Work-Zone Occupancy Tracking With Sensor Bias Correction
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Solution Overview
Problem
Existing workplace monitoring systems struggle to accurately track and recalibrate the quantity of objects, such as humans, within a space due to sensor biases and inconsistencies, leading to inaccurate occupancy counts.
Innovation Solution
A method involving sensor blocks that detect entry and exit events, utilize wireless connectivity data, and derive occupancy bias functions to correct uncorrected counts, enabling real-time updates of occupancy representations on displays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor blocks are deployed to track entry and exit events, then occupancy tracking capability is improved, but sensor biases and inconsistencies cause measurement inaccuracies
Solution Approach 1:
The system continuously compares uncorrected occupancy counts from sensor blocks against baseline occupancy counts and uses the derived occupancy bias functions to adjust future measurements. This closed-loop feedback mechanism systematically corrects sensor biases and inconsistencies, transforming unreliable raw data into accurate occupancy information.
Solution Approach 2:
The patent transforms raw occupancy counts by applying occupancy bias functions that adjust measurement parameters. By changing the parameter representation from uncorrected counts to bias-corrected counts, the system compensates for sensor inconsistencies and achieves accurate occupancy tracking despite individual sensor reliability issues.
2Measurement precision
If occupancy bias functions are derived and applied to correct counts, then measurement precision is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by deriving occupancy bias functions during baseline periods before actual occupancy tracking begins. By pre-calculating and storing these correction functions, the system avoids complex real-time calculations during operational phases, simplifying the overall system architecture while maintaining high measurement precision.
Solution Approach 2:
The patent creates simplified copies of occupancy data by deriving representative bias functions that capture sensor behavior patterns. Instead of processing every individual sensor reading through complex algorithms, the system uses these copied bias characteristics to efficiently correct occupancy counts, reducing computational complexity while preserving accuracy.
Data Source
AI summary
A method for tracking objects entering and exiting a work zone includes: accessing a first set of entry and exit events recorded by a first sensor block during a first time period; deriving a first uncorrected occupancy count for the work zone based on the first set of entry and exit events; accessing a baseline occupancy count; deriving an occupancy bias function for the work zone based on a difference between the first uncorrected occupancy count and the baseline occupancy count; accessing a second set of entry and exit events recorded by the first sensor block, during a second time period; deriving a second uncorrected occupancy count for the work zone based on the second set of entry and exit events; and correcting the second uncorrected occupancy count according to the occupancy bias function to calculate a second corrected occupancy count for the work zone.