Machine learning methods for fall detection

This paper outlines the construction and validation of a sophisticated fall detection system using time series data from 3-axial accelerometers embedded in insoles and Machine Learning (ML) algorithms. The research captures and labels movements using a thorough data gathering technique, emphasizing distinct fall patterns, especially in the z-axis values. The research uses meticulous data processing, feature engineering, and exploration of multiple ML methods to build an advanced ensemble model. This model has an F1 score of 0.95, which is more than suitable for the considerate application, and is capable of differentiating between fall and non-fall events. The results highlight the proposed system's potential for real-time fall detection and have important implications for raising the safety of those who are at risk of falling.