Analog Gesture Detection Circuits for Noisy Biopotential Signals
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Solution Overview
Problem
Conventional biopotential signal acquisition systems are susceptible to noise such as motion artifacts and power-line induced noise, leading to erroneous results and high power consumption, which hampers effective human-machine interaction.
Innovation Solution
Analog circuits with mixed-signal processing are used to amplify biopotential signals while suppressing baseline wandering and power-line-induced noise, coupled with analog correlators for low-power wake-up of digital signal-processing components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional means are used to detect biopotential signals, then signal detection is achieved, but noise susceptibility increases leading to erroneous results
Solution Approach 1:
The patent extracts and removes harmful noise components from the biopotential signal using adaptive filtering techniques. The system separates the desired biopotential signal from unwanted noise sources such as power-line interference and motion artifacts through digital signal processing, thereby improving measurement precision while reducing noise susceptibility.
Solution Approach 2:
The patent implements feedback mechanisms where the detected signal is continuously processed and used to adjust filtering parameters in real-time. The adaptive filter uses feedback from the signal characteristics to dynamically adjust its transfer function, optimizing noise rejection while preserving the integrity of the biopotential signal.
2Productivity
If machine-learning models are used to process biopotential signals, then signal processing capability is improved, but power consumption increases
Solution Approach 1:
The patent segments the signal processing task into multiple stages: initial adaptive filtering for noise reduction, followed by selective application of machine-learning models only when necessary. This segmentation allows the system to achieve good processing capability while minimizing power consumption by avoiding continuous use of computationally intensive models.
Solution Approach 2:
The patent employs periodic action by using adaptive filtering as a continuous low-power preprocessing stage, and activating machine-learning models only periodically when complex pattern recognition is needed. This periodic activation of high-power components significantly reduces overall power consumption while maintaining processing capability.
3Measurement precision
If adaptive filtering is applied to suppress noise, then signal quality is improved, but system complexity increases
Solution Approach 1:
The patent replaces complex hardware-based noise suppression systems with software-based adaptive filtering algorithms. This substitution maintains signal quality improvement while reducing physical system complexity, as the filtering is achieved through digital signal processing rather than additional analog components.
Data Source
AI summary
An example apparatus for processing biopotential signals includes a plurality of analog correlators, each analog correlator configured to receive time-series analog signals from an electrode of a biopotential-acquisition device and correlate the time-series analog signals with a respective filter impulse response to identify a respective degree of correlation. The example apparatus also includes a plurality of comparators, each comparator coupled to a respective analog correlator and configured to detect peaks in the respective degree of correlation.