Biomedical Sensor Data Processing for Power-Constrained Wireless Monitoring
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Solution Overview
Problem
Biomedical signal sensors have finite operating times due to high power consumption, particularly during continuous monitoring, which limits their ability to transmit data effectively over wireless channels without depleting their battery life.
Innovation Solution
A sensor system that reduces power consumption by transmitting only statistical data or data that deviates from a set range, using a data processing unit to determine when to transmit output data based on input data and reference data, thereby extending battery life and enabling continuous monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous data transmission is performed to enable accurate monitoring of biomedical signals, then monitoring accuracy is improved, but power consumption increases and battery life decreases
Solution Approach 1:
The patent extracts and transmits only the essential monitoring information (anomaly detection results and statistical data) while filtering out redundant continuous data. The data processing unit identifies and transmits only when anomalies are detected or when statistical thresholds are exceeded, thereby maintaining monitoring accuracy while significantly reducing power consumption during wireless transmission.
Solution Approach 2:
Instead of transmitting all continuous biomedical signal data, the system performs partial transmission by sending only the necessary portions - specifically when anomalies are detected or when statistical parameters exceed predefined thresholds. This partial action approach ensures monitoring accuracy is maintained for critical events while avoiding the excessive power consumption of continuous full-data transmission.
2Loss of information
If all sensed biomedical signal data is transmitted to maintain complete monitoring information, then data completeness is improved, but transmission power consumption increases
Solution Approach 1:
The system extracts only the most critical information from the continuous biomedical signal stream for transmission. The data processing unit identifies anomalies and extracts relevant statistical data (such as mean, standard deviation, and anomaly timestamps) rather than transmitting the entire continuous signal, thereby maintaining data completeness for diagnostic purposes while minimizing transmission energy loss.
Solution Approach 2:
The patent transforms the continuous biomedical signal into discrete parameter representations - specifically anomaly detection results and statistical parameters. By changing the data representation from continuous waveform to discrete parameters, the system maintains the essential monitoring information while dramatically reducing the data volume requiring transmission, thus reducing energy loss.
3Use of energy by moving object
If statistical data and anomaly detection are used to reduce transmission, then power consumption is reduced, but data processing complexity increases
Solution Approach 1:
The data processing unit is segmented into distinct functional modules: a statistical data generation unit that calculates mean and standard deviation, and an anomaly detection unit that compares incoming data against statistical thresholds. This segmentation allows the system to reduce power consumption through intelligent data filtering while managing processing complexity through modular, specialized components rather than a monolithic processor.
Data Source
AI summary
A method and system for monitoring a biomedical signal employ a sensor module configured to output a continuous electrical signal by sensing the biomedical signal, a memory configured to store reference data, a transmitter configured to transmit output data via a wireless channel, and a data processing unit configured to determine whether to transmit input data via the transmitter as the output data, based on the input data, which is generated from the continuous electrical signal, and the reference data.