Wearable Exertion Recommendation System Using Biometric Response Profiles
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current fitness tracking devices lack the ability to provide a precise and personalized measure of exertion and response profile during and after exercise sessions, and do not offer tailored exertion recommendations for future sessions based on prior measures.
Innovation Solution
The development of systems and methods that utilize biometric and activity data from wearable devices like wristbands and earphones to calculate exertion levels and performance capacity, enabling users to receive personalized exertion recommendations for future exercise sessions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional fitness tracking devices monitor basic biometric data, then users can track their exercise activity, but they cannot receive precise and personalized exertion recommendations based on their individual response profile
Solution Approach 1:
The system segments the exertion measurement process into distinct components: biometric data collection (heart rate, HRV), activity data collection (motion sensors), response profile calculation, and exertion recommendation generation. This segmentation allows each component to be optimized independently while maintaining overall system precision.
Solution Approach 2:
The patent introduces a processor as an intermediary that receives raw biometric and activity data, calculates the user's response profile, and generates personalized exertion recommendations. This intermediary layer transforms basic monitoring data into actionable insights without requiring direct complexity in the sensor components.
2Loss of information
If fitness devices provide general exercise tracking, then users can monitor their activity levels, but they cannot assess the impact of activities on their physical condition or receive tailored exertion guidance
Solution Approach 1:
The system performs preliminary calculation of the user's response profile based on their biometric and activity data before generating exertion recommendations. This preliminary action ensures that all necessary information is processed and stored in advance, enabling comprehensive assessment without adding real-time processing complexity during exercise sessions.
Solution Approach 2:
The patent implements a feedback mechanism where the calculated response profile is used to generate personalized exertion recommendations that are fed back to the user. This feedback loop allows the system to continuously refine its understanding of the user's physical condition and provide increasingly accurate guidance without requiring additional hardware complexity.
3Adaptability or versatility
If the system calculates personalized exertion levels based on individual characteristics, then users receive accurate exertion recommendations, but the system requires extensive biometric and activity data collection
Solution Approach 1:
The system applies local quality by focusing data collection on specific biometric parameters most relevant to exertion assessment (heart rate, heart rate variability) rather than collecting all possible health data. This targeted approach enables personalization while minimizing the quantity of data that needs to be collected and processed.
Data Source
AI summary
Systems, methods, and devices are provided for determining an exertion recommendation for an anticipated exercise session. One such system includes a wearable device comprising a biosensor that monitors biometrics (e.g. heart rate); a motion sensor that monitors activity; a processor operatively coupled to the biosensor, the processor configured to process electronic signals periodically generated by the biosensor and the motion sensor; and a non-transitory computer-readable medium operatively coupled to the processor and storing instructions that, when executed, cause the processor to execute specific functions. In particular, the instructions are executed to cause the processor to generate biometric data from the biometrics (e.g. heart rate information in particular). Further, the instructions are executed to generate an exertion recommendation based on an exertion model created from and representing a relationship between prior exertion measures and prior response profile measures.