Wearable Biomedical Signal Capture With Compressed Sensing
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Solution Overview
Problem
Current methods for signal processing in body area networks face challenges with high power consumption and increased computational complexity, particularly in transmitting biomedical signals like ECG and PPG, which result in packet loss and latency issues, necessitating a more efficient approach for signal acquisition and reconstruction.
Innovation Solution
The implementation of compressed sensing (CS) techniques for non-uniform sampling and reconstruction of biomedical signals, allowing for reduced power consumption and lower bandwidth overhead by using random sampling instances and sparse representation, enabling efficient transmission and reconstruction of signals even under packet loss conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional Nyquist sampling is used for biomedical signal acquisition, then signal fidelity is maintained, but power consumption and computational complexity 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 to achieve both low power consumption and maintained signal fidelity
Solution Approach 2:
The patent extracts only the essential signal components that carry meaningful information, discarding redundant data, which reduces the amount of data that needs to be transmitted and processed while maintaining diagnostic value
2Use of energy by moving object
If compressed sensing is used for signal acquisition, then power consumption is reduced, but computational complexity at the receiver increases
Solution Approach 1:
The patent segments the computational workload between the sensor node (simple sampling and transmission) and the receiver (reconstruction and processing), allowing the sensor to operate with minimal power while shifting complexity to the receiver
3Ease of manufacture
If uniform sampling is used for biomedical signals, then signal reconstruction is straightforward, but data transmission bandwidth requirements increase
Solution Approach 1:
The patent changes the sampling strategy from uniform to non-uniform random sampling, which reduces the total number of samples needed while maintaining signal reconstruction capability through compressed sensing algorithms
4Reliability
If Forward Error Correction coding is used in BAN, then packet loss is reduced, but transmission bandwidth and sensor complexity increase
Solution Approach 1:
The patent converts the sparsity of biomedical signals into a benefit by using compressed sensing, which inherently provides robustness against packet loss without requiring additional error correction codes, thus avoiding bandwidth overhead
Data Source
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.