentes

Abstract

The increasing adoption of wearable devices and smart health technologies has highlighted the need for efficient transmission and storage of physiological data in urban-scale health monitoring systems. In this context, one of the major challenges is the ability to manage large volumes of vital-sign data—such as heart rate, respiratory rate, body temperature, and blood pressure—generated continuously by heterogeneous devices, while ensuring clinical integrity, low latency, and optimized network usage. Current solutions often treat compression techniques in isolation and lack adaptive mechanisms responsive to the clinical condition of each monitored individual. This research addresses that gap by proposing an integrated and context-aware approach for compressing vital-sign data in smart city infrastructures. This study aimed to develop and validate the VSAC (Vital Sign Adaptive Compressor), an adaptive framework for managing the transmission of physiological signals using a combination of lossy and lossless data compression techniques. The framework was designed to dynamically adjust its compression parameters based on the signal type and clinical priority, ensuring a balance between data fidelity and transmission efficiency. The methodology consisted of designing and implementing a two-stage compression prototype in Python, applied to real-world datasets of heart rate collected from wearable devices with different sampling intervals. The evaluation included three operational scenarios—losslessonly, lossy-only, and hybrid compression—tested across datasets of varying sizes and densities. Performance was measured using standard metrics: compression rate, compression time, and distortion. The tests were repeated ten times per scenario to ensure measurement stability. The results confirmed that the hybrid and adaptive strategy proposed by VSAC outperformed traditional static methods. The framework achieved compression rates up to 46.3% higher than lossless-only approaches while maintaining distortion levels below 10% in most cases, especially in medium and large datasets. The compression time was significantly reduced with the use of LZW in the final stage, particularly for large files. The study concludes that the adaptive and hybrid compression approach embodied in VSAC offers a robust and scalable solution for the efficient transmission of vital-sign data in smart healthcare environments. Its context-aware mechanism enables real-time prioritization and efficient resource usage, making it a promising tool for public health policies that rely on connected infrastructures and timely clinical decision-making in smart cities.