Abnormal Gait Analysis using Video-based Signal Processing

This project focuses on the development of an advanced gait event and abnormality detection system using pose estimation techniques. The system incorporates a signal-based approach to improve the accuracy of gait event detection, outperforming traditional angle-based methods, resulted in a patented solution. The project involved analyzing a diverse range of abnormal gait patterns, including Antalgic, Ataxic, Hemiplegic, Parkinsonian, and Trendelenburg gaits, using an in-house database of human participants. Collaborating closely with MyMedicalHUB's (MMH) clinical team of physicians and physical therapists, the project ensured clinical relevance and accuracy.

This project focuses on the development of an advanced gait event and abnormality detection system using pose estimation techniques. The system incorporates a signal-based approach to improve the accuracy of gait event detection, outperforming traditional angle-based methods and resulting in a patented solution.

Innovation Overview

Real-time gait pattern analysis and abnormality detection (placeholder image)

Signal-Based Approach

Our innovative signal processing methodology represents a paradigm shift from traditional angle-based gait analysis. By analyzing temporal patterns in pose data as signals, we achieve:

  • Higher accuracy in gait event detection
  • Robust performance across different walking speeds
  • Better noise resilience in real-world conditions
  • Improved temporal precision for clinical measurements

Comprehensive Abnormal Gait Detection

Left: Various abnormal gait patterns analyzed. Right: Signal processing methodology. (placeholder images)

Abnormal Gait Pattern Analysis

The project involved analyzing a diverse range of abnormal gait patterns, including:

  • Antalgic Gait: Compensation patterns due to pain
  • Ataxic Gait: Unsteady, uncoordinated walking patterns
  • Hemiplegic Gait: Patterns resulting from stroke or brain injury
  • Parkinsonian Gait: Characteristic shuffling and reduced arm swing
  • Trendelenburg Gait: Hip drop due to gluteal weakness

Clinical Database

We utilized an extensive in-house database of human participants featuring:

  • Diverse patient populations
  • Various severity levels of gait abnormalities
  • Multiple walking conditions and environments
  • Longitudinal data for tracking progression

Clinical Collaboration

Clinical team collaboration and assessment process (placeholder images)

Interdisciplinary Approach

Collaborating closely with MyMedicalHUB’s (MMH) clinical team of physicians and physical therapists, the project ensured:

  • Clinical relevance of detected patterns
  • Accuracy validation against clinical standards
  • Practical applicability in healthcare settings
  • Evidence-based development approach

Technical Achievements

Patent Recognition

The innovative signal-based approach has been recognized with a patent, highlighting the novelty and clinical significance of our methodology.

Performance Metrics

  • Superior accuracy compared to angle-based methods
  • Real-time processing capabilities
  • Robust performance across different patient populations
  • High sensitivity for subtle gait abnormalities

System Capabilities

  • Automated gait event detection (heel strike, toe-off, etc.)
  • Abnormality classification with confidence scores
  • Temporal gait parameter extraction
  • Progress tracking over time
  • Clinical report generation

Clinical Applications

Diagnostic Support

The system provides healthcare professionals with:

  • Objective gait analysis metrics
  • Automated abnormality detection
  • Quantitative assessment tools
  • Longitudinal tracking capabilities

Rehabilitation Monitoring

Applications in physical therapy include:

  • Progress tracking during rehabilitation
  • Treatment effectiveness assessment
  • Personalized therapy plan adjustment
  • Remote monitoring capabilities

Technical Specifications

  • Input: Standard video recordings
  • Processing: Real-time pose estimation and signal analysis
  • Output: Gait parameters, abnormality classification, clinical reports
  • Accuracy: Superior to traditional angle-based methods
  • Database: Comprehensive in-house participant data
  • Validation: Clinical team verified results

This project was developed at MyMedicalHUB Corp., FL, USA, in collaboration with clinical experts. The resulting patented solution represents a significant advancement in automated gait analysis technology.