Fitness State Model for Personalized Exercise Guidance
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Solution Overview
Problem
Individuals face challenges in achieving a desired fitness state due to overwhelming information on exercises and uncertainty about how to tailor routines to their specific biological parameters and pre-existing medical conditions, especially for those new to fitness.
Innovation Solution
A system and method utilizing a computing device that receives biological parameters, determines the current fitness state through a machine-learning algorithm, and provides user-specific recommendations to achieve a preferred fitness state, categorized into strength, endurance, or well-being, using sensors like ECG, thermal sensors, and electrophysiological sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a system provides comprehensive fitness information and exercise recommendations, then the user can achieve their fitness goals more effectively, but the system complexity and data processing requirements increase significantly
Solution Approach 1:
The patent segments the fitness recommendation system into distinct functional modules: a fitness state model that processes biological parameters to determine current fitness levels, and a recommendation model that generates personalized exercise prescriptions. This modular segmentation allows each component to specialize in specific tasks, improving overall system efficiency while managing complexity through clear separation of concerns.
Solution Approach 2:
The patent introduces machine learning models as intermediary components between raw biological parameter data and fitness recommendations. These intermediary models (fitness state model and recommendation model) process and transform complex input data into actionable insights, effectively mediating between data collection and decision-making without requiring the end system to handle all computational complexity directly.
2Measurement precision
If the system uses machine-learning algorithms to determine fitness state, then the accuracy of fitness assessment improves, but the computational resources and processing time increase
Solution Approach 1:
The patent employs preliminary action by pre-training machine learning models offline to establish the fitness state model and recommendation model. This allows the computationally intensive model training and parameter optimization to be performed in advance, so that during actual use, the system only needs to execute pre-computed algorithms with minimal real-time computational resources, thereby maintaining high accuracy while reducing runtime energy consumption.
3Adaptability or versatility
If the system provides personalized recommendations based on biological parameters, then the adaptability to individual user needs improves, but the data collection requirements and measurement complexity increase
Solution Approach 1:
The patent implements universality by designing the system to accept multiple types of biological parameters (heart rate, body temperature, respiratory rate, blood oxygen saturation) through a unified processing framework. The fitness state model and recommendation model are constructed to handle diverse input data types consistently, allowing the system to adapt to individual user needs across different physiological measurements without requiring separate processing pipelines for each parameter type.
Data Source
AI summary
A system of providing a preferred fitness state of a user, the system comprising a computing device, wherein the computing device is configured to receive, at least a first biological parameter; determine, a current user fitness state, wherein determining the current user fitness state further comprises generating the current user fitness state using a fitness state model relating biological parameters to fitness states and a first machine-learning algorithm; and identify, a user specific recommendation as a function of the at least a first biological parameter and the current user fitness state.