Motion Sensor Exercise Menu Setting from Activity Intensity Data
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Solution Overview
Problem
Existing patient-based assessments for physical activity in rehabilitation and sports lack quantitativeness, objectivity, and reproducibility, and do not support the setting of exercise menus based on quantitative and qualitative characteristics of physical activity intensity.
Innovation Solution
An exercise support apparatus and method that utilizes a motion sensor to detect body motion information, classifies activity intensity, and provides a next exercise menu based on prior activity data from a target group, using an information processing unit to output and store activity and step data for accurate menu setting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional patient-based assessment methods are used, then simplicity of assessment is maintained, but quantitativeness, objectivity, and reproducibility are insufficient
Solution Approach 1:
The patent replaces conventional mechanical/manual assessment methods with electronic sensor-based measurement systems. Acceleration sensors, gyro sensors, and other electronic devices automatically capture motion data, transforming subjective patient reports into objective quantitative measurements of physical activity, thereby improving quantitativeness and reproducibility
Solution Approach 2:
The patent introduces motion sensors and processing units as intermediaries between the patient's physical activity and the assessment results. These intermediaries automatically detect, record, and analyze motion parameters, eliminating the need for direct manual measurement and providing consistent, reproducible quantitative data
2Measurement precision
If detailed body motion information is collected and analyzed, then accuracy of exercise menu setting is improved, but information processing complexity increases
Solution Approach 1:
The patent segments the information processing into distinct functional modules: motion detection by sensors, data transmission to processing units, classification of physical activity types, and generation of exercise recommendations. This modular segmentation manages complexity by distributing processing tasks across multiple specialized components
Solution Approach 2:
The system automatically processes collected motion data through algorithms that classify activity types, calculate intensity levels, and generate personalized exercise menus without requiring manual intervention. The self-service processing reduces the burden on users while maintaining high accuracy in exercise recommendations
3Reliability
If exercise menu is customized based on individual body motion data, then effectiveness of rehabilitation and sports improvement is enhanced, but time required for data collection and analysis increases
Solution Approach 1:
The patent performs preliminary classification and analysis of motion data as it is being collected, rather than waiting for complete data sets. The system continuously processes incoming sensor data to provide real-time or near-real-time exercise recommendations, reducing the overall time required while maintaining customization effectiveness
Solution Approach 2:
The system implements continuous feedback loops where exercise performance data is immediately processed and used to adjust and refine exercise menu recommendations. This real-time feedback mechanism allows the system to adapt quickly to individual performance patterns, enhancing effectiveness while minimizing delays through automated iterative processing
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate and objective setting of exercise menus tailored to individual needs, improving rehabilitation and sports performance by leveraging prior body motion data for quantitative and qualitative analysis.
Implementation Method 1
detected by a motion sensor
Implementation Method 2
calculates composite angular velocity, vertical component angular velocity, and horizontal component angular velocity from detected acceleration data
Data Source
Figure 1
Figure 2
Figure 3A~3B
AI summary
An exercise support apparatus (3) includes an information processing unit (31) that supports a setting of a next exercise menu for improvement to a targeted body, based on prior body motion information on a subject detected by a motion sensor (2). The information processing unit (31) includes an activity amount processing unit (312) that calculates an amount of activity and a number of steps for each selected category of each category obtained by classifying activity intensity, based on body motion information on the subject, an image display processing unit (313) that outputs distribution information on the amount of activity and the number of steps that have been obtained from a target people group in advance, for each of the selected category, to a display unit (32), and an image display processing unit (313) that outputs the amount of activity and the number of steps that have been calculated by the activity amount processing unit (312), to the display unit (32). This provides suitable support in setting the next exercise menu for improvement to the targeted body, based on the prior body motion information on the subject detected by the motion sensor.