Real-Time Evaluation of Time-Domain Pulse Rate Variability Parameters in Different Postures and Breathing Patterns Using Wireless Photoplethysmography Sensor: Towards Remote Healthcare in Low-Resource Communities.

Photoplethysmography (PPG) signals have been widely used in evaluating cardiovascular biomarkers, however, there is a lack of in-depth understanding of the remote usage of this technology and its viability for underdeveloped countries. This study aims to quantitatively evaluate the performance of a low-cost wireless PPG device in detecting ultra-short-term time-domain pulse rate variability (PRV) parameters in different postures and breathing patterns. A total of 30 healthy subjects were recruited. ECG and PPG signals were simultaneously recorded in 3 min using miniaturized wearable sensors. Four heart rate variability (HRV) and PRV parameters were extracted from ECG and PPG signals, respectively, and compared using analysis of variance (ANOVA) or Scheirer-Ray-Hare test with post hoc analysis. In addition, the data loss was calculated as the percentage of missing sampling points. Posture did not present statistical differences across the PRV parameters but a statistical difference between indicators was found. Strong variation was found for the RMSSD indicator in the standing posture. The sitting position in both breathing patterns demonstrated the lowest data loss (1.0 ± 0.6 and 1.0 ± 0.7) and the lowest percentage of different factors for all indicators. The usage of commercial PPG and BLE devices can allow the reliable extraction of the PPG signal and PRV indicators in real time.

Velocity Prediction of a Pipeline Inspection Gauge (PIG) with Machine Learning.

A device known as a pipeline inspection gauge (PIG) runs through oil and gas pipelines which performs various maintenance operations in the oil and gas industry. The PIG velocity, which plays a role in the efficiency of these operations, is usually determined indirectly from odometers installed in it. Although this is a relatively simple technique, the loss of contact between the odometer wheel and the pipeline results in measurement errors. To help reduce these errors, this investigation employed neural networks to estimate the speed of a prototype PIG, using the pressure difference that acts on the device inside the pipeline and its acceleration instead of using odometers. Static networks (e.g., multilayer perceptron) and recurrent networks (e.g., long short-term memory) were built, and in addition, a prototype PIG was developed with an embedded system based on Raspberry Pi 3 to collect speed, acceleration and pressure data for the model training. The implementation of the supervised neural networks used the Python library TensorFlow package. To train and evaluate the models, we used the PIG testing pipeline facilities available at the Petroleum Evaluation and Measurement Laboratory of the Federal University of Rio Grande do Norte (LAMP/UFRN). The results showed that the models were able to learn the relationship among the differential pressure, acceleration and speed of the PIG. The proposed approach can complement odometer-based systems, increasing the reliability of speed measurements.

FASSVid: Fast and Accurate Semantic Segmentation for Video Sequences.

Most of the methods for real-time semantic segmentation do not take into account temporal information when working with video sequences. This is counter-intuitive in real-world scenarios where the main application of such methods is, precisely, being able to process frame sequences as quickly and accurately as possible. In this paper, we address this problem by exploiting the temporal information provided by previous frames of the video stream. Our method leverages a previous input frame as well as the previous output of the network to enhance the prediction accuracy of the current input frame. We develop a module that obtains feature maps rich in change information. Additionally, we incorporate the previous output of the network into all the decoder stages as a way of increasing the attention given to relevant features. Finally, to properly train and evaluate our methods, we introduce CityscapesVid, a dataset specifically designed to benchmark semantic video segmentation networks. Our proposed network, entitled FASSVid improves the mIoU accuracy performance over a standard non-sequential baseline model. Moreover, FASSVid obtains state-of-the-art inference speed and competitive mIoU results compared to other state-of-the-art lightweight networks, with significantly lower number of computations. Specifically, we obtain 71% of mIoU in our CityscapesVid dataset, running at 114.9 FPS on a single NVIDIA GTX 1080Ti and 31 FPS on the NVIDIA Jetson Nano embedded board with images of size 1024×2048 and 512×1024, respectively.

AntVideoRecord: Autonomous system to capture the locomotor activity of leafcutter ants.

The leafcutter ants (LCA) are considered plague in a great part of the American continent, causing great damage in production fields. Knowing the locomotion and foraging rhythm in LCA on a continuous basis would imply a significant advance for ecological studies, fundamentally of animal behavior. However, studying the forage rhythm of LCA in the field involves a significant human effort. This also adds a risk of subjective results due to the operator fatigue. In this work a new development named 'AntVideoRecord' is proposed to address this issue. This device is a low-cost autonomous system that records videos of the LCA path in a fixed position. The device can be easily reproduced using the freely accessible source code provided. The evaluation of this novel device was successful because it has exceeded all the basic requirements in the field: record continuously for at least seven days, withstand high and low temperatures, capture acceptable videos during the day and night, and have a simple configuration protocol by mobile devices and laptops. It was possible to confirm the correct operation of the device, being able to record more than 1900 h in the field at different climate conditions and times of the day.

Validation of Embedded State Estimator Modules for Decentralized Monitoring of Power Distribution Systems Using IoT Components.

