Compressed PPG Sampling for Low-Power Packet-Loss-Tolerant BANs
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Solution Overview
Problem
Current signal processing methods in body area networks (BANs) face challenges in reducing power consumption and computational complexity while maintaining application-specific quality metrics, particularly in healthcare applications, where packet loss rates are high and bandwidth overhead is significant.
Innovation Solution
The implementation of non-uniform sampling techniques using compressed sensing (CS) to acquire and reconstruct signals, allowing for reduced power consumption and lower bandwidth overhead by generating non-uniform sampling instances and sensing samples of signals, which can be transmitted and reconstructed efficiently, even in the presence of packet losses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional Nyquist sampling is used to acquire signals in BANs, then signal fidelity is maintained, but power consumption and bandwidth overhead increase significantly
Solution Approach 1:
The patent changes the sampling rate parameter from Nyquist rate to lower rates, and transforms the signal representation parameters through compression sensing algorithms, enabling faithful signal recovery at lower sampling rates by exploiting signal sparsity in transformed domains
Solution Approach 2:
The patent replaces traditional mechanical sampling approaches with compression sensing mathematical transformations, using linear algebra operations and optimization algorithms to reconstruct signals from fewer samples, substituting direct physical sampling with computational reconstruction
2Loss of information
If traditional sampling methods are used, then complete signal information is captured, but computational complexity at sensors increases
Solution Approach 1:
The patent segments the computational workload between the sensor (acquiring fewer samples) and the receiver/reconstructor (performing complex reconstruction algorithms), dividing the overall signal processing task into acquisition and reconstruction phases with different complexity requirements
3Reliability
If Forward Error Correction coding is used to handle packet loss, then communication reliability improves, but bandwidth overhead and sensor complexity increase
Solution Approach 1:
The patent converts the harmful effect of packet loss into a beneficial feature by designing compression sensing acquisition to be inherently robust to packet loss, where the random sampling pattern and sparsity-based reconstruction naturally tolerate missing data without requiring additional error correction overhead
Data Source
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AI summary
Certain aspects of the present disclosure relate to a method for compressed sensing (CS). The CS is a signal processing concept wherein significantly fewer sensor measurements than that suggested by Shannon/Nyquist sampling theorem can be used to recover signals with arbitrarily fine resolution. In this disclosure, the CS framework is applied for sensor signal processing in order to support low power robust sensors and reliable communication in Body Area Networks (BANs) for healthcare and fitness applications.