Worker Motion Evaluation Using Proficiency-Based Standards
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Solution Overview
Problem
Existing work analysis methods fail to consider the proficiency level of a worker when evaluating their motion patterns, leading to inadequate assessments.
Innovation Solution
A work analysis assistance device that sets evaluation standards based on the number of times a worker has performed a task, using video analysis and biometric data to evaluate motion patterns, calculate scores, and predict future performance, thereby accounting for proficiency levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed evaluation standard is used for all workers regardless of their experience, then the evaluation process is simple and quick, but the evaluation accuracy decreases because it does not reflect the worker's proficiency level
Solution Approach 1:
The evaluation standard is made dynamic by adjusting it according to the worker's proficiency level, which is determined by the number of times they have performed the task. The system automatically modifies evaluation criteria from a fixed state to a variable state that adapts to each worker's experience level, thereby improving evaluation accuracy without manual intervention.
Solution Approach 2:
The system changes the parameters of the evaluation standard based on the worker's task execution count. By modifying evaluation parameters according to proficiency level (a quantitative parameter), the system achieves more accurate assessments while maintaining automated operation, resolving the contradiction between accuracy and complexity.
2Measurement precision
If the evaluation standard is customized for each worker's proficiency level, then the evaluation accuracy improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system performs self-service by automatically determining the worker's proficiency level based on their task execution history and autonomously selecting the appropriate evaluation standard. This eliminates the need for manual proficiency assessment, reducing the difficulty of measurement while maintaining high evaluation precision through automated data processing.
Solution Approach 2:
The system performs preliminary action by pre-establishing multiple evaluation standards corresponding to different proficiency levels before actual evaluation occurs. When a worker performs a task, the system has already prepared the appropriate evaluation criteria based on the worker's experience level, making the assessment process efficient and accurate without real-time complexity.
3Adaptability or versatility
If multiple evaluation standards are maintained for different proficiency levels, then the evaluation becomes more comprehensive and accurate, but the system complexity and data storage requirements increase
Solution Approach 1:
The system achieves universality by creating a multi-functional evaluation framework where a single system can handle multiple proficiency levels and task types. The evaluation standards are designed to be universally applicable across different workers and tasks, with the system automatically selecting and applying the appropriate standard based on input parameters, thereby maintaining adaptability without proportionally increasing data storage requirements.
Data Source
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AI summary
A motion pattern of a worker is evaluated in consideration of a proficiency level of the worker with respect to a work. A pattern acquisition unit (12) that acquires a motion pattern when a worker executes a work, an evaluation standard setting unit (15) that sets an evaluation standard for the motion pattern, and a pattern evaluation unit (18) that evaluates the motion pattern on the basis of the evaluation standard are provided, and the evaluation standard setting unit (18) sets the evaluation standard for the motion pattern when a certain worker executes certain work according to the number of times of execution of the certain work executed by the certain worker.