Presence Detection Using Time Series Analysis Models
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Solution Overview
Problem
Existing noncontact activity sensors often inaccurately determine whether a human is present or absent in a space, especially when the individual is at rest, due to temporary variations in measurement data.
Innovation Solution
A presence/absence detection method that includes acquisition processing, time series analysis processing, and decision processing, using a measuring unit to acquire data on physical quantities varying with human presence, and employing an analysis model to improve decision accuracy by reducing the impact of temporary variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If presence/absence detection is based on amplitude and frequency of Doppler sensor detection signal, then the detection method is simple, but the accuracy deteriorates when the human is at rest
Solution Approach 1:
The system performs preliminary time series analysis on detection signals before making presence/absence decisions. By analyzing temporal patterns and trends in advance, the system can distinguish between temporary signal variations and actual presence/absence states, improving accuracy without requiring complex additional hardware
Solution Approach 2:
The patent introduces an intermediate processing layer that analyzes the relationship between multiple detection signals (Doppler sensor, distance sensor, room temperature sensor) before determining presence/absence. This intermediary analysis layer reconciles conflicting signals and reduces erroneous decisions while maintaining relatively simple device architecture
2Measurement precision
If time series analysis with analysis model is used for presence/absence detection, then the detection accuracy is improved, but the processing complexity increases
Solution Approach 1:
The time series analysis is segmented into distinct processing stages: signal acquisition, preliminary filtering, pattern recognition, and decision-making. Each stage processes only relevant data with specific algorithms, reducing overall computational complexity while maintaining high detection accuracy through systematic breakdown of the analysis process
Solution Approach 2:
The system dynamically adjusts analysis parameters such as time windows, threshold values, and weighting factors based on current detection conditions. By changing parameters adaptively rather than using fixed complex models, the system achieves high accuracy with manageable processing requirements
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 method significantly enhances the accuracy of presence/absence detection by using time series analysis and decision conditions based on analysis model coefficients, effectively distinguishing between human presence and absence even when the individual is at rest.
Implementation Method 1
a Doppler sensor (measuring unit), a distance sensor, and a processor. The processor calculates the volume of activity of a subject
Implementation Method 2
a Doppler sensor (measuring unit), a distance sensor, and a processor
Data Source
AI summary
Disclosed herein is a sensor processing system including an acquisition unit, a time series analysis unit, and a decision unit. The acquisition unit acquires measurement data from a measuring unit. The measuring unit measures a physical quantity, of which a value varies depending on whether a human is present in, or absent from, an object space. The time series analysis unit obtains an analysis model for a time series analysis in which the measurement data acquired at a predetermined timing is represented by multiple items, acquired before the predetermined timing, of the measurement data. The decision unit decides, depending on a decision condition including a condition concerning a coefficient of the analysis model, whether the human is present or absent at the predetermined timing.