Capacitive Biometric Sensor with Multiplexed Signal Processing
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Solution Overview
Problem
Current biometric sensing technologies face challenges in accurately and efficiently capturing biometric data, particularly in non-contact scenarios and with complex signal processing, using capacitive sensors that require improved multiplexing schemes and signal analysis methods.
Innovation Solution
The development of capacitive-based sensors employing frequency-division multiplexing (FDM), code-division multiplexing (CDM), or hybrid modulation techniques, combined with signal infusion methods, to create heatmaps of capacitance changes and proximity data, utilizing mixed signal integrated circuits for signal processing and Fourier transforms to analyze orthogonal signals from transmitting and receiving antennas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If capacitive sensors are used for biometric sensing, then contactless measurement is enabled, but signal processing complexity increases
Solution Approach 1:
The sensor array is divided into multiple independently controllable sensor elements arranged in a grid pattern. Each sensor element can be individually activated and processed, allowing the complex signal processing task to be segmented into smaller, manageable units that can be handled separately through multiplexing schemes.
Solution Approach 2:
The patent employs periodic scanning of sensor elements in a time-division multiplexing approach, where sensor elements are activated in sequential groups during different time intervals. This periodic activation pattern simplifies the simultaneous processing of multiple sensors by converting spatial complexity into temporal sequencing.
2Measurement precision
If multiple sensor elements are used to improve measurement accuracy, then biometric data precision increases, but processing time increases
Solution Approach 1:
Sensor elements are activated in periodic time intervals using time-division multiplexing, where different groups of sensors are scanned during different time slots. This allows multiple sensors to be processed sequentially rather than simultaneously, maintaining measurement precision while reducing overall processing time through efficient time management.
Solution Approach 2:
The patent implements dynamic control of sensor activation states, where sensor elements can be selectively enabled or disabled based on measurement requirements. This dynamic approach allows the system to adjust the number of active sensors according to the specific biometric parameter being measured, optimizing the balance between precision and processing speed.
3Productivity
If frequency-division multiplexing is implemented to reduce processing time, then measurement efficiency improves, but signal analysis complexity increases
Solution Approach 1:
The patent assigns different frequency modulations to different sensor elements or sensor groups, allowing simultaneous transmission of multiple sensor signals at distinct frequencies. This frequency-division approach enables parallel processing of multiple sensors without temporal sequencing, improving measurement efficiency while the frequency encoding provides a structured method for signal separation that manages analysis complexity.
4Speed
If sensor elements are activated simultaneously to improve processing speed, then measurement speed increases, but signal separation becomes difficult
Solution Approach 1:
The patent employs frequency modulation as a distinguishing parameter for simultaneous sensor signals, where each sensor element or group is assigned a unique frequency signature. This allows multiple sensors to operate simultaneously at different frequencies, maintaining high processing speed while enabling easy signal separation through frequency-based filtering and demodulation techniques.
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
Enables accurate and efficient biometric data capture, including non-contact interactions, with enhanced signal processing capabilities, allowing for precise measurement of biometric activities such as heart rate, circulatory, and respiratory activities, and other physiological parameters.
Implementation Method 1
utilizing mixed signal integrated circuits for signal processing and Fourier transforms to analyze orthogonal signals from transmitting and receiving antennas
Implementation Method 2
The sensor configurations are suited for use with frequency-orthogonal signaling techniques and employ capacitive sensors
Data Source
AI summary
A biometric sensing apparatus is employed by a person in order to obtain biometric data regarding the person. Transmitting and receiving antennas are used in order to transmit and receive signals. Measurements of the received signals are correlated with biological activity in order to provide biometric data for the person.