Wearable Health Monitoring System for Panic Attack Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current remote health monitoring systems fail to provide adequate support to users by only presenting healthcare data without offering personalized recommendations, often requiring intrusive equipment that is not suitable for everyday life, and are unable to predict impending panic attacks effectively.
Innovation Solution
A method and system that uses a wearable device to collect real-time physiological data, generates a personalized prediction model by analyzing this data, and sends real-time recommendations to the user's mobile device based on contextual factors to predict and manage panic attacks and other health conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If current remote health monitoring systems present healthcare data without personalized recommendations, then the system structure is simple, but the user support adequacy deteriorates
Solution Approach 1:
The system performs preliminary actions by generating personalized prediction models during off-peak times using historical data, then applies these models to real-time data processing. This allows the system to provide adequate personalized support without increasing operational complexity during critical moments.
Solution Approach 2:
The patent introduces an intermediary processing layer that includes prediction models and change point detection algorithms. This intermediary layer transforms raw healthcare data into actionable personalized recommendations, bridging the gap between simple data presentation and complex personalized support.
2Measurement precision
If multiple types of intrusive physiological monitoring equipment are required, then the measurement precision is improved, but the ease of operation deteriorates
Solution Approach 1:
The patent merges multiple physiological monitoring functions into a single integrated wearable device. By combining heart rate monitoring, respiratory rate detection, and activity tracking in one device, the system maintains measurement precision while eliminating the need for multiple intrusive equipment pieces.
Solution Approach 2:
The wearable device is designed with universal functionality to monitor multiple physiological parameters simultaneously. This multi-functional approach allows the device to replace several specialized monitoring equipment pieces while maintaining comprehensive measurement capabilities.
3Reliability
If real-time physiological data analysis is performed to predict panic attacks, then the reliability is improved, but the use of energy deteriorates
Solution Approach 1:
The system performs preliminary energy-efficient actions by pre-processing historical data to create personalized prediction models and threshold parameters. During real-time operation, the system only needs to compare current readings against these pre-established criteria, significantly reducing instantaneous energy consumption while maintaining high prediction reliability.
Solution Approach 2:
The patent applies partial action by monitoring only the most critical physiological parameters relevant to panic attack detection rather than continuously analyzing all possible biological signals. This selective monitoring approach maintains reliable prediction capability while minimizing energy consumption.
4Reliability
If personalized prediction models are generated using historical data, then the reliability is improved, but the loss of time deteriorates
Solution Approach 1:
The system performs the time-consuming model generation action during off-peak periods when computational resources are available and user attention is not required. By completing the heavy lifting of historical data analysis in advance, the system can provide rapid real-time predictions without increasing perceived user waiting time.
Solution Approach 2:
The prediction models are generated autonomously by the system using its own historical data without requiring user intervention or manual input. This self-service approach eliminates the time users would otherwise need to spend on data entry and model configuration, effectively reducing perceived time loss.
Data Source
Figure 1
Figure 2
Figure 3A~3B
AI summary
A method and system for generating a personalized health management recommendation for a user. During operation, the system obtains first physiological data generated by a wearable device worn by the user that indicates a physiological condition of the user. The system then generates a prediction model for the user based on the first physiological data. Next, the system obtains real-time physiological data generated by the wearable device. The system may generate a prediction by analyzing the real-time physiological data to determine whether the user's physiological condition exceeds a threshold parameter according to the prediction model. Upon determining that the threshold parameter has been exceeded, the system may select a recommendation and send the recommendation message to the user's mobile device.