AI Exercise Interface for Dynamic Resistance and Feedback
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Solution Overview
Problem
Existing exercise and rehabilitation devices lack the ability to dynamically adjust resistance and provide personalized feedback to users, leading to suboptimal exercise performance and compliance with exercise plans.
Innovation Solution
The integration of an artificial intelligence engine that utilizes machine learning models to receive sensor measurements from exercise devices and wearable devices, allowing for real-time adjustment of resistance, personalized exercise plans, and interactive user interfaces to enhance user engagement and compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If exercise devices use fixed resistance settings, then the device structure is simple, but the adaptability to different users and exercise goals is poor
Solution Approach 1:
The exercise device transitions from fixed resistance settings to dynamically adjustable resistance. The system continuously monitors user performance metrics (reps, sets, rest intervals) and automatically modifies resistance levels in real-time, enabling the same device to adapt to different users and exercise goals without requiring multiple fixed configurations.
Solution Approach 2:
The resistance parameter is made variable rather than fixed. The system changes resistance values based on real-time performance data, allowing the device to adjust difficulty levels dynamically. This enables a single device structure to serve multiple users with different fitness levels and objectives by modifying operational parameters rather than requiring structural complexity.
2Ease of operation
If exercise devices lack real-time feedback mechanisms, then the device is simpler, but user engagement and compliance with exercise plans are reduced
Solution Approach 1:
The system implements real-time feedback loops that continuously monitor user performance metrics (reps, sets, rest intervals) and provide immediate feedback through the user interface. This feedback mechanism enhances user engagement by allowing real-time adjustment of exercise parameters and providing motivation through performance tracking, while the feedback is processed through software algorithms that add functionality without significant hardware complexity.
3Productivity
If the system manually adjusts exercise parameters, then the control system is simpler, but the productivity of exercise plan customization is low
Solution Approach 1:
The system performs self-adjustment of exercise parameters based on automated performance monitoring. The control system automatically modifies resistance, rep ranges, and rest intervals without requiring manual intervention, enabling rapid customization of exercise plans for different users. This automation dramatically increases productivity in exercise plan customization while reducing the need for manual adjustment operations.
Data Source
AI summary
A method is disclosed for using an artificial intelligence engine to present a user interface capable of presenting the progress of a user in domains. 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 user interface that causes icons to dynamically change position on the user interface. While a user performs an exercise using the exercise device, the method includes receiving the measurements from sensors associated with the exercise device. The method includes presenting, on a computing device associated with the exercise device, sections of the user interface, wherein the sections are each related to a separate domain comprising the domains and wherein, based on the measurements, each section includes the icons placed.