RF Signal Association for Accurate Occupancy Sensing
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Solution Overview
Problem
Existing occupancy sensing methods lack sufficient coverage, accuracy, and resolution, often requiring costly specialized sensors that may not provide reliable occupancy data.
Innovation Solution
A system that associates RF signals with individuals using existing components like cameras, wireless signal readers, and user devices, employing machine learning models to determine occupancy and density through unsupervised learning, reducing reliance on specialized sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized sensors are used for determining occupancy, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent combines multiple existing sensing modalities (RF signal detection, camera-based presence detection, wireless signal reading) into a unified occupancy determination system. Instead of relying on a single specialized sensor, the system merges data from multiple conventional components to achieve accurate occupancy measurement while avoiding the complexity and cost of dedicated specialized sensors.
Solution Approach 2:
The system employs multi-functional components that serve multiple purposes. For example, RF signals are used both for their intended communication function and for occupancy detection. Camera systems serve both security/surveillance purposes and occupancy sensing. This multi-functionality reduces the need for specialized single-purpose sensors, thereby decreasing device complexity while maintaining measurement precision.
2Measurement precision
If multiple sensing methods are used to assess occupancy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent integrates multiple sensing methods (RF signal analysis, optical camera detection, wireless communication signal monitoring) into a cohesive system. The fusion of these different sensing modalities allows the system to overcome the limitations of individual methods, achieving higher occupancy assessment accuracy while managing complexity through unified processing architecture.
Solution Approach 2:
The system introduces machine learning models and data fusion algorithms as intermediaries that process and integrate data from multiple sensing methods. These intermediary processing layers harmonize the diverse data streams from different sensors, enabling accurate occupancy determination without requiring direct complex interconnections between all sensing components.
3Device complexity
If existing infrastructure is leveraged for occupancy sensing, then device complexity is reduced, but measurement precision may deteriorate
Solution Approach 1:
The patent demonstrates that existing infrastructure components (RF transceivers, cameras, wireless access points) can be repurposed for occupancy sensing while maintaining their primary functions. By extracting occupancy-related information from these multi-functional components, the system achieves adequate measurement precision without requiring specialized dedicated sensors, thus reducing device complexity.
Solution Approach 2:
The system changes the operational parameters and analysis methods of existing infrastructure components to enable occupancy detection. For example, RF signals are analyzed not just for communication but also for presence detection based on signal characteristics. Camera systems are configured to detect occupancy patterns rather than just visual content. These parameter changes allow existing infrastructure to provide sufficient occupancy detection accuracy.
Data Source
AI summary
Systems and methods for determining occupancy. In some embodiments, a system comprises at least one processor and memory storing instructions executable by the at least one processor, the instructions when executed cause the system to: obtain information related to presence of a user within a space; obtain information related to one or more RF signals within the space; associate the one or more RF signals with the user based on the information related to presence of the user and information related to the one or more RF signals; and determine presence of the user based on the association of the user with the one or more RF signals.