Fall detection is an assistive technology for elderly people that helps in emergency situations. This work presents the development of a wearable device to detect falls connected to a ultra low power wireless network. The device is connected to a smart home system to trigger alarms when events are detected. The fall detection is done by a threshold algorithm based on data fusion from inertial sensors. The wearable sensor is based on EnOcean protocol, which includes a wireless connection with a smart home system, according to the KNX standard, through the Home Assistant platform. The tests were performed in a prototype and the results include the evaluation of fall and nonfall movements in two different body characteristics. The results revealed sensitivity and specificity of up to 96% and 100%, respectively.
Cattle monitoring is an important demand in precision livestock and crossbreed quality control. Previous studies and products have been proposed to approach this problem, although several factors pose challenges for real-time data acquisition and analysis. In this work, we present a proof of concept prototype for a cattle crossbreed monitoring system based on wireless sensor networks. The hardware implementation, the sensor data acquisition system and the field tests are described in detail. Supervised machine learning algorithms are applied for copulation detection and the classification metrics show that some of the proposed models have good sensitivity, suggesting promising directions for future steps and optimization.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.