A Novel Feature Extraction Method for Preimpact Fall Detection System Using Deep Learning and Wearable Sensors

Abstract

Fall detection and prevention are crucial in elderly healthcare and humanoid robotic research as they help mitigate the damaging after-effects of falls. In this work, we have presented a deep-learning-based preimpact fall detection system (FDS) that detects a fall within 0.5 s of the fall initiation phase, thus providing a sufficient lead time of 0.5 s which is far better than the state-of-the-art. To achieve this, we have developed an automatic feature extraction methodology that can extract temporal features from all types of human fall data collected using wearable sensors. A deep neural classifier based on the ensemble of convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) is trained on the extracted temporal features. The classifier has performed exceptionally well in detecting the Fall Initiation phase with a sensitivity of 99.24% and an F1-score of 98.79% for different types of falls. A sensitivity of 99.24% signifies that the model has sufficiently reduced the occurrence of false negatives, which is far more critical for an FDS. A concept of a transitional window is introduced to improve the reaction time of the FDS. We utilized two standard fall datasets, viz. SisFall and KFall, for the experimentation. Dataset fusion is employed to increase the generalizability of the system. This work can be utilized to design and develop fall detection devices for the Internet-of-Healthcare-Things applications (IoHT) and for imparting fall detection capabilities to humanoid robots and gait rehabilitation devices such as exoskeleton robots and smart prosthetic legs.