Balance Training System Using Dual Center of Gravity Detection
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Solution Overview
Problem
Balance training apparatuses fail to effectively improve balance in trainees as they often resort to unfavorable postures or leaning on structures to maintain balance, reducing the effectiveness of the training.
Innovation Solution
A balance training apparatus equipped with a riding plate, load sensors, an image-capturing unit, and a determination unit that calculates and compares the center of gravity from load data and posture analysis, alerting the trainee to correct their posture and adjusting the training plate's movement accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the balance training apparatus moves the riding plate according to the trainee's center of gravity movement, then the training effectiveness should be improved, but the trainee may adopt unfavorable postures or lean on surrounding structures to maintain balance, reducing training effectiveness
Solution Approach 1:
The system captures images of the trainee's posture, detects key body points, calculates the center of gravity position from posture data, and provides real-time feedback by moving the riding plate according to the detected center of gravity. This closed-loop feedback mechanism ensures the trainee receives appropriate training stimuli while maintaining proper posture, as unfavorable postures result in inaccurate center of gravity detection and inappropriate plate movement that would alert the trainee to correct their posture.
2Device complexity
If the apparatus uses only load sensor data to determine center of gravity, then the system is simple, but it cannot detect when the trainee adopts incorrect posture or leans on surrounding structures
Solution Approach 1:
The system merges two different detection approaches: load sensor data from the riding plate and image-based posture analysis. The image-capturing unit captures images of the trainee, and the posture detection algorithm identifies key body points to calculate the center of gravity. By combining these two independent detection methods, the system achieves more accurate and reliable center of gravity detection than either method alone, while also being able to detect improper postures.
3Reliability
If the apparatus continuously monitors and alerts the trainee about posture, then training quality is improved, but the system complexity and processing requirements increase
Solution Approach 1:
The system uses the trainee's own body as the measurement tool by detecting posture through image analysis of visible body landmarks. The trainee's natural posture changes automatically provide the data needed for center of gravity calculation, eliminating the need for additional sensors or complex measurement devices. The system processes only essential posture information rather than continuous video streams, reducing computational complexity while maintaining training quality control.
Data Source
AI summary
A training system includes a riding plate, a load sensor, a first center of gravity calculation unit, an image-capturing unit, a second center of gravity estimation unit, and a determination unit. The load sensor detects a load that the riding plate receives from a trainee. The first center of gravity calculation unit calculates a first center of gravity, which is a center of gravity of a load, based on the load detected by the load sensor. The image-capturing unit acquires image data of an image including a posture of the trainee. The second center of gravity estimation unit estimates a second center of gravity, which is a centroid position of the trainee, based on the image data. The determination unit determines that an alert to the trainee should be output based on a difference between the first center of gravity and the second center of gravity.