Publications

Abstract : In this article, we propose a new real-time quality-aware quality-control data compression framework for maximizing the battery life of Internet of Things (IoT) and Smartphone based health monitoring devices. The proposed framework is implemented on Arduino Due with a 32-bit Atmel SAM3X8E ARM Cortex-M3 processor and validated using four standard databases and real-time signals obtained by using our sensing hardware. The proposed framework achieves compression ratios between seven and 28 with energy saving between 83% and 92%. Results demonstrate that our framework provides a promising energy saving solution by discarding the noisy photoplethysmogram (PPG) signals and pulse-free signals before performing data compression and compressing the noise-free PPG signals efficiently with very small feature parameter error.