Falls represent a major public health risk worldwide for the elderly people. A fall not assisted in time can cause functional impairment in an elderly and a significant decrease in his mobility, independence, and life quality. In this sense, we propose IoTE-Fall system, an intelligent system for detecting falls of elderly people in indoor environments that takes advantages of the Internet of Thing and the ensemble machine learning algorithm. IoTE-Fall system employs a 3D-axis accelerometer embedded into a 6LowPAN wearable device capable of capturing in real time the data of the movements of elderly volunteers. To provide high efficiency in fall detection, in this paper, four machine learning algorithms (classifiers): decision trees, ensemble, logistic regression, and Deepnets are evaluated in terms of AUC ROC, training time and testing time. The acceleration readings are processed and analyzed at the edge of the network using an ensemble-based predictor model that is identified as the most suitable predictor for fall detection. The experiment results from collection data, interoperability services, data processing, data analysis, alert emergency service, and cloud services show that our system achieves accuracy, precision, sensitivity, and specificity above 94%.
Falls is one of most concerning accidents in aged population due to its high frequency and serious repercussion; thus, quick assistance is critical to avoid serious health consequences. There are several Ambient Assisted Living (AAL) solutions that rely on the technologies of the Internet of Things (IoT), Cloud Computing and Machine Learning (ML). Recently, Deep Learning (DL) have been included for its high potential to improve accuracy on fall detection. Also, the use of fog devices for the ML inference (detecting falls) spares cloud drawback of high network latency, non-appropriate for delay-sensitive applications such as fall detectors. Though, current fall detection systems lack DL inference on the fog, and there is no evidence of it in real environments, nor documentation regarding the complex challenge of the deployment. Since DL requires considerable resources and fog nodes are resource-limited, a very efficient deployment and resource usage is critical. We present an innovative highly-efficient intelligent system based on a fog-cloud computing architecture to timely detect falls using DL technics deployed on resource-constrained devices (fog nodes). We employ a wearable tri-axial accelerometer to collect patient monitoring data. In the fog, we propose a smart-IoT-Gateway architecture to support the remote deployment and management of DL models. We deploy two DL models (LSTM/GRU) employing virtualization to optimize resources and evaluate their performance and inference time. The results prove the effectiveness of our fall system, that provides a more timely and accurate response than traditional fall detector systems, higher efficiency, 98.75% accuracy, lower delay, and service improvement.
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