Biological Data Sensing With Radio Waves for Motion-Resistant Accuracy
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Solution Overview
Problem
Existing wearable devices for measuring biological data, such as heartbeats, are limited in their ability to accurately capture and analyze biological signals in dynamic environments, particularly when the user is moving or in the presence of external disturbances.
Innovation Solution
A biological data obtaining system that utilizes wide band or ultra wide band radio waves to transmit and receive reflected waves, which are processed to generate time-series fluctuation data, corrected for movement and external influences, and analyzed to extract accurate biological data like respiration and pulsation, using multiple resolution analysis to separate relevant signals from noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wearable devices are used to measure biological data, then the measurement can be performed on the user, but the measurement accuracy deteriorates in dynamic environments and when the user is moving
Solution Approach 1:
The patent replaces mechanical contact-based measurement (wearable devices touching the body) with electromagnetic wave-based measurement (radio waves reflecting off the body). This substitution eliminates the mechanical constraints and movement limitations of wearable devices, allowing accurate biological data measurement even when the user is moving or in dynamic environments.
Solution Approach 2:
The patent introduces radio waves as an intermediary medium to transmit biological information from the user's body to the measurement system. Instead of direct contact measurement, the radio waves reflect off the body and carry physiological signals (heartbeat, respiration) to the receiver, enabling non-contact and movement-resistant measurement.
2Device complexity
If conventional measurement methods are used, then the device structure is simple, but the ability to capture biological signals in dynamic environments deteriorates
Solution Approach 1:
The patent employs dynamic signal processing techniques including multiple resolution analysis and wavelet transforms to adaptively process signals in real-time. The system dynamically adjusts to changing environmental conditions and user movements, maintaining reliable biological signal capture despite the increased processing complexity.
Solution Approach 2:
The patent segments the received radio wave signals into different frequency components and time scales using multiple resolution analysis. This segmentation allows the system to separately analyze different physiological signals (heartbeat, respiration) and filter out noise, improving reliability without requiring overly complex hardware.
3Measurement precision
If multiple resolution analysis is applied to separate signals from noise, then the signal separation accuracy is improved, but the data processing complexity increases
Solution Approach 1:
The patent applies periodic wavelet transforms and multiple resolution analysis at different decomposition levels to systematically separate signals from noise. This periodic, multi-level processing approach achieves high signal separation accuracy by progressively filtering different frequency components, managing processing complexity through structured iterative analysis.
Solution Approach 2:
The patent changes processing parameters such as wavelet decomposition levels, frequency thresholds, and analysis windows to optimize signal separation. By adjusting these parameters based on the specific measurement conditions, the system achieves high accuracy while controlling processing complexity through adaptive parameter selection.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively captures and analyzes biological data with reduced noise, enabling precise measurement of pulsation and respiration even in dynamic conditions, improving accuracy and reliability.
Implementation Method 1
the reflected waves are wide band radio waves or ultra wide band radio waves
Data Source
AI summary
A biological data obtaining device includes a storage unit, a first generation unit, and a second generation unit. The storage unit is configured to store time-series data in which first to Nth distance-based fluctuation data are arranged. The first to Nth distance-based fluctuation data are obtained based on reflected waves which are reflected from a living body at different times, wherein nth distance-based fluctuation data indicates changes in signal strength with respect to distance. The first generation unit is configured to generate time-based fluctuation data by performing strength obtaining process. The strength obtaining process includes obtaining one corresponding strength information, wherein the one corresponding strength information is a signal strength based on reflected waves from a predetermined detection part of the living body. The second generation unit is configured to generate biological data of the detection part of the living body based on the time-based fluctuation data.