An Ultrasonic-based Metrological Approach for Fall Detection and User recognition using Supervised and Unsupervised Machine Learning Techniques

The rapid aging of the world's population has increased the demand for innovative fall detection systems to ensure the safety and well-being of older people. Falls represent a major health risk, often leading to hospitalization, loss of independence, and increased caregiver burden. Traditional fall detection solutions, such as wearable sensors and camera-based systems, face challenges related to user compliance, privacy, and limited coverage areas. This study presents a novel ultrasonic (US)-based approach that combines fall detection and user identification using machine learning (ML) techniques. Unlike traditional wearable sensors, US technology offers a noninvasive and privacy-preserving alternative for monitoring falls in home and nursing home environments. The proposed system utilizes an US mounted on the ceiling for a dual purpose: fall detection and user identification. For fall detection, two ML algorithms, Random Forest and Support Vector Machine (SVM), were tested. The dataset was split using an 80/20% ratio, allocating 80% of the data for training and 20% for testing. Results indicate that Random Forest achieved high accuracy (up to 89%), demonstrating high reliability in detecting falls with minimal false negatives. Additionally, for user identification, an unsupervised K-means clustering algorithm was employed, achieving a classification accuracy of 70% based on walking patterns and height of individuals. These findings highlight the effectiveness of US-based monitoring in both detecting falls and user classification. Future work will focus on improving classification accuracy, and validating the system in real-world environments.