Human Activity Classification

Advanced ML pipeline for sensor-based activity recognition

Human Activity Classification using Body Sensor Data

This project implements an advanced machine learning pipeline for human activity classification using multi-dimensional time-series data from body-worn sensors. The system processes and analyzes data from the AReM (Activity Recognition system based on Multisensor data fusion) dataset to classify seven distinct human activities.

Project Highlights

  • 94% Classification Accuracy across seven distinct human activities
  • 12% Accuracy Improvement through advanced preprocessing and optimization
  • Comprehensive ML Pipeline implementation for time-series data
  • Robust Feature Engineering with multiple selection techniques
Data Engineering Pipeline: From raw sensor data to final evaluation

Technical Architecture

Advanced Feature Engineering

  1. Time Series Processing
    • Dynamic segmentation up to 20 segments per series
    • Temporal pattern extraction
    • Local characteristic analysis
  2. Statistical Feature Extraction 
    features = {
'statistical': ['min', 'max', 'mean', 'median'],
'distribution': ['std', 'q1', 'q3'],
'temporal': ['sliding_windows', 'segment_analysis']
}
  3. Dimensionality Reduction
    • Principal Component Analysis (PCA)
    • Recursive Feature Elimination (RFE)
    • Cross-validated feature selection
Visualization of the feature engineering process, showcasing the transformation from raw data to engineered features.

Technical Implementation

Class Imbalance Solutions

  • SMOTE Implementation
  • Synthetic sample generation
  • Balanced class distribution
  • Enhanced model robustness

Model Optimization

  1. Regularization Techniques
    • L1 (Lasso) for feature sparsity
    • L2 (Ridge) for overfitting prevention
    • Cross-validation for hyperparameter tuning
  2. Performance Metrics ```python

metrics = { ‘accuracy’: 0.94, ‘improvement’: ‘12%’, ‘cross_validation’: ‘5-fold’, ‘evaluation’: [‘confusion_matrix’, ‘ROC_curves’] } ``` This project demonstrates the effective application of advanced machine learning techniques to classify human activities from sensor data. The implemented pipeline achieves high accuracy through careful feature engineering, model optimization, and handling of class imbalances. — For more details, visit the GitHub repository