AI-Driven Pedal Resistance Adjustment for Exercise Devices
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Solution Overview
Problem
Current exercise machines lack the ability to dynamically adjust resistance based on real-time user feedback and machine learning, limiting personalized and effective osteogenesis and muscular hypertrophy training.
Innovation Solution
An AI-driven system that uses machine learning models to analyze sensor data from exercise devices, adjusting resistance in real-time to ensure users exceed osteogenesis and muscular strength thresholds, providing personalized exercise plans and feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If resistance is manually adjusted or fixed, then device complexity is reduced, but adaptability and effectiveness of personalized training are limited
Solution Approach 1:
The resistance of the exercise device is transformed from a static, manually-adjusted parameter to a dynamic, automatically-adjusted parameter. Sensors continuously monitor user performance metrics (force, power, velocity), and the control system dynamically modifies resistance levels in real-time based on detected movements and performance thresholds, enabling the device to adapt to each user's capabilities without manual intervention.
Solution Approach 2:
The exercise device performs self-adjustment of resistance without requiring external control or manual intervention. The embedded sensors and control system autonomously detect user movements, analyze performance data, and automatically modify resistance parameters to maintain optimal training conditions, allowing the device to serve itself in adapting to user needs.
2Measurement precision
If real-time sensor monitoring and AI processing are implemented, then measurement precision and adaptability are improved, but device complexity and cost increase
Solution Approach 1:
The sensor system and control unit are designed to perform multiple functions: detecting various types of movements, measuring performance parameters (force, power, velocity), analyzing data against predefined thresholds, and controlling resistance adjustments. This multi-functional integration reduces the need for separate specialized components for each measurement and control task.
Solution Approach 2:
The control system acts as an intermediary between the sensors that detect user performance and the resistance mechanism that adjusts load. It processes sensor data, compares measurements against thresholds, and translates this information into appropriate resistance adjustments, mediating between raw data and actionable control signals.
3Productivity
If automatic resistance adjustment is implemented, then exercise effectiveness and compliance are improved, but ease of operation may be reduced due to system complexity
Solution Approach 1:
The resistance adjustment function operates autonomously without requiring user intervention. The system self-monitors performance, self-analyzes data, and self-adjusts resistance parameters, freeing the user from the need to manually control or monitor resistance settings during exercise sessions.
Solution Approach 2:
The system continuously monitors user performance through sensors and provides real-time feedback by adjusting resistance based on detected movements and performance thresholds. This closed-loop feedback mechanism ensures the exercise remains challenging but achievable, automatically adapting to maintain optimal training zones without user input.
Data Source
AI summary
A method is disclosed for using an artificial intelligence engine to modify resistance of pedals of an exercise device. The method includes generating, by the artificial intelligence engine, a machine learning model trained to receive measurements as input, and outputting, based on the measurements, a control instruction that causes the exercise device to modify, independently from each other, the resistance of the pedals. While a user performs an exercise using the exercise device, the method includes receiving the measurements from sensors associated with the pedals. The method includes determining, based on the measurements, a quantifiable or qualitative modification to the resistance provided by a pedal of the pedals. The resistance provided by another pedal of the pedals is not modified. The method includes transmitting the control instruction to the exercise device to cause the resistance provided by the pedal to be modified.