Recent theoretical studies demonstrate the advantages of using decentralized architectures over traditional centralized architectures for real-time Power Distribution Systems (PDSs) operation. These advantages include the reduction of the amount of data to be transmitted and processed when performing state estimation in PDSs. The main contribution of this paper is to provide lab validation of the advantages and feasibility of decentralized monitoring of PDSs. Therefore, this paper presents an advanced trial emulating realistic conditions and hardware setup. More specifically, the paper proposes: (i) The laboratory development and implementation of an Advanced Measurement Infrastructure (AMI) prototype to enable the simulation of a smart grid. To emulate the information traffic between smart meters and distribution operation centers, communication modules, that enable the use of wireless networks for sending messages in real-time, are used, bridging concepts from both IoT and Edge Computing. (ii) The laboratory development and implementation of a decentralized architecture based on Embedded State Estimator Modules (ESEMs) are carried out. ESEMs manage information from smart meters at lower voltage networks, performing real-time state estimation in PDSs. Simulations performed on a real PDS with 208 buses (considering both medium and low voltage buses) have met the aims of this paper. The results show that by using ESEMs in a decentralized architecture, both the data transit through the communication network, as well as the computational requirements involved in monitoring PDSs in real-time, are reduced considerably without any loss of accuracy.

Wearable Edge AI Applications for Ecological Environments.

Ecological environments research helps to assess the impacts on forests and managing forests. The usage of novel software and hardware technologies enforces the solution of tasks related to this problem. In addition, the lack of connectivity for large data throughput raises the demand for edge-computing-based solutions towards this goal. Therefore, in this work, we evaluate the opportunity of using a Wearable edge AI concept in a forest environment. For this matter, we propose a new approach to the hardware/software co-design process. We also address the possibility of creating wearable edge AI, where the wireless personal and body area networks are platforms for building applications using edge AI. Finally, we evaluate a case study to test the possibility of performing an edge AI task in a wearable-based environment. Thus, in this work, we evaluate the system to achieve the desired task, the hardware resource and performance, and the network latency associated with each part of the process. Through this work, we validated both the design pattern review and case study. In the case study, the developed algorithms could classify diseased leaves with a circa 90% accuracy with the proposed technique in the field. This results can be reviewed in the laboratory with more modern models that reached up to 96% global accuracy. The system could also perform the desired tasks with a quality factor of 0.95, considering the usage of three devices. Finally, it detected a disease epicenter with an offset of circa 0.5 m in a 6 m × 6 m × 12 m space. These results enforce the usage of the proposed methods in the targeted environment and the proposed changes in the co-design pattern.

Low-Cost Joystick for Pediatric Respiratory Exercises.

The use of body signals for health care applications has become ubiquitous in the last decade. One utilization of such measurements is the monitoring of respiratory flow for physiotherapy assistance. This application is based on relative flow measures which can rely on inexpensive sensors. Based on that, we present a low-cost electronic device that detects blows and suctions with a pressure sensor and emulates a keyboard for interfacing with computers. This joystick allows children to control free internet games by associating blows and suctions with different intensities to keyboard actions. Also, the intensity can be calibrated according to the user's pulmonary capacities. This feature is adequate for gradual respiratory physiotherapy and can be customized for each patient. In order to verify the operation of the proposed device, practical tests were performed with three online free games, where the joystick functionality was assessed with different therapeutic configurations.

A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants.

Photovoltaic (PV) energy use has been increasing recently, mainly due to new policies all over the world to reduce the application of fossil fuels. PV system efficiency is highly dependent on environmental variables, besides being affected by several kinds of faults, which can lead to a severe energy loss throughout the operation of the system. In this sense, we present a Monitoring System (MS) to measure the electrical and environmental variables to produce instantaneous and historical data, allowing to estimate parameters that ar related to the plant efficiency. Additionally, using the same MS, we propose a recursive linear model to detect faults in the system, while using irradiance and temperature on the PV panel as input signals and power as output. The accuracy of the fault detection for a 5 kW power plant used in the test is 93.09%, considering 16 days and around 143 hours of faults in different conditions. Once a fault is detected by this model, a machine-learning-based method classifies each fault in the following cases: short-circuit, open-circuit, partial shadowing, and degradation. Using the same days and faults applied in the detection module, the accuracy of the classification stage is 95.44% for an Artificial Neural Network (ANN) model. By combining detection and classification, the overall accuracy is 92.64%. Such a result represents an original contribution of this work, since other related works do not present the integration of a fault detection and classification approach with an embedded PV plant monitoring system, allowing for the online identification and classification of different PV faults, besides real-time and historical monitoring of electrical and environmental parameters of the plant.

High-Level Modeling and Simulation Tool for Sensor Conditioning Circuit Based on Artificial Neural Networks.

