Unified Quality-Aware Compression and Pulse-Respiration Rates Estimation Framework for Reducing Energy Consumption and False Alarms of Wearable PPG Monitoring Devices.

Due to the high demands of tiny, compact, lightweight, and low-cost photoplethysmogram (PPG) monitoring devices, these devices are resource-constrained including limited battery power. Consequently, it highly demands frequent charge or battery replacement in the case of continuous PPG sensing and transmission. Further, PPG signals are often severely corrupted under ambulatory and exercise recording conditions, leading to frequent false alarms. In this paper, we propose a unified quality-aware compression and pulse-respiration rates estimation framework for reducing energy consumption and false alarms of wearable and edge PPG monitoring devices by exploring predictive coding techniques for jointly performing signal quality assessment (SQA), data compression and pulse rate (PR) and respiration rate (RR) estimation without the use of different domains of signal processing techniques that can be achieved by using the features extracted from the smoothed prediction error signal. By using the five standard PPG databases, the performance of the proposed unified framework is evaluated in terms of compression ratio (CR), mean absolute error (MAE), false alarm reduction rate (FARR), processing time (PT) and energy saving (ES). The compression, PR, RR estimation, and SQA results are compared with the existing methods and results of uncompressed PPG signals with sampling rates of 125 Hz and 25 Hz. The proposed unified qualityaware framework achieves an average CR of 4%, SQA (Se of 92.00%, FARR of 84.87%), PR (MAE: 0.46 +/- 1.20) and RR (MAE: 1.75 (0.65-4.45), PT (sec) of 15.34 +/- 0.01) and ES of 70.28% which outperforms the results of uncompressed PPG signal with a sampling rate of 125 Hz. Arduino Due computing platformbased implementation demonstrates the real-time feasibility of the proposed unified quality-aware PRRR estimation and data compression and transmission framework on the limited computational resources. Thus, it has great potential in improving energy-efficiency and trustworthiness of wearable and edge PPG monitoring devices.