Motion Detection via DCT Analysis for AR Rehabilitation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing virtual and augmented reality systems for rehabilitation and learning lack efficient and cost-effective methods for accurately detecting and characterizing motion, particularly differentiating between voluntary and involuntary movements, and are often resource-intensive and difficult to operate.
Innovation Solution
A method and system for detecting and characterizing motion in a region of interest (ROI) by capturing time-lapsed images, generating a motion distribution, and analyzing it to identify motion, using a computer program with image capture and motion detection/characterization utilities that filter and correlate movement data with color histograms, allowing for sensitivity adjustment and differentiation between intentional and unintentional movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pixel-by-pixel threshold analysis is applied to detect motion, then motion detection capability is achieved, but computational resource consumption increases and operation becomes difficult
Solution Approach 1:
The patent divides the image into multiple regions of interest (ROIs) and processes each region separately using block-based DCT analysis. This segmentation approach reduces the computational burden compared to pixel-by-pixel analysis while maintaining motion detection accuracy within each region. The DCT coefficients are calculated for blocks of pixels rather than individual pixels, achieving a balance between precision and complexity.
Solution Approach 2:
The patent replaces the mechanical pixel-by-pixel threshold comparison method with a frequency-domain approach using Discrete Cosine Transform (DCT). This substitution transforms the motion detection problem from spatial domain thresholding to frequency domain analysis, reducing computational complexity while preserving motion detection capability through analysis of DCT coefficient variations.
2Measurement precision
If comprehensive motion analysis is performed to differentiate voluntary and involuntary movements, then measurement precision improves, but processing time increases
Solution Approach 1:
The patent performs preliminary motion detection by analyzing DCT coefficient variations to identify regions with motion activity before conducting detailed characterization. This preliminary analysis filters out static or non-interest areas, allowing subsequent detailed motion characterization to focus only on relevant regions, thereby reducing overall processing time while maintaining precision in differentiating movement types.
Solution Approach 2:
The patent applies different analysis depths to different regions based on their motion characteristics. Regions showing significant DCT coefficient changes undergo detailed motion characterization, while regions with minimal changes receive simpler processing. This local quality approach ensures high measurement precision for active regions while minimizing processing time for inactive areas.
3Measurement precision
If motion detection sensitivity is increased to capture subtle movements, then measurement precision improves, but false detections from inadvertent movements increase
Solution Approach 1:
The patent uses feedback from DCT coefficient analysis to dynamically adjust motion detection thresholds. By analyzing the distribution and magnitude of DCT coefficient changes across multiple blocks, the system establishes a baseline that distinguishes intentional movements from inadvertent ones. This feedback mechanism allows high sensitivity for detecting subtle intentional movements while filtering out false detections through statistical comparison against established patterns.
Solution Approach 2:
The patent changes the analysis parameter from simple pixel intensity differences to DCT frequency domain coefficients. This parameter transformation enables the system to detect subtle movements by analyzing frequency components while simultaneously filtering out high-frequency noise associated with inadvertent movements. The DCT parameter space provides a more robust basis for distinguishing intentional from unintentional motion.
Data Source
AI summary
A method for motion detection/characterization is provided including the steps of (a) capturing a series of time lapsed images of the ROI, wherein the ROI moves between at least two of such images; (b) generating a motion distribution in relation to the ROI across the series of images; and (c) identifying motion of the ROI based on analysis of the motion distribution. In a further aspect of motion detection/characterization in accordance with the invention, motion is detected/characterized based on calculation of a color distribution for a series of images. A system and computer program for presenting an augmented environment based on the motion detection/characterization is also provided. An interface means based on the motion detection/characterization is also provided.