Context Health Profiles via Distributed Sensor Fusion
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Solution Overview
Problem
Conventional systems using stationary sensors are limited in their ability to determine characteristics of people and environments due to restricted sampling, range, and accuracy, leading to potentially spurious results when estimating mood or other attributes in large areas, as they only capture a limited sample of individuals within their range and may miss the sentiments of those avoiding the sensors.
Innovation Solution
The implementation of distributed sensor-enabled electronic devices, both mobile and stationary, that collect and analyze health and context data to generate comprehensive health profiles for specific contexts, allowing for flexible definitions of spatial and temporal components, thereby providing a more accurate representation of the characteristics of a given area by integrating data from multiple sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If stationary sensors are used to determine characteristics of people in a location, then the system can detect physical properties of people within sensor range, but the sampling is limited and results may be spurious when interpolating characteristics for the entire location
Solution Approach 1:
The system segments the population into multiple groups based on detected characteristics (e.g., mood, health status, demographics) and uses separate sensors or sensor combinations for different characteristic types. This allows each sensor to specialize in detecting specific characteristics while collectively covering the entire population, thereby improving both measurement precision and sample size coverage.
Solution Approach 2:
The patent employs universal sensors capable of detecting multiple characteristics simultaneously (e.g., cameras that can detect both facial expressions for mood and physical features for demographics). This multi-functionality increases the effective sample size without requiring additional sensors, while maintaining measurement precision through specialized analysis algorithms for each characteristic type.
2Device complexity
If stationary sensors are deployed in limited locations, then the device complexity is reduced, but the area coverage and representativeness of the sample are insufficient
Solution Approach 1:
The system transitions from a two-dimensional spatial deployment of stationary sensors to a three-dimensional coverage model by incorporating temporal dimension. Sensors collect data over extended time periods, and the system interpolates characteristics across both space and time, effectively expanding coverage area without adding physical sensors to every location.
Solution Approach 2:
The patent uses digital replicas and virtual models to extend sensor coverage. Virtual sensors are created through data interpolation and machine learning models that replicate the functionality of physical sensors in areas where no physical sensors exist, thereby expanding effective coverage area without increasing physical device complexity.
3Ease of operation
If stationary sensors are positioned at specific locations (e.g., front door, stage), then the sensors can capture characteristics of people at those points, but people avoiding those locations are not represented in the sample
Solution Approach 1:
The system dynamically adjusts sensor positioning and detection parameters based on real-time data flow and population movement patterns. Sensors can change their field of view, detection ranges, and target characteristics dynamically to ensure continuous representation of the population, preventing systematic biases from developing as people adapt to or avoid fixed sensor locations.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors the representativeness of its sample and adjusts sensor deployment or data weighting accordingly. When certain population segments are underrepresented, the system can trigger additional data collection efforts, adjust interpolation algorithms, or reposition sensors to improve coverage, thereby maintaining result reliability while keeping operational complexity manageable.
Data Source
AI summary
Systems, methods, and devices for determining contexts and determining associated health profiles using information received from multiple health sensor enabled electronic devices, are disclosed. Contexts can be defined by a description of spatial and/or temporal components. Such contexts can be arbitrarily defined using semantically meaningful and absolute descriptions of time and location. Health sensor data is associated with or includes context data that describes the circumstances under which the data was determined. The health sensor data can include health sensor readings that are implicit indications of health for the context. The sensor data can also include user reported data with explicit descriptions of health for the context. The health sensor data can be filtered by context data according a selected context. The filtered sensor data can then be analyzed to determine a health profile for the context that can be output to one or more users or entities.