Motion Analysis Apparatus Using Similarity Point Extraction
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Solution Overview
Problem
Current analysis methods in manufacturing sites are time-consuming and require manual recording or monitoring to analyze operation efficiency, which hinders quick and accurate assessment of operation periods.
Innovation Solution
An analysis apparatus comprising an acquisition unit and a processor that acquires and processes motion-based information using sensors like depth sensors or acceleration sensors, extracting similarity points and calculating time intervals to automatically determine operation periods, thereby reducing analysis time and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual recording or monitoring is used to analyze operation efficiency, then measurement precision can be achieved, but loss of time increases significantly
Solution Approach 1:
The patent replaces manual recording and monitoring methods with an automated analysis apparatus that uses sensors (such as depth sensors or acceleration sensors) to automatically detect and analyze operation periods. This substitution of mechanical/manual processes with automated sensing and processing systems directly resolves the contradiction by maintaining measurement precision while eliminating the time-consuming nature of manual analysis.
Solution Approach 2:
The analysis apparatus enables the system to automatically analyze operation periods without requiring human intervention for recording or monitoring. The apparatus autonomously detects motion-based information, extracts similarity points, calculates time intervals, and determines operation periods, allowing the system to serve itself in the analysis process rather than relying on manual human effort.
2Productivity
If automated analysis using sensors is implemented, then productivity increases and analysis time decreases, but device complexity increases
Solution Approach 1:
The patent extracts and utilizes specific motion-based information from sensor data that is relevant to operation period analysis. By focusing only on the essential features (similarity points in motion data) rather than processing all sensor information, the system achieves high productivity while managing device complexity through selective information extraction and processing.
3Ease of operation
If motion-based information is automatically detected using sensors, then ease of operation improves and manual recording is eliminated, but measurement precision requirements increase
Solution Approach 1:
The analysis apparatus employs feedback mechanisms where the processor continuously compares detected motion information against established criteria to identify similarity points. The system uses the detected similarity points and calculated time intervals to refine and validate operation period determinations, ensuring high measurement precision while maintaining ease of automatic operation through iterative verification processes.
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 efficient and accurate automatic analysis of operation periods without the need for manual recording or monitoring, allowing for real-time reflection of analysis results during operations and reducing dependence on human experience or assessment.
Implementation Method 1
acquires first information with a first time length between a first time and a second time, based on motion of an object person
Implementation Method 2
measuring operation time, or recoding operation is carried out and the result is analyzed
Data Source
AI summary
According to one embodiment, an analysis apparatus includes an acquisition unit and a processor. The acquisition unit acquires first information with a first time length between a first time and a second time. The first information is based on motion of an object person. The processor extracts multiple similarity points from the first information. The multiple similarity points are similar to each other in the first information. The processor calculates a time interval between the similarity points.