Unveiling Fall Origins: Leveraging Wearable Sensors to Detect Pre-Impact Fall Causes

Falling poses a significant challenge to the health and well-being of the elderly and people with various disabilities. Precise and prompt fall detection plays a crucial role in preventing falls and mitigating the impact of injuries. In this research, we propose a deep classifier for pre-impact fall detection which can detect a fall in the pre-impact phase with an inference time of 46-52 ms. The proposed classifier is an ensemble of convolutional neural networks (CNNs) and bidirectional gated recurrent units (BiGRUs) with residual connections. We validated the performance of the proposed classifier on a comprehensive, publicly available pre-impact fall dataset. The dataset covers 36 diverse activities, including 15 types of fall-related activities and 21 types of activities of daily living (ADLs). Furthermore, we evaluated the proposed model using three different inputs of varying dimensions: 6-D input (comprising 3-D accelerations and 3-D angular velocities), 3-D input (3-D accelerations), and 1-D input (magnitude of 3-D accelerations). The reduction in the input space from 6-D to 1-D is aimed at minimizing the computation cost. We have attained commendable results outperforming the state-of-the-art approaches by achieving an average accuracy and {F}1 -score of 98% for 6-D input size. The potential implications of this research are particularly relevant in the realm of smart healthcare, with a focus on the elderly and differently abled population.