Electromiographic Signal Processing Using Embedded Artificial Intelligence: An Adaptive Filtering Approach

In recent times, Artificial Intelligence (AI) has become ubiquitous in technological fields, mainly due to its ability to perform computations in distributed systems or the cloud. Nevertheless, for some applications -as the case of EMG signal processing- it may be highly advisable or even mandatory an on-the-edge processing, i.e., an embedded processing methodology. On the other hand, sEMG signals have been traditionally processed using LTI techniques for simplicity in computing. However, making this strong assumption leads to information loss and spurious results. Considering the current advances in silicon technology and increasing computer power, it is possible to process these biosignals with AI-based techniques correctly. This paper presents an embedded-processing-based adaptive filtering system (here termed edge AI) being an outstanding alternative in contrast to a sensor-computer- actuator system and a classical digital signal processor (DSP) device. Specifically, a PYNQ-Z1 embedded system is used. For experimental purposes, three methodologies on similar processing scenarios are compared. The results show that the edge AI methodology is superior to benchmark approaches by reducing the processing time compared to classical DSPs and general standards while maintaining the signal integrity and processing it, considering that the EMG system is not LTI. Likewise, due to the nature of the proposed architecture, handling information exhibits no leakages. Findings suggest that edge computing is suitable for EMG signal processing when an on-device analysis is required

Environment Monitoring of Rose Crops Greenhouse Based on Autonomous Vehicles with a WSN and Data Analysis

This work presents a monitoring system for the environmental conditions of rose flower-cultivation in greenhouses. Its main objective is to improve the quality of the crops while regulating the production time. To this end, a system consisting of autonomous quadruped vehicles connected with a wireless sensor network (WSN) is developed, which supports the decision-making on type of action to be carried out in a greenhouse to maintain the appropriate environmental conditions for rose cultivation. A data analysis process was carried out, aimed at designing an in-situ intelligent system able to make proper decisions regarding the cultivation process. This process involves stages for balancing data, prototype selection, and supervised classification. The proposed system produces a significant reduction of data in the training set obtained by the WSN while reaching a high classification performance in real conditions—amounting to 90% and 97.5%, respectively. As a remarkable outcome, it is also provided an approach to ensure correct planning and selection of routes for the autonomous vehicle through the global positioning system

Intelligent WSN System for Water Quality Analysis Using Machine Learning Algorithms: A Case Study (Tahuando River from Ecuador)

This work presents a wireless sensor network (WSN) system able to determine the water quality of rivers. Particularly, we consider the Tahuando River from Ibarra, Ecuador, as a case study. The main goal of this research is to determine the river’s status throughout its route, by generating data reports into an interactive user interface. To this end, we use an array of sensors collecting several measures such as: turbidity, temperature, water quality, pH, and temperature. Subsequently, from the information collected on an Internet-of-Things (IoT) server, we develop a data analysis scheme with both data representation and supervised classification. As an important result, our system outputs a map that shows the contamination levels of the river at different regions. Furthermore, in terms of data analysis performance, the proposed system reduces the data matrix by 97% from its original size, while it reaches a classification performance over 90%. Furthermore, as an additional remarkable result, we here introduce the so-called quantitative metric of balance (QMB), which measures the balance or ratio between performance and power consumption

Statistical Analysis of the Impact of COVID-19 on PM2.5 Concentrations in Downtown Quito during the Lockdowns in 2020

In this paper, a comparative analysis between the PM2.5 concentration in downtown Quito, Ecuador, during the COVID-19 pandemic in 2020 and the previous five years (from 2015 to 2019) was carried out. Here, in order to fill in the missing data and achieve homogeneity, eight datasets were constructed, and 35 different estimates were used together with six interpolation methods to put in the estimated value of the missing data. Additionally, the quality of the estimations was verified by using the sum of squared residuals and the following correlation coefficients: Pearson’s r, Kendall’s τ, and Spearman’s ρ. Next, feature vectors were constructed from the data under study using the wavelet transform, and the differences between feature vectors were studied by using principal component analysis and multidimensional scaling. Finally, a robust method to impute missing data in time series and characterize objects is presented. This method was used to support the hypothesis that there were significant differences between the PM2.5 concentration in downtown Quito in 2020 and 2015–2019

Smart and Portable Air-Quality Monitoring IoT Low-Cost Devices in Ibarra City, Ecuador

Nowadays, increasing air-pollution levels are a public health concern that affects all living beings, with the most polluting gases being present in urban environments. For this reason, this research presents portable Internet of Things (IoT) environmental monitoring devices that can be installed in vehicles and that send message queuing telemetry transport (MQTT) messages to a server, with a time series database allocated in edge computing. The visualization stage is performed in cloud computing to determine the city air-pollution concentration using three different labels: low, normal, and high. To determine the environmental conditions in Ibarra, Ecuador, a data analysis scheme is used with outlier detection and supervised classification stages. In terms of relevant results, the performance percentage of the IoT nodes used to infer air quality was greater than 90%. In addition, the memory consumption was 14 Kbytes in a flash and 3 Kbytes in a RAM, reducing the power consumption and bandwidth needed in traditional air-pollution measuring stations