Sitting Posture Pattern Analysis for Low Back Pain Prediction
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Solution Overview
Problem
Existing techniques struggle to accurately predict the occurrence of low back pain in individuals who frequently change their sitting posture, making it difficult to prevent the condition effectively.
Innovation Solution
A low back pain analysis system utilizing a sensor attached to furniture to measure gravity center movement data, which employs clustering and machine learning to estimate the probability of low back pain through a trained model that generates sitting situation data, including common patterns and their frequency, to improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional posture monitoring techniques are used to detect static sitting postures, then simple posture classification is achieved, but accuracy in predicting low back pain is insufficient for dynamic sitting behaviors
Solution Approach 1:
The patent segments gravity center movement data into discrete patterns through clustering analysis. The analysis unit divides continuous sitting behavior data into distinct movement patterns (e.g., forward lean, backward lean, lateral movement) that can be individually analyzed and correlated with low back pain risk, transforming complex continuous data into manageable discrete categories.
Solution Approach 2:
The system performs preliminary clustering analysis on gravity center movement data to establish pattern templates before actual prediction. By pre-processing and categorizing movement data into standardized patterns, the system creates a foundation for more accurate real-time prediction without requiring complex analysis during the prediction phase itself.
2Loss of information
If clustering analysis is applied to classify gravity center movement data, then sitting posture patterns are effectively identified, but data processing complexity increases
Solution Approach 1:
The clustering analysis operates autonomously on the gravity center movement data without requiring manual intervention or complex external processing. The analysis unit automatically identifies patterns and categorizes movements based on inherent data characteristics, allowing the system to self-organize the information and reduce the need for external computational resources.
Data Source
AI summary
According to an aspect of the invention, there is provided a low back pain analysis device comprising: a processor; and a storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by the processor, perform processing of: obtaining a relationship between a result of a pattern and a low back pain, using the result of the pattern obtained by classifying gravity center movement data acquired by a sensor, which is attached to furniture and acquires the gravity center movement data for a sitting period including a period for which a person is sitting on the furniture, using clustering.