Slips, trips and falls are among the main causes of accidents in a workplace. For this reason, many fall detection approaches have been proposed in the literature. One of the most important categories of approaches is based on the usage of wearable devices. These devices have many advantages, but they also pose some challenging open issues. In particular, they must not be bulky, must have low power consumption and must be able to optimize the low computational power available. In this paper, we aim at facing these challenges by proposing SaveMeNow.AI, a new wearable device for fall detection. SaveMeNow.AI is based on the deployment of a Machine Learning approach for fall detection embedded in it. This approach exploits data continuously measured by a six-axis IMU present inside the device.
SaveMeNow.AI: A Machine Learning Based Wearable Device for Fall Detection in a Workplace
De Donato, Massimo Callisto;
2021-01-01
Abstract
Slips, trips and falls are among the main causes of accidents in a workplace. For this reason, many fall detection approaches have been proposed in the literature. One of the most important categories of approaches is based on the usage of wearable devices. These devices have many advantages, but they also pose some challenging open issues. In particular, they must not be bulky, must have low power consumption and must be able to optimize the low computational power available. In this paper, we aim at facing these challenges by proposing SaveMeNow.AI, a new wearable device for fall detection. SaveMeNow.AI is based on the deployment of a Machine Learning approach for fall detection embedded in it. This approach exploits data continuously measured by a six-axis IMU present inside the device.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
