@article{ref1,
title="Energy-aware IoT-based method for a hybrid on-wrist fall detection system using a supervised dictionary learning technique",
journal="Sensors (Basel)",
year="2023",
author="Othmen, Farah and Baklouti, Mouna and Lazzaretti, André Eugenio and Hamdi, Monia",
volume="23",
number="7",
pages="e3567-e3567",
abstract="In recent decades, falls have posed multiple critical health issues, especially for the older population, with their emerging growth. Recent research has shown that a wrist-based fall detection system offers an accessory-like comfortable solution for Internet of Things (IoT)-based monitoring. Nevertheless, an autonomous device for anywhere-anytime may present an energy consumption concern. Hence, this paper proposes a novel energy-aware IoT-based architecture for Message Queuing Telemetry Transport (MQTT)-based gateway-less monitoring for wearable fall detection. Accordingly, a hybrid double prediction technique based on Supervised Dictionary Learning was implemented to reinforce the detection efficiency of our previous works. A controlled dataset was collected for training (offline), while a real set of measurements of the proposed system was used for validation (online). It achieved a noteworthy offline and online detection performance of 99.8% and 91%, respectively, overpassing most of the related works using only an accelerometer. In the worst case, the system showed a battery consumption optimization by a minimum of 27.32 working hours, significantly higher than other research prototypes. The approach presented here proves to be promising for real applications, which require a reliable and long-term anywhere-anytime solution.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s23073567",
url="http://dx.doi.org/10.3390/s23073567"
}