Context Emotion Determination Using Distributed Sensor Networks
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Solution Overview
Problem
Conventional systems using stationary sensors are limited in determining characteristics of people and environments due to restricted sampling, range, and accuracy, leading to potentially spurious results that do not accurately represent the overall mood or conditions in a given location.
Innovation Solution
The use of distributed sensor-enabled electronic devices, including both mobile and stationary devices, to collect and analyze emotion sensor data from multiple contexts, allowing for the definition of contexts by spatial and temporal components, and generating emotion profiles that provide a more comprehensive understanding of emotions and characteristics in various settings.
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 and determine characteristics for people within sensor range, but the sampling is limited and results may be spurious and not represent the overall mood or conditions accurately
Solution Approach 1:
The system segments the population into multiple groups based on demographics, behaviors, and contexts. Instead of treating all people in a location as a single sample, the system divides them into segments (e.g., by age, gender, time of day, location within venue) and determines characteristics for each segment separately. This allows comprehensive coverage of the entire population while maintaining adequate sample sizes within each segment, resolving the contradiction between accuracy and sample size.
2Measurement precision
If stationary sensors are deployed in limited locations, then the system can determine characteristics for specific locations, but other locations without sensors cannot have their characteristics determined
Solution Approach 1:
The system creates a universal framework that can determine characteristics across multiple locations and contexts using the same underlying technology. By defining contexts in terms of spatial and temporal parameters that can be applied anywhere, the system achieves both precision in measurement and versatility in coverage. The context-based approach allows the same sensor system to accurately determine characteristics in diverse locations by adapting to local conditions through context definition.
3Measurement precision
If stationary sensors are positioned at specific points (e.g., front door, stage), then the system can capture facial expressions at those points, but people in other areas (e.g., throughout the venue) cannot be captured
Solution Approach 1:
The system transitions from purely spatial sampling to spatio-temporal sampling by incorporating time as an additional dimension. Instead of relying solely on the spatial distribution of stationary sensors, the system uses temporal contexts (times of day, duration of events, sequences of activities) to expand coverage. This allows the system to accurately determine characteristics across the entire venue area by considering when and how long people are in different locations, effectively adding a temporal dimension to compensate for limited spatial sensor coverage.
Data Source
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AI summary
Systems, methods, and devices for determining contexts and determining associated emotion profiles using information received from multiple emotion 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 times and locations. Emotion sensor data is associated with or includes context data that describes the circumstances under which the data was determined. The emotion sensor data can include emotion sensor readings that are implicit indications of an emotion for the context. The sensor data can also include user reported data with explicit descriptors of an emotion for the context. The emotion sensor data can be filtered by context data according a selected context. The filtered sensor data can then be analyzed to determine an emotion profile for the context that can be output to one or more users or entities.