Context-Aware Wellness System for Biometric Activity Adaptation
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Solution Overview
Problem
Existing health and wellness recommendation systems fail to automatically consider a user's current physical situation and health conditions when suggesting physical activities, often leading to overexertion or under-exertion, and lack the ability to adapt based on real-time biometric and motion data from mobile devices.
Innovation Solution
A method and system that utilizes sensors to collect and process biometric and motion data to identify the user's activity and health condition, providing personalized recommendations to enhance wellness, including reminders to modify activity levels or reduce risk, and allowing manual override in scenarios like meetings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing health recommendation systems provide generic physical activity recommendations, then users receive simple activity suggestions, but the recommendations do not consider user's current physical situation and health conditions leading to overexertion or under-exertion
Solution Approach 1:
The system continuously monitors user biometric data (heart rate, temperature, activity levels) and provides feedback loops that adjust recommendations in real-time based on the user's physiological state, ensuring recommendations adapt to current physical conditions without requiring complex manual assessment
Solution Approach 2:
The patent integrates multiple sensor types (accelerometer, gyroscope, biometric sensors) into a single unified system that performs multiple functions: activity detection, health monitoring, and recommendation generation, reducing overall system complexity while improving adaptability
2Ease of operation
If activity recommendation systems continuously monitor and notify users, then users receive timely wellness recommendations, but the system cannot be disabled when desirable in scenarios like meetings
Solution Approach 1:
The system automatically detects meeting scenarios through context analysis (location, calendar data, motion patterns) and autonomously adjusts notification behavior without requiring manual user intervention, maintaining high automation while respecting user context
Solution Approach 2:
The notification system dynamically adjusts its behavior based on detected context, transitioning between active recommendation mode and passive monitoring mode depending on the situation, allowing seamless adaptation to user needs without manual configuration
3Extent of automation
If simple alarm tools are used for activity reminders, then users receive basic reminders, but the system requires manual setup and does not consider user's physical situation or past activity history
Solution Approach 1:
The system pre-configures recommendation algorithms with access to user health profiles, activity history, and sensor data streams, so that when activity detection occurs, recommendations are immediately generated based on accumulated contextual information without manual setup
Solution Approach 2:
The patent implements nested data structures where activity history, biometric data, and contextual information are layered within each other, allowing the system to automatically access and process multiple levels of user information simultaneously to generate context-aware recommendations
Data Source
AI summary
A device, method and system processes data about the user for determining and recommending a health/wellness action to the user. The action determined can be based on: (1) current biometric and/or motion data about the user (from the sensors), and (2) current health/medical information or condition about the user (from the user's personal information, e.g., health library or programmed into the smartphone). Specific information about the user is taken into consideration when recommending user action, such as the user's specific health or medical conditions.