Resource-Efficient Compressive PPG Sensing Encoders With Best Sensing Matrix and Sparse Recovery Algorithm for IoT Wearable and Edge Devices

Resource-constrained wearable photoplethysmography (PPG) monitoring devices highly demand energy consumption reduction strategies to maximize the battery lifetime under continuous health monitoring applications. In this paper, we present a digital compressed PPG sensing (DCS-PPG) framework with a resource-efficient sensing matrix, sparse recovery algorithm, and best sparsifying matrix for wearable and edge PPG analysis devices in achieving a higher compression ratio (CR) with low waveform distortion and enabling data encryption or parameter extraction without reconstruction. We consider four sensing matrices (deterministic block binary diagonal (DBBD), random sparse fixed binary (RSFB), random Bernoulli (RBe), and random Gaussian (RG)), four sparsifying matrices (discrete cosine $\Psi =[C]$ , a combination of discrete cosine and discrete sine $\Psi =[C~S]$ , a combination of impulse, discrete cosine and discrete sine $\Psi =[I~C~S]$ and Haar wavelet), and six sparse recovery algorithms (orthogonal matching pursuit (OMP), approximate message passing (AMP), L1-minimization (L1-min), compressive sampling matching pursuit (CoSaMP), iterative hard thresholding (IHT) and iterative soft thresholding (IST)). On a wide variety of normal and abnormal PPG signals and five distortion measures, results show that the DCS-PPG framework based on the DBBD sensing matrix and OMP-based recovery algorithm with the sparsifying matrix $\Psi =[I~C~S]$ achieved a lower percentage root mean square difference (PRD) of $1.86~\pm ~1.65$ % at a higher CR of 3 with a lesser recovery time as compared to the results of other algorithms. Results show that the DBBD-based DCS-PPG sensing encoder can enable simultaneous data reduction and parameter extraction in the CS domain. The RSFB-based DCS-PPG sensing encoder can enable simultaneous data reduction and encryption with a lower recovery time and PRD of $2.55~\pm ~1.19$ % at a CR of 2.4 using AMP-based reconstruction with the sparsifying matrix $\Psi =[I~C~S]$ . We demonstrate the real-time feasibility of the DBBD and RSFB-based DCS-PPG encoder on an Arduino Due platform, while the Raspberry Pi platform is used to implement sparse recovery algorithms for reconstructing the compressed and encrypted data. The real-time implementation of the RSFB and AMP-based DCS-PPG encoding and decoding framework achieves a lower PRD of $2.93 \pm 1.10\%$ and energy consumption (EC) of $430.26 \pm 30.3$ mJ at a CR of 2.4 over a duration of two seconds, compared to other reconstruction algorithms such as OMP (PRD=5.84%) and L1-min (PRD=7.05%), while also achieving energy savings of $\approx 96.3\%$ with respect to L1-min. The real-time evaluation results on the Arduino platform show that the digital compressed sensing (DCS) using sensing matrix DBBD achieved energy savings of $\approx 86.8\%$ and a processing time of $295 \pm 30.5~\mu $ s compared to the DCS using RSFB. The results show that selecting the optimal sensing matrix, recovery algorithm, and sparsifying matrix plays a vital role in improving energy efficiency with reduced latency.INDEX TERMS Compressed sensing, data compression and encryption, energy efficiency, Internet of Things, PPG signal analysis, sparse recovery algorithms, wearable health monitoring.