Floor Seismometer Network for Privacy-Preserving Occupancy Tracking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing indoor monitoring systems infringe on privacy by requiring surveillance cameras to track individuals, and they lack effective methods for accurately tracking footstep-induced vibrations in indoor environments, especially in areas with low signal-to-noise ratios and complex backgrounds.
Innovation Solution
A network of seismometer sensors installed on indoor flooring records footstep-induced vibrations, using wavelet denoising and phase-constrained angle of arrival techniques to isolate and track footstep signatures, enabling occupancy estimation and trajectory tracking without visual surveillance, and generating alerts for events like falls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vision-based surveillance systems are used to track individuals, then trajectory tracking capability is improved, but individual privacy is compromised
Solution Approach 1:
The patent replaces vision-based surveillance systems with a seismometer-based mechanical vibration detection system. Seismometers installed in the floor detect footstep-induced vibrations and process this data to track individual trajectories, thereby achieving the same functional goal without visual surveillance and preserving individual privacy.
2Measurement precision
If traditional acoustic or vibration sensing is used in noisy environments, then occupancy detection is attempted, but measurement precision deteriorates due to low signal-to-noise ratios
Solution Approach 1:
The patent divides the monitoring area into multiple zones with distributed seismometer sensors. Each sensor captures localized vibration data, and the system processes these segmented signals individually before integrating them for comprehensive occupancy detection. This segmentation approach enhances signal-to-noise ratio by focusing on local footstep patterns rather than processing entire environment noise.
Solution Approach 2:
The patent introduces wavelet transform as an intermediary signal processing technique between the raw vibration signals and the final occupancy detection. The wavelet transform effectively separates footstep-induced vibrations from background noise by transforming the signals into time-frequency domain, thereby improving measurement precision in noisy environments.
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
This solution maintains individual privacy while accurately tracking footstep movements and detecting events, providing effective occupancy estimation and trajectory analysis, with improved accuracy in noisy indoor environments and the ability to alert for potential hazards like falls.
Implementation Method 1
Human footsteps induce the floor vibrations. A seismometer is a sensor that can sense floor vibrations.
Implementation Method 2
wavelet denoising and phase-constrained angle of arrival techniques to isolate and track footstep signatures
Implementation Method 3
wavelet denoising and phase-constrained angle of arrival techniques to isolate and track footstep signatures
Data Source
Figure 1
Figure 2~3
Figure 4~5
AI summary
The present disclosure relates to a monitoring system configured to monitor the activities of individuals without having to keep the surrounding area under the surveillance of a camera thereby maintaining the privacy of the individual. In particular, the monitoring system includes a network of sensitive sensor units that are installed onto indoor flooring to record human footstep induced vibrations. The data collected from the sensors can then be processed to identify individual occupants, determine the number of occupants, estimate the location of footsteps, and track the trajectory of each occupant. The extracted trajectory information can be used to assess an occupant's personal activity and social interaction, which can then be used to analyze the individual's physical and psychological health.