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 Ψ = [<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i>], a combination of discrete cosine and discrete sine Ψ = [<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C S</i>], a combination of impulse, discrete cosine and discrete sine Ψ = [<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I C S</i>] 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 Ψ = [<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I C S</i>] achieved a lower percentage root mean square difference (PRD) of 1.86 ± 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 the CR of 2.4 and lower PRD of 2.55 ± 1.19%, and recovery time using AMP-based reconstruction with the sparsifying matrix Ψ = [<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I C S</i>]. We demonstrated the real-time feasibility of the DBBD and RSFB-based DCS-PPG encoder on an Arduino Due platform, while the Raspberry Pi platform was 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 achieved a lower PRD of 2.93 ± 1.10% and energy consumption (EC) of 430.26 ± 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 ≈ 96.3% with respect to L1-min. The real-time evaluation results on the Arduino platform show that the digital compressed sensing (DCS) based on DBBD achieved energy savings of ≈ 86.8% and a processing time of 295 ± 30.5 μs compared to the DCS based on 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.