This paper addresses the issue of fall detection, particularly for elderly individuals who may live alone and be unable to call for help after a fall. The objective is to develop a deep learning-based approach that can detect falls and identify individuals without needing model fine-tuning for different datasets. The proposed method uses a multi-task learning model that processes raw inertial data to simultaneously detect falls and identify people. The model achieves over 98% accuracy in fall detection across four datasets, with less than 1.6% false positives, and identifies people with an average accuracy of 79.6%. It operates in real-time, requiring no retraining for new subjects, making it suitable for practical implementation.
