AI Exercise Management Server Using Look-Up Tables
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Solution Overview
Problem
Existing digital healthcare solutions for home training lack personalized and adaptive exercise management, failing to effectively adjust exercise difficulty based on individual user's physical fitness level and real-time vital sign data.
Innovation Solution
A server-based AI exercise management system that receives hit point timing, skeleton degree, and vital sign information from user devices to perform exercise training or strength management processes, using a look-up table to adjust exercise difficulty and recommend exercises based on user's gender, age, BMI, and real-time data from sensors like accelerometers, gyroscopes, and heart rate sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a server-based AI exercise management system is implemented to provide personalized exercise management, then exercise management effectiveness and user engagement are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The system segments exercise management into multiple dimensions: hit point timing analysis, skeleton degree measurement, and vital sign monitoring. Each dimension is processed independently and then integrated to provide comprehensive personalized exercise management, reducing overall system complexity while maintaining adaptability.
Solution Approach 2:
A server acts as an intermediary between user devices and the exercise management algorithm. The server receives data from multiple sensors (accelerometer, gyroscope, heart rate sensor), processes this information centrally, and provides personalized exercise recommendations, thereby managing complexity at a centralized location rather than in individual devices.
2Measurement precision
If real-time data from multiple sensors is collected and processed, then measurement precision and exercise analysis accuracy are improved, but data processing time and computational load increase
Solution Approach 1:
The system performs preliminary actions by pre-defining exercise programs with target hit points, skeleton degree ranges, and vital sign thresholds before the user begins exercising. During exercise, the system only needs to compare real-time sensor data against these pre-established criteria, significantly reducing processing time while maintaining high measurement precision.
Solution Approach 2:
The system skips unnecessary processing steps by directly comparing sensor data against pre-defined exercise program parameters. Instead of performing complex real-time analysis of all movement patterns, the system focuses on key measurement points (hit points) and critical parameters (skeleton degrees at specific moments, vital sign ranges), rapidly processing only the most relevant data.
3Adaptability or versatility
If look-up tables are applied to adjust exercise difficulty based on user characteristics, then adaptability to individual users is improved, but system complexity and processing requirements increase
Solution Approach 1:
The system uses look-up tables that store pre-calculated exercise parameters (difficulty levels, target values, thresholds) organized by user characteristics such as age, gender, and fitness level. When a user begins exercising, the system retrieves appropriate parameters from the look-up table based on the user's profile, enabling easy adaptation of exercise difficulty without complex real-time calculations.
Data Source
AI summary
The present invention relates to a server and a method for providing an exercise management solution to a user. Furthermore, one embodiment of the present invention relates to technology applying the look-up table (LUT). Furthermore, one embodiment of the present invention relates to technology applying artificial intelligence.