This Fall Detection project explores various machine learning techniques to detect falls using accelerometer data. It includes exploratory data analysis, feature engineering, and the implementation of several classification models.
Key features:
- Exploratory Data Analysis (EDA) of accelerometer data
- Feature extraction and engineering
- Implementation of various machine learning models:
- Logistic Regression
- Support Vector Machines (SVM)
- Linear Discriminant Analysis (LDA)
- Class imbalance handling techniques
- Principal Component Analysis (PCA) for dimensionality reduction
The repository contains R markdown files for different analyses and model implementations, as well as data files and visualizations.