Biosignal Sensor Filtering for Power and Bandwidth Reduction
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Solution Overview
Problem
Traditional biosignal monitoring systems face challenges with power drain and network bandwidth usage due to continuous ambulatory monitoring, which limits the battery life and hardware capabilities of biosignal sensors, and existing methods like intermittent monitoring and low-power ICs either lose pertinent data or require additional memory and bandwidth.
Innovation Solution
Implementing a multi-stage series of orthonormal domain filters to classify biosignals as normal or abnormal, transmitting only abnormal signals, thereby reducing network bandwidth and power consumption, and using a combination of frequency and time domain filters to enhance classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous ambulatory monitoring is implemented, then real-time feedback and proper diagnosis are ensured, but power drain increases and battery life decreases
Solution Approach 1:
The patent extracts and transmits only the essential information (abnormal biosignal events) rather than continuous raw biosignal data. The sensor device performs local processing to identify abnormal events and transmits only these discrete events to the remote device, significantly reducing power consumption while maintaining diagnostic reliability.
Solution Approach 2:
The sensor device performs preliminary processing and classification of biosignals locally before transmission. By pre-identifying abnormal events at the source, the system avoids the need to continuously transmit all biosignal data, thereby reducing power consumption while ensuring that only diagnostically relevant information is sent to the remote device.
2Loss of information
If continuous biosignal transmission is performed, then complete data is available for analysis, but network bandwidth consumption increases
Solution Approach 1:
The system extracts only abnormal biosignal events from the continuous biosignal stream and transmits only these extracted events to the remote device. This selective transmission maintains the completeness of diagnostically relevant information while dramatically reducing network bandwidth consumption compared to transmitting continuous raw data.
Solution Approach 2:
The sensor device performs preliminary classification of biosignals to identify abnormal events before transmission. This pre-processing ensures that only events requiring medical attention are transmitted, preserving information completeness for critical events while minimizing overall data transmission volume.
3Quantity of substance
If additional hardware is added to sensors, then power supply capacity and memory increase, but device size increases
Solution Approach 1:
The sensor device performs signal processing, classification, and event detection functions locally using its existing hardware resources. By making the sensor self-sufficient in processing biosignals and identifying abnormal events, the system eliminates the need for additional large power supplies or memory at the sensor, as the processing is done in real-time with minimal data storage requirements.
Solution Approach 2:
The sensor device performs preliminary processing and classification of biosignals locally before transmission. This pre-processing approach allows the use of minimal hardware resources at the sensor, as the complex analysis is performed locally rather than requiring large power supplies or memory capacity for data buffering.
4Use of energy by moving object
If intermittent monitoring is used, then power consumption decreases, but pertinent biosignal information is lost
Solution Approach 1:
The system continuously monitors biosignals but extracts and transmits only abnormal events that have been identified through local processing. This approach maintains the power savings of intermittent transmission while preventing information loss, as the continuous monitoring is used solely for event detection rather than for continuous data transmission.
Solution Approach 2:
The sensor device performs preliminary classification of continuously monitored biosignals to identify abnormal events before transmission. This pre-processing ensures that no pertinent information is lost, as all abnormal events are captured and transmitted, while still maintaining reduced power consumption compared to continuous transmission of all data.
5Use of energy by moving object
If localized data batching is implemented, then memory requirements increase, but power consumption and bandwidth usage are reduced
Solution Approach 1:
The system extracts abnormal events from continuous biosignal monitoring in real-time and transmits them immediately, rather than batching data locally. This approach reduces the need for large memory capacity at the sensor device, as data is processed and transmitted event-by-event rather than requiring storage of large data batches.
Solution Approach 2:
The sensor device performs preliminary classification of biosignals to identify abnormal events before transmission. This real-time event detection and immediate transmission approach eliminates the need for localized data batching and large memory capacity, as each event is processed and transmitted independently as it occurs.
Data Source
AI summary
Technologies for filtering biosignals include one or more biosignal sensors coupled to a user to receive biosignals and a computing device to receive biosignals from the biosignal sensors. The biosignal sensors filter the received biosignals to identify abnormal biosignals using a plurality of domain filters including a time domain filter and a frequency domain filter. The biosignals identified as abnormal by each of the domain filters are transmitted to the computing device, while the remaining biosignals are discarded.