Toyota jidosha kabushiki kaisha (20240188848). LEARNING SYSTEM, WALKING TRAINING SYSTEM, METHOD, PROGRAM, AND TRAINED MODEL simplified abstract
Contents
- 1 LEARNING SYSTEM, WALKING TRAINING SYSTEM, METHOD, PROGRAM, AND TRAINED MODEL
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 LEARNING SYSTEM, WALKING TRAINING SYSTEM, METHOD, PROGRAM, AND TRAINED MODEL - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Walking Pattern Detection
- 1.13 Original Abstract Submitted
LEARNING SYSTEM, WALKING TRAINING SYSTEM, METHOD, PROGRAM, AND TRAINED MODEL
Organization Name
toyota jidosha kabushiki kaisha
Inventor(s)
Nobuhisa Otsuki of Toyota-shi (JP)
Issei Nakashima of Toyota-shi (JP)
Manabu Yamamoto of Toyota-shi (JP)
Hodaka Kito of Nagoya-shi (JP)
LEARNING SYSTEM, WALKING TRAINING SYSTEM, METHOD, PROGRAM, AND TRAINED MODEL - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240188848 titled 'LEARNING SYSTEM, WALKING TRAINING SYSTEM, METHOD, PROGRAM, AND TRAINED MODEL
Simplified Explanation
The patent application describes a learning system that uses data from rehabilitation exercises to detect abnormal walking patterns in trainees. Machine learning is then used to analyze this data and provide feedback for improvement.
- Data generation unit creates learning data from rehabilitation data.
- Sensor detects motion amounts in walking.
- Abnormal walking criteria are used to identify abnormal walking patterns.
- Learning unit uses rehabilitation data for machine learning.
Key Features and Innovation
- Utilizes rehabilitation data to improve walking patterns.
- Sensor technology for detecting motion amounts.
- Machine learning for analyzing and providing feedback.
- Focuses on detecting and correcting abnormal walking patterns.
Potential Applications
This technology can be applied in:
- Physical therapy settings.
- Sports training programs.
- Rehabilitation centers.
- Elderly care facilities.
Problems Solved
- Detecting abnormal walking patterns.
- Providing personalized feedback for improvement.
- Enhancing rehabilitation exercises with machine learning.
Benefits
- Improved walking patterns.
- Personalized feedback for trainees.
- Enhanced rehabilitation outcomes.
- Efficient analysis of motion data.
Commercial Applications
Title: "Enhanced Rehabilitation and Training System for Walking Patterns" This technology can be used in:
- Medical device industry.
- Fitness and wellness industry.
- Research institutions focusing on mobility.
- Sports performance enhancement companies.
Prior Art
Readers can explore prior art related to motion detection sensors, machine learning in rehabilitation, and gait analysis technologies.
Frequently Updated Research
Stay updated on advancements in motion detection sensors, machine learning algorithms for rehabilitation, and gait analysis techniques.
Questions about Walking Pattern Detection
How does this technology improve rehabilitation outcomes?
This technology improves rehabilitation outcomes by providing personalized feedback based on the analysis of motion data, helping trainees correct abnormal walking patterns and enhance their overall walking performance.
What are the potential applications of this technology beyond rehabilitation?
This technology can be applied in various settings such as sports training programs, elderly care facilities, and research institutions focusing on mobility, to improve walking patterns and enhance overall physical performance.
Original Abstract Submitted
the learning system includes a data generation unit configured to generate learning data based on rehabilitation data and a learning unit configured to perform machine learning using the learning data. a sensor is provided to detect a plurality of motion amounts in a walking motion of a trainee, and it is evaluated that, when one of the motion amounts matches one of abnormal walking criteria, that the walking motion is an abnormal walking pattern that meets the matched abnormal walking criterion. the data generation unit generates each of the pieces of rehabilitation data before and after a change in the results of evaluation of the abnormal walking pattern as learning data. the learning unit sequentially inputs each of the pieces of rehabilitation data as one data set, thereby performing machine learning.
