The measurement of environmental variables has become a daily problem in recent years. However, the equipment commonly used for these measurements is expensive and bulky, and therefore, it is not possible to have enough spatial resolution. In addition, many of the measurement methods do not provide real-time information to deliver to citizens in a timely manner. In some works, these issues have been handled through the deployment of WSN based on low cost technologies. The improvement of the spatial and temporal resolution implies the increase in the amount of information to be transmitted and stored. For this reason, in this paper is presented a method for data reduction through a dynamic sub-sampling of the measured variable, data fusion from several sensors for the same variable, and data scaling taking into account the the variables range. The reduction of data is implemented to save energy, reduce the transmission time, keep the channel available, and save storage space. The method is validated using a low-cost monitoring station that combines environmental, particulate matter, gas, electromagnetic radiation, and inertial sensors to be transmitted in a 50-byte reduced packet using a LoRa network. The subsampling adjustment was developed for the PM signal. The results show a reduction in the volume of stored data and the relevant information is not affected. The transmitted data packet can be reduced from 96 to 50 bytes, and sampling can be reduced to 4% of the original sampling without affecting the trend of the PM information.

Motion capture (MOCAP) is a widely used technique to record human, animal, and object movement for various applications such as animation, biomechanical assessment, and control systems. Different systems have been proposed based on diverse technologies, such as visible light cameras, infrared cameras with passive or active markers, inertial systems, or goniometer-based systems. Each system has pros and cons that make it usable in different scenarios. This paper presents a dataset that combines Optical Motion and Inertial Systems, capturing a well-known sports movement as the vertical jump. As a reference system, the optical motion capture consists of six Flex 3 Optitrack cameras with 100 FPS. On the other hand, we developed an inertial system consisting of seven custom-made devices based on the IMU MPU-9250, which includes a three-axis magnetometer, accelerometer and gyroscope, and an embedded Digital Motion Processor (DMP) attached to a microcontroller mounted on a Teensy 3.2 with an ARM Cortex-M4 processor with wireless operation using Bluetooth. The purpose of taking IMU data with a low-cost and customized system is the deployment of applications that can be performed with similar hardware and can be adjusted to different areas. The developed measurement system is flexible, and the acquisition format and enclosure can be customized. The proposed dataset comprises eight jumps recorded from four healthy humans using both systems. Experimental results on the dataset show two usage examples for measuring joint angles and COM position. The proposed dataset is publicly available online and can be used in comparative algorithms, biomechanical studies, skeleton reconstruction, sensor fusion techniques, or machine learning models.

The dynamic reconfiguration and maximum power point tracking in large-scale photovoltaic (PV) systems require a large number of voltage and current sensors. In particular, the reconfiguration process requires a pair of voltage/current sensors for each panel, which introduces costs, increases size and reduces the reliability of the installation. A suitable solution for reducing the number of sensors is to adopt image-based solutions to estimate the electrical characteristics of the PV panels, but the lack of reliable data with large diversity of irradiance and shading conditions is a major problem in this topic. Therefore, this paper presents a dataset correlating RGB images and electrical data of PV panels with different irradiance and shading conditions; moreover, the dataset also provides complementary weather data and additional image characteristics to support the training of estimation models. In particular, the dataset was designed to support the design of image-based estimators of electrical data, which could be used to replace large arrays of sensors. The dataset was captured during 70 days distributed between 2020 and 2021, generating 5211 images and registers. The paper also describes the measurement platform used to collect the data, which will help to replicate the experiments in different geographical locations.

The physical challenges of human development reflected in the sport are increasingly technology-related. This relationship can be demonstrated in several lines; design on sports elements using high technology as well as sports analysis systems that trying to enhance technical indicators in every discipline. The analysis systems facilitate the feedback process to coaches and athletes, based on mathematical models using derived measures, avoiding precision errors of human observations. One meaningful measure for coaches, strength and conditioning professionals, and sport professionals in general is the vertical jump height. In this paper the initial development of a vertical jump height measurement system is presented, that allows to know the flight height of a person in the development of exercise, and his flight time. The system relies on information from an Inertial Measurement Unit (IMU) consisting of an triaxial accelerometer, a triaxial gyroscope and a triaxial magnetometer and two pressure sensors that fuse the jump measured data. The equipment is completely portable and wireless allowing measurements under standard conditions and these can be developed at the same time with the sporting activity. In average were obtained a flight time of 0.4720s and a maximum height of 0.3160m. The results evidence that these systems based on initiatives of low-cost national technology can be at the level of the products used commercially. Finally, it is important to note that the development of these devices can be complemented with specific applications that automatically process data to support the practice of sports.