A cross-dataset deep learning-based classifier for people fall detection and identification - Delgado-Escaño, Rubén; Castro, Francisco M.; Ramos-Cózar, Julián; Marín-Jiménez, Manuel J.; Guil-Mata, Nicolás; Casilari-Pérez, Eduardo (Elsevier, 2020)
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.
Keywords: Ancianos - Protección, asistencia, etc. - Innovaciones tecnológicas; Fall detection; Activities of daily living; Inertial sensors; Convolutional neural network; Long short-term memory; Multi-task