Sensor-Based Trauma Detection with Real-Time Stress Alerts
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Solution Overview
Problem
Current technologies lack effective methods to detect and respond to trauma events in real-time, particularly for first responders and other professionals, to prevent negative repercussions and provide personalized interventions based on user data.
Innovation Solution
A system utilizing network-connected sensor devices, including cameras and microphones, to detect trauma events through classification models, determine a stress score using a prediction model, and generate alerts or automated interventions when the score exceeds a threshold, incorporating user data such as biometric, calendar, and environmental data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time trauma event detection systems are implemented using sensor devices and classification models, then the ability to detect and respond to trauma events is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The system segments the detection process into multiple independent components: sensor devices for data collection, classification models for trauma event identification, and stress prediction models for risk assessment. Each component processes specific types of data independently before integrating results, reducing overall system complexity while maintaining high detection reliability.
Solution Approach 2:
The patent introduces an intermediary processing layer that aggregates data from multiple sensor devices and preliminary classification results before feeding them into the final stress prediction model. This intermediary layer simplifies the interaction between complex components by providing a standardized interface and consolidated input data structure.
2Measurement precision
If multiple sensor devices and data sources are integrated to improve detection accuracy, then the measurement precision of trauma events is improved, but the loss of time for data processing and analysis increases
Solution Approach 1:
The system performs preliminary actions by pre-processing sensor data in real-time as it is collected, and by having pre-trained classification models ready to immediately analyze trauma event patterns. This preliminary processing reduces the time required for final analysis and enables faster response while maintaining high accuracy through the stress prediction model's comprehensive data integration.
Data Source
AI summary
A processing system including at least one processor may obtain data associated with a user, the data associated with the user including at least one of: visual data captured via at least one camera associated with the user or audio data captured via at least one microphone associated with the user, detect at least one trauma event of at least one defined trauma event type in at least one of the visual data or the audio data via at least one classification model, determine, responsive to detecting the at least one trauma event, a stress score based upon at least a portion of the data associated with the user in accordance with a stress prediction model, and generate an alert in response to the stress score exceeding a threshold.