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🤖 ML Walkway - Machine Learning for Gait Analysis

The ML Walkway is a comprehensive machine learning system designed for advanced gait analysis using computer vision and deep learning techniques. This module provides a complete pipeline from data collection to prediction, making biomechanical analysis more accessible and automated.

🎯 Overview

The ML Walkway system integrates multiple components:

  • Data Collection: Automated video processing and feature extraction
  • Model Training: Custom deep learning models for gait parameters
  • Model Validation: Comprehensive validation and testing frameworks
  • Real-time Prediction: Live analysis capabilities
  • YOLO Integration: State-of-the-art object detection for tracking

🛠️ Available Tools

vaila_mlwalkway

📋 Module Information

  • Category: Ml
  • File: vaila\vaila_mlwalkway.py
  • Lines: 104

  • Size: 2844 characters

  • GUI Interface: ✅ Yes

📖 Description

vaila_mlwalkway.py

Create by: Abel G. Chinaglia & Paulo R. P. Santiago LaBioCoM - Laboratory of Biomechanics and Motor Control Date: 10.Feb.2025 Update: 24.Feb.2025

This module provides a graphical user interface (GUI) for executing various machine learning (ML) tasks related to gait analysis using the VAILA system. The GUI includes buttons for: 1. Processing gait features from MediaPipe data (use pixel data from MediaPipe in button vailá -> Makerless 2D). 2. Training ML models (use features in extract -> ). 3. Validating trained ML models. 4. Running ML predictions using pre-trained models.

Each button triggers the respective function that executes the corresponding ML pipeline.

🔧 Main Functions

Total functions found: 5

  • run_process_gait_features
  • run_ml_models_training
  • run_ml_valid_models
  • run_walkway_ml_prediction
  • run_vaila_mlwalkway_gui

📅 Gerado automaticamente em: 08/10/2025 10:07:00
🔗 Part of vailá - Multimodal Toolbox
🌐 GitHub Repository