LoRa Based IoT Platform for Remote Monitoring of Large-Scale Agriculture Farms in Chile.

Nowadays, conventional agriculture farms lack high-level automated management due to the limited number of installed sensor nodes and measuring devices. Recent progress of the Internet of Things (IoT) technologies will play an essential role in future smart farming by enabling automated operations with minimum human intervention. The main objective of this work is to design and implement a flexible IoT-based platform for remote monitoring of agriculture farms of different scales, enabling continuous data collection from various IoT devices (sensors, actuators, meteorological masts, and drones).

Automatic Registration of Footsteps in Contact Regions for Reactive Agility Training in Sports.

In collective sports, reactive agility training methodologies allow to evaluate and improve the player performance, being able to consider a mixture of technical, tactical, physical, and psychological abilities, similarly to real game-play situations. In this article, we present a new methodology for reactive agility training (neural training), the technological setup for the methodology, and a new footstep tracking algorithm, as the key element for automating the speed data gathering process, necessary for obtaining the relevant variables of the neural training approach. This new methodology is oriented to accurately measure two of the most relevant variables for reactive agility training: total response time (sprint time) and response correctness, related to a stimuli sequence presented to a player.

Physical fitness standards in students from province of Neuquén, Argentina. Physical Fitness Assessment Plan study.

Introduction: At present, there is no information about the physical fitness (PF) of children and adolescents attending school in the province of Neuquén. The provincial Department of Sports developed the Physical Fitness Assessment Plan. The main objective of this study was to administer the ALPHA-Fitness test battery to the students of Neuquén in order to develop PF reference standards.

Video-understanding framework for automatic behavior recognition.

We propose an activity-monitoring framework based on a platform called VSIP, enabling behavior recognition in different environments. To allow end-users to actively participate in the development of a new application, VSIP separates algorithms from a priori knowledge. To describe how VSIP works, we present a full description of a system developed with this platform for recognizing behaviors, involving either isolated individuals, groups of people, or crowds, in the context of visual monitoring of metro scenes, using multiple cameras.