l-isaro/Formative-2---Hidden-Markov-Models

# Formative 2 – Human Activity Recognition using Hidden Markov Models **Course:** Machine Learning Techniques – African Leadership University (2025) **Authors:** Leslie Isaro & David Ubushakebwimana --- ## Project Overview This project applies **Hidden Markov Models (HMMs)** to classify smartphone motion-sensor data into human activities. Each group member recorded accelerometer and gyroscope signals for **Jumping, Standing, Still, and Walking** using the **Sensor Logger** app. The dataset was preprocessed, feature-engineered in both time and frequency domains, and modeled with an HMM to infer hidden activity states from noisy sensor measurements. The goal was to design a full machine learning pipeline that: 1. Collects and cleans real-world motion data, 2. Extracts time and frequency domain features, 3. Trains an HMM to model each activity’s temporal dynamics, 4. Evaluates model accuracy on unseen data. --- ## Key Notebook Sections | Section | Purpose | |----------|----------| | **Data Loading & Visualization** | Loads accelerometer & gyroscope CSVs, merges, resamples, plots raw data. | | **Feature Extraction** | Computes time-domain (mean, std, RMS, SMA) and frequency-domain (FFT, spectral entropy) features. | | **Normalization & Clipping** | Applies Z-score normalization, removes NaNs/Infs, and clips outliers. | | **HMM Training & Decoding** | Fits a `GaussianHMM` for each activity, decodes sequences using Viterbi. | | **Evaluation (LOO)** | Tests on unseen clips, calculates accuracy, sensitivity, specificity, and confusion matrices. | | **Reflection** | Analyzes misclassifications and suggests improvements. | --- ## How to Run 1. **Install dependencies** ```bash pip install -r requirements.txt ``` 2. **Launch Jupyter Notebook** Start the notebook environment and open the main analysis file: ```bash jupyter notebook HAR_HMM_all_activities.ipynb ``` 4. **Prepare your data** - Place all `*_acc_*.csv` and `*_gyro_*.csv` files in the `data/` folder. - En …