For current microelectronic integrated systems, the design methodology involves different steps that end up in the full system simulation by means of electrical and physical models prior to its manufacture. However, the higher the circuit complexity, the more time is required to complete these simulations, jeopardizing the convergence of the numerical methods and, hence, meaning that the reliability of the results are not guaranteed. This paper shows the use of a high-level tool based on Matlab to simulate the operation of an artificial neural network implemented in a mixed analog-digital CMOS process, intended for sensor calibration purposes. The proposed standard tool enables modification of the neural model architecture to adapt its characteristics to those of the electronic system, resulting in accurate behavioral models that predict the complete microelectronic IC system behavior under different operation conditions before its physical implementation with a simple, time-efficient, and reliable solution.

Assisting Visually Impaired People in the Public Transport System through RF-Communication and Embedded Systems †.

For a significant number of people with visual impairments, public transport plays an important role in productivity, community participation, and independence, since it may be the only feasible mobility option to participate in their education, work, medical care, food, and to attend many other places in their community. To use the public bus system safely, effectively, and autonomously, these people need to collect information about their physical environment and visible information at stops and terminals, such as timetables, routes, etc. Unfortunately, most people who are blind or visually impaired experience difficulties in getting on the right bus or getting off at the right destination. These situations usually force them to depend on other people that assist them in activities close to their homes, or settle for simpler jobs, or simply stay at home. Therefore, our efforts should aim to develop a system where technology is used to empower people with visual disabilities, allowing them to navigate autonomously in the public transport system. This paper presents a system based on radio frequency (RF) communication proposed within the framework of the MOVIDIS (Mobility for Visually Disabled People) research project (funded by the National Secretariat of Science, Technology and Innovation-SENACYT, under Grants No. 109-2015-4-FID14-073 and No. 99-2018-4-FID17-031), which provides an alternative to assist people with visual disabilities with their mobility in the public transport system. The various modules of this system communicate with each other by means of radio frequency and allow users to interact with buses and their respective stops. The first experimental results show that RF communication represents a viable option to help people with visual disabilities in public transport services.

SISTEMA DE PULSIOXIMETRÍA Y CAPNOGRAFÍA PARA DISPOSITIVOS MÓVILES ANDROID / A PULSE-OXIMETRIC AND CAPNOGRAPHIC SYSTEM FOR ANDROID MOBILE DEVICES / SISTEMA DE OXIMETRIA DE PULSO E CAPNOGRAFIA PARA DISPOSITIVOS MÓVEIS ANDROID

El monitoreo constante del nivel de saturación de oxígeno y la producción de CO2 es de vital importancia para la supervisión del estado respiratorio del paciente. Este artículo presenta el diseño de un sistema de oximetría de pulso y capnografía que tiene como unidad de procesamiento un chip programable de señales mixtas denominado PSoC (Programable-System-On-Chip), el cual incorpora bloques análogos y digitales configurables, permitiendo que la adecuación de las señales suministradas por los sensores y el procesamiento digital de señales se lleve a cabo en el mismo chip. Se realizó una aplicación en Android para la visualización y registro de las señales biomédicas en una base de datos local, compatible con dispositivos móviles con conectividad wifi. El sistema fue verificado usando un simulador de SpO2 (Saturación parcial de oxígeno), que permitió la calibración de frecuencias cardiacas desde 55 BPM (Beats per Minute) a 145 BPM, así como la curva R con valores de 75% a 100% de SpO2. Se encontró que el error de medición de la frecuencia cardiaca es 1,81%, y 1.33% para la SpO2.

Constant monitoring of oxygen saturation level and CO2 production is vital for monitoring the patient's respiratory status. This paper presents the design of a pulse-oximetric and capnographic system, which core consists of a mixed signal programmable chip, PSoC (Programmable-System-On-Chip), which incorporates a whole analog and digital configurable block system, in order to adequate and process the signals from the sensors all in a single chip. An Android application was also developed, which can display biomedical signals in mobile devices with wireless connectivity, as well as to store information from these signals in a local user database. The microsystem was verified using a SpO2 (oxygen partial saturation) simulator, and heart rates of 55 BPM to 145 BPM were calibrated, as well as the R curve with values of 75% to 100% SpO2. The heart rate measurement error found is 1,81% and 1,33% for the SpO2.

O monitoramento constante do nível de saturação de oxigênio e produção de CO2 é fundamental para monitorar o estado respiratório do paciente. Este artigo apresenta o projeto de um sistema de oximetria de pulso e capnografia cuja unidade de processamento um chips de sinal misto programável chamado PSoC (Programmable-System-On-Chip), o qual incorpora blocos analógicos e digitais configuráveis, permitindo a adaptação dos sinais fornecidos pelos sensores e o processamento digital de sinais será executada no mesmo chip. Foi realizada una aplicação Android para visualização e gravação de sinais biomédicos em um banco de dados local, compatível com dispositivos móveis com conectividade sem fio. O sistema foi testado usando um simulador de SpO2 (saturação de oxigênio parcial), permitindo a calibração da freqüência cardíaca de 55 BPM (batidas por minuto) a 145 BPM, assim como a curva R com valores de 75% a 100% SpO2 . Verificou-se que o erro de medição do ritmo cardíaco é 1,81% e 1,33% para o SpO2.