18583104. LEARNING SYSTEM, WALKING TRAINING SYSTEM, METHOD, PROGRAM, AND TRAINED MODEL simplified abstract (TOYOTA JIDOSHA KABUSHIKI KAISHA)
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 the Technology
- 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 18583104 titled 'LEARNING SYSTEM, WALKING TRAINING SYSTEM, METHOD, PROGRAM, AND TRAINED MODEL
Simplified Explanation
The patent application describes a learning system that uses rehabilitation data to generate learning data and perform machine learning. It includes a sensor to detect abnormal walking patterns in trainees and generates data sets for machine learning based on changes in the evaluation of these patterns.
- The learning system generates learning data from rehabilitation data.
- A sensor detects abnormal walking patterns in trainees.
- Data sets are created for machine learning based on changes in the evaluation of abnormal walking patterns.
Key Features and Innovation
- Utilizes rehabilitation data to generate learning data for machine learning.
- Detects abnormal walking patterns in trainees using a sensor.
- Creates data sets for machine learning based on changes in the evaluation of abnormal walking patterns.
Potential Applications
This technology can be used in:
- Physical therapy settings to monitor and improve walking patterns.
- Fitness tracking devices to provide feedback on walking form.
- Sports training programs to analyze and correct movement patterns.
Problems Solved
- Identifying and addressing abnormal walking patterns in trainees.
- Utilizing rehabilitation data effectively for machine learning.
- Providing real-time feedback on walking motions.
Benefits
- Improved monitoring and correction of abnormal walking patterns.
- Enhanced use of rehabilitation data for machine learning.
- Real-time evaluation and feedback on walking motions.
Commercial Applications
- Title: "Enhanced Walking Pattern Analysis System"
- This technology can be used in physical therapy clinics, fitness centers, and sports training facilities.
- It has the potential to improve rehabilitation outcomes, enhance athletic performance, and optimize fitness training programs.
Prior Art
Readers can explore prior art related to motion analysis systems, gait analysis technologies, and machine learning in healthcare and fitness industries.
Frequently Updated Research
Stay updated on advancements in motion analysis technologies, machine learning algorithms for healthcare, and sensor technologies for movement tracking.
Questions about the Technology
How does this technology improve rehabilitation outcomes?
This technology improves rehabilitation outcomes by providing real-time feedback on walking patterns, allowing for timely corrections and adjustments.
What are the potential long-term benefits of using this system?
The potential long-term benefits include improved walking patterns, reduced risk of injury, and enhanced 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.
