Personalized Step Count Algorithm for Body Composition
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Solution Overview
Problem
Current weight management through exercise is often non-individualized and ineffective, failing to achieve and sustain weight loss due to broad recommendations that do not account for key factors like appetite and energy intake.
Innovation Solution
A system and method for customized activity level recommendations using a recommendation algorithm that determines a custom physical activity threshold based on a user's body health indicators, including fat mass metric and weight/body fatness targets, to provide personalized step count targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If non-individualized broad exercise recommendations are used, then ease of operation is improved, but effectiveness of weight management deteriorates
Solution Approach 1:
The patent applies parameter changes by transitioning from generic exercise recommendations to personalized recommendations based on specific body composition parameters (fat mass, fat-free mass) and metabolic health indicators. The system calculates individualized step targets by modifying the standard recommendation parameters according to each user's measured body composition, thereby improving effectiveness while maintaining ease of use through automated calculations.
Solution Approach 2:
The patent segments the population into distinct groups based on body composition categories (e.g., different fat mass percentiles, fat-free mass levels) and metabolic health status. This segmentation allows the system to provide tailored exercise recommendations for each segment rather than a one-size-fits-all approach, improving weight management effectiveness while keeping the system easy to operate through automated classification.
2Reliability
If individualized activity recommendations based on body composition are implemented, then effectiveness of weight management is improved, but device complexity increases
Solution Approach 1:
The system applies self-service by automatically measuring body composition parameters and calculating personalized exercise recommendations without requiring complex manual assessments or expert intervention. The automated measurement and calculation processes enable individualized recommendations to be generated efficiently, improving effectiveness while minimizing the perceived complexity for users.
Solution Approach 2:
The patent simplifies the complex individualization process by focusing on a limited set of key parameters (fat mass, fat-free mass, metabolic health indicators) rather than attempting to account for all possible variables. This selective parameter approach maintains effectiveness while reducing device complexity by concentrating computational resources on the most influential factors.
3Ease of operation
If standardized exercise prescriptions are used, then ease of operation is improved, but adaptability to individual body composition deteriorates
Solution Approach 1:
The patent applies dynamics by making exercise prescriptions adaptive and dynamic rather than static. The system continuously adjusts recommendations based on measured body composition parameters, allowing the exercise prescription to evolve as the user's body composition changes over time. This dynamic approach maintains simplicity through automated adjustments while improving adaptability to individual characteristics.
Solution Approach 2:
The system changes the prescription parameters based on individual body composition measurements, transforming standardized fixed recommendations into personalized dynamic targets. By modifying key parameters (step targets, activity levels) according to measured fat mass and fat-free mass, the system achieves adaptability while maintaining ease of operation through automated parameter adjustment.
Data Source
AI summary
A method for customized activity level recommendations, the method comprising: receiving via an interface, at least one body health indicator associated with a user; determining, based at least on the body health indicator, a fat mass metric of the user and target weight and body composition of the user; determining, based on at least the fat mass metric and using a recommendation algorithm comprising a set of rules, a custom physical activity threshold for the user; and outputting an indication of the custom physical activity threshold.