Skeletal Structure Correction for Musculoskeletal Risk Assessment
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Solution Overview
Problem
Current methods for investigating risks to the musculoskeletal system of workers are labor-intensive and subjective, requiring experts to analyze extensive operation images to assess posture risks, which can lead to inconsistent interpretations and increased analysis time.
Innovation Solution
A skeletal structure correction apparatus and method using deep learning technology to extract and correct skeletal information from operation images, determining action levels and improving operation postures by analyzing images of workers' body parts, joints, and motion directions, thereby reducing the workload for experts and providing quantitative analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If experts manually analyze operation images to assess posture risks, then assessment accuracy can be maintained through expert judgment, but analysis time and workload increase significantly
Solution Approach 1:
The patent introduces an automated analysis system with deep learning models as an intermediary between operation images and expert assessment. The system extracts skeletal information, calculates joint angles, and identifies risky postures automatically, serving as a mediator that prepares pre-analyzed results for expert review, thereby reducing the time experts need to spend on manual image analysis while maintaining assessment accuracy.
Solution Approach 2:
The patent replaces the manual mechanical process of expert image analysis with an automated computational system. The deep learning model automatically detects skeletal points, calculates joint angles, and identifies risky postures without requiring experts to manually examine each image, thus dramatically reducing analysis time while maintaining or improving assessment precision through consistent application of evaluation criteria.
2Reliability
If experts analyze extensive operation images to assess posture risks, then comprehensive risk assessment can be achieved, but workload and complexity increase
Solution Approach 1:
The patent segments the complex analysis process into distinct automated components: skeletal information extraction, joint angle calculation, posture classification, and risk assessment. Each component is handled by specific deep learning models or algorithms, breaking down the overall complexity into manageable modules that can be processed automatically, thereby maintaining comprehensive risk assessment while reducing process complexity.
Solution Approach 2:
The system enables self-service analysis where the automated model independently performs image processing, skeletal extraction, and risk identification without requiring expert intervention for each step. The model serves itself by automatically preparing and organizing analysis results, reducing the burden on experts while ensuring comprehensive coverage of all operation images.
3Measurement precision
If multiple operation images are analyzed in detail by experts, then accurate posture assessment can be obtained, but interpretation consistency decreases due to subjective judgment
Solution Approach 1:
The patent transforms subjective expert interpretation into objective parameter-based assessment. The system calculates specific joint angles, skeletal positions, and posture parameters using standardized mathematical formulas, replacing variable human judgment with consistent parameter measurements. This ensures that the same posture will always yield the same assessment results, improving interpretation consistency while maintaining accuracy through precise parameter measurement.
4Productivity
If automated deep learning models are used to extract skeletal information, then analysis time is reduced, but measurement precision may be affected by capturing angles
Solution Approach 1:
The patent addresses angle-related precision issues by incorporating multi-dimensional analysis. The system processes operation images from multiple capturing angles and uses 3D skeletal reconstruction to calculate joint angles in three-dimensional space. This dimensional approach allows the automated model to accurately determine skeletal positions and joint angles regardless of the camera's viewing angle, maintaining measurement precision while achieving high analysis speed through automated computation.
Data Source
AI summary
Provided are a skeletal structure correction apparatus and a skeletal structure correction method for assessing risks to a musculoskeletal system of a worker. The skeletal structure correction method quantitatively determines an analysis time and uncertain body motions by providing an assessment tool for significantly reducing the workload of ergonomic/industrial engineering experts investigating the risks to the musculoskeletal system.