Detection of Motion on a Trampoline with a Smartwatch

In this study, we develop a method for detecting the motions performed on a trampoline using an accelerometer mounted on a smartwatch. This method will lead to a system that can be used to promote trampoline exercise using a home trampoline by detecting motions on the trampoline using a smartwatch. We proposed a method based on the convolutional neural network to detect the motions on a trampoline. As a result of the performance evaluation by leave-one-subject-out cross-validation on eight subjects, our method achieves 78.8% estimation accuracy, which is the best estimation accuracy compared to the baseline methods. We also evaluate the inference time and the battery consumption when the model is actually running on a smartwatch. Our method is effective for on-device prediction.

Microcontrollers Performance in Portable Electronic Stethoscope Design with a Disease Symptoms Detection Feature

One of the early examinations that is often done is to detect heart disease using a stethoscope. The electronic stethoscope consists of a membrane and tube from a conventional stethoscope coupled with a condenser microphone which is then connected to a computer. The purpose of this study is to analyze the comparison of two types of microcontrollers in the design of a portable electronic stethoscope equipped with a symptom detector. The research method used is instrumentation with 2 types of microcontrollers to design a heart sound detector. In processing the data to be displayed on the 16x2 Character LCD. Sending heart signal data for 60 seconds to produce BPM data which is processed using 2 different types of microcontrollers. The results of data collection on battery consumption of power usage on the AT mega 2560 resulted in an average saving of 0.11W. Therefore, it can be concluded that the two stethoscopes have a significant difference when compared, where the Arduino Mega 2560 is able to process data from heart signals faster than the AT mega 328P. The results of the research that have been carried out can be implemented using a system that strongly supports the needs when checking heart sound signals

Low-Power Failure Detection for Environmental Monitoring Based on IoT

Many environmental monitoring applications that are based on the Internet of Things (IoT) require robust and available systems. These systems must be able to tolerate the hardware or software failure of nodes and communication failure between nodes. However, node failure is inevitable due to environmental and human factors, and battery depletion in particular is a major contributor to node failure. The existing failure detection algorithms seldom consider the problem of node battery consumption. In order to rectify this, we propose a low-power failure detector (LP-FD) that can provide an acceptable failure detection service and can save on the battery consumption of nodes. From simulation experiments, results show that the LP-FD can provide better detection speed, accuracy, overhead and battery consumption than other failure detection algorithms.

Pushed SOLID: Deploying SOLID in Smartphones

Personal information has become one of the most valuable coins on the Internet. Companies gather a massive amount of data to create rich profiles of their users, trying to understand how they interact with the platform and what are their preferences. However, these profiles do not follow any standard and are usually incomplete in the sense that users provide different subsets of information to distinct platforms. Thus, the quality and quantity of the data vary between applications and tends to inconsistency and duplicity. In this context, the Social Linked Data (SOLID) initiative proposes an alternative to separate the user’s information from the platforms which consume it, defining a unique and autonomous source of data. Following this line, this study proposes Pushed SOLID, an architecture that integrates SOLID in the user’s smartphone to store and serve their information from a single entity controlled by the users themselves. In this study, we present an implementation of the Pushed SOLID proposal with the aim of experimentally assessing the technical viability of the solution. Satisfactory performance results have been obtained at battery consumption and response time. Furthermore, users have been interviewed about the proposal, and they find the solution attractive and reliable. This solution can improve the way data are stored on the Internet, empowering users to manage their own information and benefiting third party applications with consistent and update profiles.

Location tracking using LoRa

<span>Local area network (LAN) as Bluetooth, WiFi and ZigBee are well established technology. The biggest problem with many LAN is the battery consumption and short ranges link budgets. LoRa is a new, private, unlicensed and spread spectrum modulation technique which allows sending low rates at extremely long ranges with minimal power consumption. More importantly, there is no access fee associated with this type of wireless technology. The main idea behind this work is to conduct performance and capability analysis of a currently available LoRa transceiver. We develop a location monitoring system using LoRa and global positioning system (GPS) module and we analyze the detectable range of its data, its battery consumption as well as received signal strength indicator (RSSI). Our deployment experiment demonstrates that the sy<span>stem is able to detect the transmitted data within 290 meters of distances. Using 6 volts of battery AA, the transmission of data still occurred after 24 hours</span>. <span>This project is emphasized a location monitoring system that provide low power usage</span> but long range.</span>

Implicit detection of user handedness in touchscreen devices through interaction analysis

Mobile devices now rival desktop computers as the most popular devices for web surfing and E-commerce. As screen sizes of mobile devices continue to get larger, operating smartphones with a single-hand becomes increasingly difficult. Automatic operating hand detection would enable E-commerce applications to adapt their interfaces to better suit their user’s handedness interaction requirements. This paper addresses the problem of identifying the operative hand by avoiding the use of mobile sensors that may pose a problem in terms of battery consumption or distortion due to different calibrations, improving the accuracy of user categorization through an evaluation of different classification strategies. A supervised classifier based on machine learning was constructed to label the operating hand as left or right. The classifier uses features extracted from touch traces such as scrolls and button clicks on a data-set of 174 users. The approach proposed by this paper is not platform-specific and does not rely on access to gyroscopes or accelerometers, widening its applicability to any device with a touchscreen.

Analysis of Battery Management Algorithms on DC Microgrids

Management of battery at direct current (DC) microgrids is the essential factor to maintain the balance of power and the bus voltage's stability in the grids. To ensure the quality of battery management is necessary to simulate the operation of the battery management system. This paper presents the simulations in various battery management algorithms. The simulations were designed to determine the effect of these variations on the balance of the power balance, bus voltage stability, and battery consumption level. The configurations of one, two, and three battery groups could maintain a balance of power balance. The three arrangements could ensure the bus voltage stability at a value of 24 Volts. The variations in the battery group configurations cause different battery consumption levels. The three-battery group configuration has a lower power consumption rate of 0.1% than other battery group configurations. Variations in the battery management algorithms affect power balance, bus voltage stability, and battery electricity consumption. The result showed the best power balance achieved by an algorithm without counting a value-based state of charge (SoC). The algorithm also committed that the difference between the supply and demand equal to 0 Watts. For the voltage stability, the algorithms that were counting a value-based SoC can maintain bus voltage stability at a value of 24 Volts. Nevertheless, other algorithms that rely on less than one SoC value-based and have the lowest mean value of SoC reduction are equal to 0.19%.