Falls and their aftermath pose significant healthcare challenges, impacting individuals across various age groups and occupational backgrounds. These incidents detrimentally affect functional mobility and overall quality of life, necessitating a comprehensive approach to fall detection systems in diverse populations. Therefore, wearable devices are necessary to continuously monitor activities. This work introduces a novel deep-learning model specifically optimized for edge devices capable of detecting falls. The wearable sensor integrates a pressure sensor, a three-axis gyroscope, and a three-axis accelerometer. The developed system works in real time with the dual objective of identifying the activities carried out and classifying them as falls or daily life activities. We evaluated this approach using both our self-collected dataset and a publicly available one (SisFall). Furthermore, in our dataset, we also introduced the syncope between falls that the sensor must be able to detect. Results demonstrate that while maintaining low cost, low complexity of the model, low-power consumption, and high-speed data processing, combining usage of the three sensors and deep learning (DL) algorithm allows to obtain an accuracy of 99.38% and an inference time of 25 ms.
A Novel Embedded Deep Learning Wearable Sensor for Fall Detection
Belli A.;
2024-01-01
Abstract
Falls and their aftermath pose significant healthcare challenges, impacting individuals across various age groups and occupational backgrounds. These incidents detrimentally affect functional mobility and overall quality of life, necessitating a comprehensive approach to fall detection systems in diverse populations. Therefore, wearable devices are necessary to continuously monitor activities. This work introduces a novel deep-learning model specifically optimized for edge devices capable of detecting falls. The wearable sensor integrates a pressure sensor, a three-axis gyroscope, and a three-axis accelerometer. The developed system works in real time with the dual objective of identifying the activities carried out and classifying them as falls or daily life activities. We evaluated this approach using both our self-collected dataset and a publicly available one (SisFall). Furthermore, in our dataset, we also introduced the syncope between falls that the sensor must be able to detect. Results demonstrate that while maintaining low cost, low complexity of the model, low-power consumption, and high-speed data processing, combining usage of the three sensors and deep learning (DL) algorithm allows to obtain an accuracy of 99.38% and an inference time of 25 ms.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
