Wearable Biosensor System for Predicting User Performance Capacity
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Solution Overview
Problem
Current fitness monitoring devices lack the capability to model and predict a user's performance capacity based on biometric and activity data, leading to suboptimal training load determination and overall fitness and well-being.
Innovation Solution
A system comprising wearable biosensors and motion sensors, coupled with processors, that generate biometric and activity data to create response profiles, including HRV and fatigue scores, to intelligently calculate predicted responses to training loads, enabling personalized training load determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current fitness monitoring devices track user activity and biometric data, then basic fitness tracking is achieved, but the devices cannot predict user response to training loads or determine optimal training loads
Solution Approach 1:
The system performs preliminary actions by collecting and storing biometric data (heart rate, HRV) and activity data over time before training sessions. This historical data is used to build individualized response profiles that predict how the user will respond to future training loads, enabling proactive optimization rather than reactive tracking
Solution Approach 2:
The system changes parameters by transforming raw biometric and activity data into derived metrics such as HRV scores, fatigue scores, and performance capacity indicators. These transformed parameters are then used to predict user response to training loads and determine optimal training prescriptions
2Adaptability or versatility
If universal statistics are used to determine training load, then simple training recommendations can be provided, but personalized optimization of user performance capacity is not achieved
Solution Approach 1:
The system segments the population into individual users with unique response profiles. Instead of applying universal training statistics to all users, the system divides the approach into personalized components by analyzing each user's specific biometric responses, activity patterns, and recovery characteristics to determine individualized optimal training loads
Solution Approach 2:
The system implements feedback loops where user responses to training (measured through biometric changes) are continuously monitored and fed back into the model. This feedback allows the system to refine predictions and adjust future training load recommendations to maximize each user's performance capacity over time
Data Source
AI summary
Systems and methods are provided for determining performance capacity. One such system includes a wearable device having a biosensor that measures biometrics and a motion sensor that monitors activity. The system also includes a processor coupled to 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. The instructions are executed to cause the processor to generate biometric data from the biometrics and activity data from the activity. Further, the instructions are executed to create a response profile based on one or more of a heart rate variability (HRV) score based on the biometric data, a fatigue score based on the activity data, a predicted HRV score based on the biometric and activity data, and a predicted fatigue score based on the biometric data and/or the activity data.