Fatigue Estimation System Using Imaging Device
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Solution Overview
Problem
Current fatigue level estimation methods are not adequately effective in accurately assessing fatigue in individuals, leading to potential health issues, injuries, and accidents.
Innovation Solution
A fatigue estimation system that utilizes an imaging device to capture and analyze the posture of a subject over a predetermined time period, counting specific movements associated with fatigue accumulation to estimate and output the fatigue level, incorporating personal fatigue information and subjective feedback for correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fatigue estimation methods (force measurement and bioelectrical impedance analysis) are used, then the estimation can be performed, but the accuracy and appropriateness of fatigue level estimation is insufficient
Solution Approach 1:
The patent replaces traditional mechanical and electrical measurement methods (force measurement, bioelectrical impedance analysis) with an optical imaging-based system. The imaging device captures visual information about body posture and movements, which are then analyzed to estimate fatigue levels. This substitution enables more accurate and reliable fatigue estimation by observing actual physical behaviors and postural changes that indicate fatigue states.
2Measurement precision
If imaging device is used to capture body posture and movements, then fatigue estimation accuracy is improved, but device complexity increases
Solution Approach 1:
The imaging device serves multiple functions: it captures images of the subject's body posture, detects movements, identifies specific fatigue-related movements, and provides visual feedback. This multi-functionality reduces the need for separate specialized sensors and devices, thereby managing system complexity while maintaining high measurement precision for fatigue estimation.
3Measurement precision
If specific movements are counted to estimate fatigue, then personalized estimation is achieved, but measurement time and processing requirements increase
Solution Approach 1:
The system pre-identifies and defines specific movements that are characteristic of fatigue states. By having these movement patterns predetermined and categorized in advance, the system can quickly compare captured images against these predefined patterns during actual measurement, reducing real-time processing requirements and measurement time while maintaining high accuracy in personalized fatigue estimation.
Data Source
AI summary
A fatigue estimation system includes: an information output device (e.g., an imaging device) that outputs information regarding locations of body parts of a subject; and an estimation device that, based on the information output from the information output device in a predetermined time period, estimates a fatigue level of the subject accumulated in the predetermined time period by counting a specific movement that appears in response to fatigue accumulation, and outputs the estimated fatigue level.