Towards an AI-Driven Elderly Assistance Framework with Multi-Sensor Data for Real-Time Fall Detection

Wearable sensors enable continuous human activity monitoring for health, rehabilitation, and assistive applications. This study investigates the feasibility of a belt-mounted array of multi-placement Inertial Measurement Units (IMUs) for real-time fall detection and activity recognition. A deep learning framework based on Long Short-Term Memory (LSTM) networks is developed and compared against classical machine learning models, including Support Vector Machines (SVM), Random Forest, and XGBoost. The experimental setup employs a custom prototype integrating the Adafruit ICM-20948 IMU sensor across three different devices: a knee-mounted sensor and a waist-mounted sensor, along with the Huzzah32 microcontroller, utilizing Bluetooth Low Energy (BLE) for real-time data transmission. Experimental results show that the LSTM model achieves the highest recognition accuracy of 93.6% using data from a knee-mounted sensor, outperforming all traditional machine learning models such as Random Forest, SVM, and XGBoost. These findings underscore the potential of IMU-based wearable systems for reliable and portable fall detection, contributing to enhanced elderly home care and emergency response applications.