Walking Training Parameter Control Using Recovery Learning Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing walking training systems struggle to adjust settings optimally for individual trainees with varying degrees of paralysis and recovery, often requiring manual intervention by training staff that may not be effective.
Innovation Solution
A learning system that includes data acquisition, control, and generation units to generate a learning model for setting parameters using machine learning, utilizing sensors to detect walking motion and estimate recovery levels, and classifying trainees for personalized assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual adjustment of actuator settings by training staff is performed, then individualized training can be provided, but the effectiveness is limited by staff capability and time consumption
Solution Approach 1:
The system automatically adjusts actuator settings based on sensor data and machine learning models without requiring manual intervention. The control unit receives detection results from sensors and autonomously determines optimal setting parameters, enabling the system to serve itself in the adjustment process.
Solution Approach 2:
Manual mechanical adjustment by training staff is replaced with an automated control system that uses sensors, processing units, and machine learning algorithms to determine and apply setting parameters automatically.
2Ease of operation
If actuator assisting force is increased, then training difficulty is reduced for low-ability trainees, but high-ability trainees cannot train effectively
Solution Approach 1:
The actuator settings are made dynamic and adaptable, automatically adjusting the assisting force based on real-time sensor data and the trainee's current ability level. This allows the system to provide high assistance when needed while automatically reducing assistance as the trainee improves, maintaining optimal training effectiveness throughout the rehabilitation process.
Solution Approach 2:
The control unit automatically changes the actuator parameters (assisting force, resistance levels) based on the trainee's performance data and recovery progress, transitioning from fixed manual settings to dynamic parameter adjustment that adapts to the trainee's evolving capabilities.
3Measurement precision
If comprehensive sensor data collection is implemented, then accurate recovery assessment is achieved, but system complexity increases
Solution Approach 1:
The control unit serves multiple functions: it processes sensor data, generates detection results, determines setting parameters, and controls actuators. This multi-functional design consolidates complexity into a single processing unit while achieving comprehensive recovery assessment through integrated data analysis.
Data Source
AI summary
A learning apparatus, a walking training apparatus, a method, a system, a program, and a trained model for performing walking training at appropriate setting parameters are provided. A learning apparatus according to an embodiment includes: a data acquisition unit configured to acquire rehabilitation data from a walking training apparatus including an actuator configured to assist a walking motion of a trainee and a control unit configured to control the actuator in accordance with a setting parameter; and a data generation unit configured to generate an index indicating a degree of recovery of the trainee and the setting parameter as learning data; and a learning unit configured to generate a learning model that receives the index and outputs a recommended value of the setting parameter by performing machine learning using the learning data.