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 milliseconds. The proposed classifier is an ensemble of Convolutional Neural Networks (CNNs) and Bidirectional Gated Recurrent Units (BiGRU) with residual connections. We validated the performance of the proposed classifier on a comprehensive, publicly available preimpact 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:
6D input
(comprising 3D accelerations and 3D angular velocities),
3D input
(3D accelerations), and
1D input
(magnitude of 3D accelerations). The reduction in the input space from 6D to 1D 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 F1 score of 98% for 6D 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.