Nec corporation (20240206769). INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM simplified abstract
INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM
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INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240206769 titled 'INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM
Simplified Explanation: This patent application describes an information processing apparatus capable of evaluating a subject's breathing motor function by calculating a respiratory feature amount based on distance image data acquired during a breathing exercise.
- The apparatus inputs time-series distance image data that measures the distance from the subject during the exercise.
- It also receives division position data indicating the position where the chest and abdominal regions of the subject are divided in the image data.
- The apparatus calculates a standard deviation image showing the variation of values for each pixel in the distance image.
- Using the standard deviation image, it calculates a respiratory feature amount that reflects the subject's breathing pattern.
Key Features and Innovation:
- Calculation of respiratory feature amount based on distance image data.
- Use of standard deviation image to analyze breathing patterns.
- Division of chest and abdominal regions for accurate evaluation.
Potential Applications: This technology can be used in medical settings to assess respiratory function in patients with breathing difficulties. It can also be applied in sports science to monitor athletes' breathing efficiency during training.
Problems Solved:
- Provides a quantitative measure of respiratory function.
- Enables detailed analysis of breathing patterns.
- Facilitates early detection of respiratory issues.
Benefits:
- Improved assessment of breathing motor function.
- Enhanced monitoring of respiratory health.
- Early intervention for respiratory problems.
Commercial Applications: Title: "Advanced Respiratory Function Assessment Technology for Healthcare and Sports Science" This technology can be commercialized as a diagnostic tool for healthcare providers and sports performance centers. It has the potential to improve patient care and optimize athletic training programs.
Prior Art: Readers interested in prior art related to this technology may explore research on respiratory function assessment tools and image processing techniques in medical and sports science literature.
Frequently Updated Research: Researchers are continually exploring new methods to enhance respiratory function assessment technology, including advancements in image processing algorithms and wearable sensors for real-time monitoring.
Questions about Respiratory Function Assessment Technology: 1. How does this technology compare to traditional methods of assessing respiratory function? 2. What are the potential limitations of using distance image data for evaluating breathing motor function?
Original Abstract Submitted
an information processing apparatus and the like capable of calculating a respiratory feature amount necessary for evaluating a breathing motor function of a subject is provided. the information processing apparatus inputs time-series distance image data acquired by measuring a distance from the subject during a breathing exercise and division position data indicating a position at which a chest region and an abdominal region of the subject are divided in the time-series distance image data. the information processing apparatus calculates a standard deviation image indicating a standard deviation of values for each pixel in a distance image indicated by the time-series distance image data. the information processing apparatus calculates a respiratory feature amount indicating a feature of breathing of the subject, based on a divided standard deviation image acquired by dividing the standard deviation image for each divided region being divided at a position indicated by the division position data.
