Learning Model for Rehabilitation Support System
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Solution Overview
Problem
In rehabilitation support systems, training staff members struggle to provide optimal assistance as they cannot directly observe a trainee's motivation, leading to ineffective rehabilitation support, as the assistance timing and degree significantly impact training results.
Innovation Solution
A learning system that generates a model predicting feedback control based on motivation information, using rehabilitation data including training data, motivation information, and feedback information to improve motivation, allowing for personalized assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If training staff members provide assistance during rehabilitation, then training results can be improved, but the staff members cannot directly observe the trainee's motivation leading to inappropriate assistance timing and degree
Solution Approach 1:
The patent introduces an image processing apparatus as an intermediary that captures images of the trainee's face and extracts motivation information (such as facial expressions indicating motivation level) to transmit to the rehabilitation support system. This mediator bridges the gap between the trainee's internal state and the training staff's decision-making process, enabling appropriate assistance timing and degree based on observed motivation.
Solution Approach 2:
The patent replaces the mechanical/physical observation method (direct visual observation by training staff) with an automated image processing and analysis system. The system uses image capture devices, image processing units, and motivation information extraction algorithms to automatically detect and quantify the trainee's motivation state, substituting human observation with automated technological means.
2Loss of time
If training staff members continuously monitor the trainee to provide timely assistance, then assistance timing can be optimized, but the complexity of the rehabilitation support system increases
Solution Approach 1:
The rehabilitation support system performs self-monitoring by automatically capturing images, extracting motivation information, and determining assistance timing without requiring continuous human observation. The system serves itself by autonomously monitoring the trainee's state and triggering assistance based on extracted motivation information, reducing the need for complex human-in-the-loop monitoring mechanisms.
Solution Approach 2:
The patent implements a feedback mechanism where the image processing apparatus continuously monitors the trainee's facial expressions and motivation level, and this information is fed back to the rehabilitation support system in real-time. This feedback loop enables the system to automatically adjust assistance timing and degree based on the trainee's current motivation state, optimizing assistance delivery without requiring complex manual monitoring protocols.
Data Source
AI summary
A learning unit of a learning system generates a learning model, the learning model being configured to input rehabilitation data about rehabilitation and predict feedback control to be performed, the rehabilitation being performed by a trainee using a rehabilitation support system. The rehabilitation support system performs the feedback control based on motivation information of the trainee. The rehabilitation data includes at least training data including the motivation information of the trainee and feedback information indicating the feedback control. The learning unit generates the learning model by using, as teacher data, the rehabilitation data that is obtained when the motivation information is one that causes such a change that the motivation of the trainee is improved.