Adaptive Rehabilitation Training System for Dynamic Difficulty Adjustment
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Solution Overview
Problem
Conventional rehabilitation training content systems fail to adapt difficulty levels based on user progress, leading to monotony and reduced effectiveness in motor and sensory function rehabilitation.
Innovation Solution
A method for dynamically setting training difficulty levels by checking preset reference values and collecting user training details, allowing for increased difficulty adjustments based on performance, and providing customized training content through a computing system and electronic device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional rehabilitation training content systems use fixed difficulty levels, then the system structure is simple and easy to operate, but the training becomes monotonous and effectiveness decreases as users get used to it
Solution Approach 1:
The patent implements dynamic difficulty adjustment by automatically modifying training content parameters based on real-time user performance data. The system transitions from static fixed difficulty levels to dynamic adaptive difficulty, where training parameters such as speed, distance, or repetition count are continuously adjusted according to user progress, thereby maintaining training effectiveness without requiring complex manual intervention.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor user performance during training sessions and use this information to automatically adjust difficulty levels. By analyzing user responses, completion rates, and performance metrics, the system provides continuous feedback loops that adapt training content difficulty, resolving the contradiction between simplicity and adaptability through automated feedback-driven adjustment.
2Adaptability or versatility
If rehabilitation training content remains typical and unchanging, then the program is easy to implement, but user engagement decreases over time as users adapt to the routine
Solution Approach 1:
The patent applies dynamics by transforming static training content into dynamic, adaptive content that evolves based on user progress. Training programs automatically modify parameters such as exercise intensity, duration, frequency, and type based on user performance data, ensuring continuous variability in training content while maintaining ease of delivery through automated system control rather than manual program redesign.
Solution Approach 2:
The system utilizes parameter changes to create training content variability by adjusting key training parameters including speed, distance, repetition count, rest intervals, and exercise selection based on user performance. This approach maintains ease of operation by automatically modifying parameters through algorithmic adjustment rather than requiring complex manual content creation, thereby achieving content variability without sacrificing operational simplicity.
Data Source
AI summary
The present disclosure relate to a method for setting a difficulty level of training contents and an electronic device implementing the same. A method for setting a difficulty level of training according to various embodiments of the present disclosure includes: checking a first reference value, which is a preset difficulty level for a first session, if the user wants to perform a training motion of the first session; providing the user with training contents corresponding to the checked first reference value during the first session; collecting the user's training details of the training performed during the first session; setting an increased difficulty level based on a second reference value, which is a preset difficulty level for a second session and the user's training details collected during the first session, if the user wants to perform a motion of the second session; and providing the user with training contents corresponding to the set increased difficulty level during the second session.