Passive Human Detection Using Ambient EMF and Low-Frequency Filtering
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Solution Overview
Problem
Current methods fail to passively and non-invasively detect and classify human-specific signals, such as electric field patterns, which are essential for monitoring physiological conditions and presence, especially in environments where contact or differential signals are not feasible.
Innovation Solution
A system utilizing a multi-purpose, very low frequency sensor that detects 'Traveling Voltage Gradient' (TVG), 'Magnetic Field' (M-Field), or 'Electric Field' (E-Field) signals, optimized to operate at 50 Hz or less, with bandpass filters to attenuate ambient noise, and capable of providing digital outputs for signal processing and display, allowing for passive detection and classification of human-specific signals without emitting or creating reference signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If contact-based or differential signal detection methods are used, then measurement precision may be improved, but ease of operation and non-invasiveness deteriorate
Solution Approach 1:
The patent uses ambient electromagnetic fields as an intermediary medium to detect human presence and physiological signals without direct contact. The system detects modifications in existing EMF caused by the human body's electrical activity, eliminating the need for skin contact while maintaining detection capability through field-based measurement
2Measurement precision
If active signal emission is used to improve detection capability, then measurement precision improves, but loss of energy increases and the system becomes more intrusive
Solution Approach 1:
The system utilizes naturally occurring ambient electromagnetic fields as the detection medium, requiring no active signal transmission or energy-emitting components. The ambient EMF serves itself as both the carrier and the measurement target, eliminating energy loss from signal generation while enabling passive detection of human-specific electrical patterns
3Measurement precision
If ambient noise is not filtered, then ease of operation is maintained, but measurement precision deteriorates
Solution Approach 1:
The patent applies frequency-selective filtering targeted at the specific ULF band (1-2 Hz) where human physiological signals occur. Rather than broad-spectrum noise reduction, the system implements localized filtering at the critical frequency range, maintaining signal integrity while reducing unnecessary processing complexity
4Ease of operation
If contactless detection is implemented, then ease of operation and non-invasiveness improve, but measurement precision and reliability deteriorate
Solution Approach 1:
The system detects contactless physiological signals by monitoring parameter changes in ambient electromagnetic fields caused by human body electrical activity. It measures frequency, amplitude, and temporal patterns of field modifications, translating subtle field variations into reliable physiological data without physical contact
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 achieves accurate classification of human-specific signals with over 98% accuracy, distinguishing humans from non-humans and monitoring physiological conditions like heart rate and respiration, even through dielectric materials, offering a non-invasive and contactless monitoring solution.
Implementation Method 1
The human heart generates on the surface of the living humans skin, a coherent dynamic pulse... The major source of the electric field is the polarization, rapid depolarization and repolarization of the heart
Implementation Method 2
A system utilizing a multi-purpose, very low frequency sensor that detects 'Traveling Voltage Gradient' (TVG), 'Magnetic Field' (M-Field), or 'Electric Field' (E-Field) signals
Implementation Method 3
The Spatial change and the delta function of the action potentials at this point create extremely large volts per cm per cm per second change in the electric field
Implementation Method 4
optimized to operate at 50 Hz or less, with bandpass filters to attenuate ambient noise
Data Source
AI summary
A low input current amplifier has a voltage spectral density curve to operate at 50 Hz or less and is connected to dielectric materials to receive a signal irrespective of ground reference. The amplifier outputs a first output. A multi-stage amplifier includes a stage connected in series with the low input current amplifier to amplify the first signal to distinguish the incidence, traverse and physiological condition of a living human. The resulting signal is then processed by an algorithm and displayed as human specific motion, heart rate and respiratory rate.