Mathematical Modeling and Statistical Analysis of Elederly Fall Detection System Using Improved Support Vector Machine

Abstract

This research focuses on enhancing the safety of elderly individuals through early fall detection using mathematical modelling and statistical analysis of machine learning techniques for the application and effectiveness of elderly fall detection. Falls among the elderly can lead to severe consequences, necessitating timely intervention. Leveraging machine learning algorithms, this innovative open-source project analyses sensor data from wearable sensors like accelerometers, gyroscopes, and magnetometers, along with environmental data such as temperature and humidity, to promptly identify fall patterns. The project uses a dataset containing 14 variables, including age, sex, medical indicators, and more, collected from diverse subjects and activities. The results obtained during the testing phase underscore the importance of refining the model through dataset adjustments. As the physical, cognitive, and sensory functions decline with age, the risk of falls increases, highlighting the need for fall detection and prevention systems. This research reviews the latest machine learning-based systems for fall detection and prevention, analyzing them based on various parameters. It identifies support vector machines and wearable devices as common tools, but emphasizes the need for broader studies in different contexts. The paper also visualizes the performance metrics of ML algorithms in conjunction with various wearables and outlines future research directions, including energy efficiency, sensor fusion, context awareness, and wearable design, to advance fall detection and prevention for the elderly.