AI Exercise Device Control for Dynamic Resistance Adjustment
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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 uses machine learning models to receive sensor and wearable device measurements, allowing for real-time adjustment of resistance, presentation of personalized user interfaces, and dynamic exercise plan adaptation based on user feedback and fitness levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If exercise devices use fixed resistance settings, then device complexity is reduced, but adaptability to different users and exercise goals deteriorates
Solution Approach 1:
The exercise device transitions from fixed resistance settings to dynamic resistance adjustment capabilities. The system continuously monitors user performance metrics (reps, sets, time to completion) and automatically modifies resistance levels in real-time, allowing the device to adapt to different users and exercise goals without requiring manual intervention or complex mechanical adjustment mechanisms.
Solution Approach 2:
The system changes operational parameters (resistance levels) based on monitored performance data. By adjusting resistance parameters dynamically according to user progress, the device achieves high adaptability while maintaining relatively simple mechanical structures, as the complexity is shifted to the control system rather than the mechanical design.
2Ease of operation
If exercise devices lack real-time feedback mechanisms, then device complexity is reduced, but user engagement and compliance deteriorate
Solution Approach 1:
The system implements real-time feedback mechanisms that monitor user performance metrics (reps, sets, time to completion) and provide immediate feedback through the interface. This feedback loop enhances user engagement and compliance by allowing users to track their progress and adjust their exercise routine based on real-time data, while the feedback is delivered through software rather than complex hardware additions.
3Productivity
If exercise devices do not provide personalized adjustments, then ease of manufacture is improved, but exercise performance optimization deteriorates
Solution Approach 1:
The system performs self-service by automatically analyzing user performance data and adjusting exercise parameters without requiring manual programming or complex manufacturing customization. The device serves itself by using monitored metrics to determine optimal resistance levels and exercise modifications, achieving personalized optimization through software algorithms rather than complex manufacturing processes.
4Loss of time
If exercise devices use manual adjustment mechanisms, then device complexity is reduced, but time required for setup and adjustment increases
Solution Approach 1:
The system performs preliminary action by pre-configuring exercise programs and parameters that are automatically applied when a user begins a workout. The device proactively sets up optimal resistance levels and exercise parameters based on user profile data or previous performance, eliminating the need for manual adjustment during setup and reducing time requirements without adding significant complexity to the device structure.
Data Source
AI summary
A method is disclosed for using an artificial intelligence engine to interact with a user of an exercise device during an exercise session. The method includes generating, by the artificial intelligence engine, a machine learning model trained to receive data as input, and based on the data, providing an output. While a user performs an exercise using the exercise device, the method includes receiving the data from an input peripheral of a computing device associated with the user. Based on the data being received from the input peripheral, the method includes determining, via the machine learning model, the output to control an aspect of the exercise device.