Video Motion Vector Analysis for Neurological Disorder Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for detecting depression and neurological disorders, such as abnormal gait patterns, require physical presence of physicians and rely on questionnaires and clinical equipment, which are inefficient and fail to accurately identify fine movements and dominant motion regions, limiting remote management and precision in diagnosis.
Innovation Solution
A system and method that uses video analysis to detect regions of interest, determine motion vectors, and compare them with pre-stored values to identify expressions and gait patterns, eliminating the need for questionnaires and clinical equipment, and enabling remote monitoring of depression and neurological disorders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video analysis with motion vector comparison is used, then measurement precision of fine movements is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex clinical equipment and manual examination systems with a video-based computational system. By using standard video recording devices combined with image processing algorithms (motion vector analysis), the system achieves precise detection of fine movements without requiring specialized medical equipment, thus improving measurement precision while managing device complexity through substitution of mechanical/clinical systems with optical-computational systems.
Solution Approach 2:
The patent uses video copies (recordings) of patient movements instead of requiring real-time presence of physicians or use of complex clinical devices. The video frames are analyzed to extract motion information, creating a digital replica of the physical examination process. This allows precise measurement of fine movements through computational analysis of the video copies, reducing the need for complex physical examination equipment.
2Ease of operation
If remote monitoring system is implemented, then ease of operation is improved, but reliability of diagnosis deteriorates
Solution Approach 1:
The patent replaces the mechanical requirement of physician presence with an automated video analysis system. The system uses computer vision algorithms to automatically detect and analyze gait patterns and facial expressions, substituting human physical examination with computational analysis. This enables remote monitoring while maintaining diagnostic reliability through objective, algorithm-based measurement of movement parameters that are comparable to clinical assessment.
Solution Approach 2:
The system implements automated feedback loops where video analysis results are continuously compared against established patterns for neurological disorders. The motion vector analysis provides quantitative feedback on gait parameters, and the system can automatically identify deviations from normal patterns. This feedback mechanism ensures diagnostic reliability by providing objective, measurable data that can be consistently evaluated without requiring continuous physician involvement.
3Productivity
If automated video analysis is used, then productivity is improved, but measurement precision of expressions deteriorates
Solution Approach 1:
The patent segments the video analysis process into distinct stages: frame extraction, region of interest identification, motion vector calculation, and pattern recognition. By dividing the complex task of expression and gait analysis into these manageable segments, the system can process videos efficiently (improving productivity) while applying specialized algorithms to each segment to maintain high measurement precision. The segmentation allows parallel processing and optimization of each individual step without compromising overall accuracy.
Data Source
AI summary
A method and system for predicting neurological disorders is provided. The method comprises receiving videos of individuals and detecting Regions of Interest (ROI) in video frames. The method further comprises determining a Motion Vector (MV) for each ROI in a set of successive frames and comparing value of the determined MV with pre-stored values. Furthermore, the method comprises identifying a MV matching a pre-stored value thereby identifying a ROI and a frame corresponding to the identified MV, wherein the pre-stored value indicates onset of an expression. Also, the method comprises determining MVs for the identified ROI in subsequent sets of successive frames and comparing value of the determined MVs with a pre-stored value of MV corresponding to peak and offset of the indicated expression. The method further comprises identifying the frame corresponding to the peak and offset of the indicated expression and generating pictorial representation for predicting neurological disorders.