Recent Submissions

  • Model for Implementing a IoMT Architecture with ISO/IEC 27001 Security Controls for Remote Patient Monitoring

    Alegria, Brandon; Wong, Lenis; Bedriniana, Diego (IEEE Computer Society, 2022-01-01)
    Due to the recent pandemic, the healthcare sector has been forced to incorporate new technologies into its systems, such as IoT and Fog Computing. However, being new technologies, they are prone to security breaches. From this context, it is identified that medical systems do not have a sufficient level of security, due to the use of new technologies such as IoT and the lack of controls to protect these new technologies. Therefore, a model for implementing an Internet of Medical Things (IoMT) Architecture with ISO/IEC 27001 security controls for remote patient monitoring is proposed. This model has 4 stages: Stage 1 selects an information security standard for the healthcare sector. Stage 2 selects the information security controls of the selected standard. Stage 3 selects and evaluates an IoMT architecture applicable to the healthcare sector. And Stage 4 designs the information security controls for each layer of the IoMT architecture. The IoMT architecture and information security controls are simulated and experimented with physicians (the productivity of the system) and with information security expert (the quality of the implemented controls). The results of the first experiment show that 'effectiveness', 'productivity', and 'satisfaction' regarding the use of the IoMT architecture have an average rating of 4.05 (high level). The results of the second experiment show that 'Information Security', 'Awareness' and 'Security Incident Management' regarding the quality of the security controls implemented have an average rating of 3.65 (high level).
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  • Design and Validation of an Instrument to Measure Digital Skills in University Students of the First Cycles of Health Careers

    Ramirez-Rodriguez, Laritza Tatiana; Sanchez-Pimentel, Janett Isabel; Osorio-Galvan, Roxana Clara; Perez-Ortiz, Joaquin Omar (Institute of Electrical and Electronics Engineers Inc., 2022-01-01)
    The objective of this research was to describe the process of validation and reliability of an instrument to measure the information selection, search and processing capabilities of university students in the first cycles of health careers. This need arose because in our environment, there is a gap in the validation and quality of instruments for information management in digital contexts. It is descriptive-quantitative research of instrumental design with psychometric properties. For this, the following steps were followed: 1) review of the literature on digital competencies; 2) construction of the instrument based on the Likert scale; and 3) testing of the content validity and reliability of the instrument by 15 experts. From this evaluation, it was obtained that the content validity index 0,80, based on Lawshe's method; and, by means of Cronbach's Alpha coefficient, the VAR POB 0,997 was determined. Therefore, it is concluded that the instrument allows measuring students' capabilities in information management in the Peruvian university context.
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  • Technological Solution For Children With ADHD By Using Augmented Reality In Serious Games

    Mendoza-Marin, Aldo Anderson; Duenas-Guevara, Miguel Alejandro; Burga, Daniel (Institute of Electrical and Electronics Engineers Inc., 2022-01-01)
    Attention Deficit Hyperactivity Disorder (ADHD) is one of the neurodevelopmental problems with the highest rate of attention demands in children worldwide. There are several manual methods for the treatment of ADHD, one of these methods involves the analysis of the family context through differential diagnosis in which physical cognitive behavioral games are used for the initial diagnosis. It was found that the tools of the traditional method of the process of differential diagnosis of ADHD in children do not allow to meet the demand for care requests. In this paper, we present an alternate digital tool for Android mobile devices using Augmented Reality (AR) with the AR Foundation Software Development Kit (SDK) in the Unity3D game engine for differential diagnosis, in which it helps the psychologist to obtain an initial diagnosis of family problems that could be aggravating the patient's situation, by analyzing the family context, as well as, a digital alternative to the cognitive behavioral game of memory tiles. As a result of the research, it was found that the solution was more effective and efficient than the traditional method in the differential diagnosis.
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  • Self-regulation in academic work of distance education students

    Pinto-Guillergua, Vanessa; Montoya-Cantoral, Elisa; Gomez-Carbonel, Piero; Gallardo-Echenique, Eliana (Institute of Electrical and Electronics Engineers Inc., 2022-01-01)
    This study describes the perception of self-regulation for the planning of academic work in distance education students. Its approach is qualitative with a phenomenological interpretive design because it analyzes the phenomenon of student self-regulation to start and finish academic work in virtual environments. 8 students from a higher education institute were interviewed. It was evidenced that students plan their activities according to the level of difficulty, which increases their self-efficacy and responsibility in decision-making in this modality. In addition, they have identified the development of self-regulation of their learning and valued the teaching presence in their learning environment.
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  • Artificial Intelligence Techniques for Biosignal Pattern Recognition and Classification in Upper-Limb Prostheses: A Review

    Terrazas-Rodas, Dana; Carrion-Perez, Joanna (Institute of Electrical and Electronics Engineers Inc., 2022-01-01)
    Currently, disability is a condition in which people are considered to have long-term physical, mental, intellectual, or sensory impairments due to different circumstances or situations, which may be due to an accident, illness, among others. According to the United Nations (UN), approximately 10% of people (650 million approximately) are registered with some type of disability, which is increasing due to population growth worldwide, medical advances and the aging process. Upper limb prostheses are devices that replace parts of the body of a person or user with upper limb disability or amputation, such as the arm, hand, among others. In this review, various Artificial Intelligence (AI) techniques were examined for their applications such as pattern recognition and classification of biosignals in a total of 72 upper limb prostheses in different categories such as the commercial name or main author's name of the device, the characteristics of the patient-user who will use it, the level of amputation, the mechanism which is the body part that replaces the bionic hand, the control biosignals that activate the operation of the prosthesis, the Artificial Intelligence (AI) methods that have been employed, the applications of AI techniques and the Technology Readiness Level (TRL), which is the level of development of the upper limb prosthesis between the lowest level (1) and the highest level (9).
  • A soft grip design using Finite Element Analysis for mango handling on the KUKA KR3 robot

    Auris, Luis; Gallegos, Marco; Vinces, Leonardo; Ronceros, Julio (Institute of Electrical and Electronics Engineers Inc., 2022-01-01)
    This article proposes the design of a gripper that allows a soft grip through finite element analysis (FEA) for the manipulation of the Kent mango implemented in the KUKA KR3 AGILUS robotic arm. According to INEI, there was a 59.6% growth in mango production between 2018 and 2019 [1]. In this way, it was proposed to increase the efficiency in handling the mango during the classification processes in large quantities. Therefore, as the main objective, it is considered that, through an automated system during the post-harvest mango classification process, it is possible to raise production efficiency levels through continuous classification. Yonua by caliber and a pneumatic system adaptable to the geometric body of each mango. The ergonomics of this system is what makes it attractive, since the gripper to be used was designed and developed to perfectly fit the geometry of the Kent mango. For gripper actuation, that is, for each soft pneumatic actuator, it is necessary to evaluate the levels of necessary pressure that enter its channels. This in order to be able to support the weight of the mango when it is lifted from its conveyor belt. This evaluation will be influenced by simulation in the Abaqus-CAE software in a non-linearity study.
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  • The Use of Invasive and Non-Invasive Electrodes in Novel Technology of Upper Limb Prostheses: A Current Review

    Terrazas-Rodas, Dana; Carrion-Perez, Joanna (Institute of Electrical and Electronics Engineers Inc., 2023-01-01)
    n amputation is the mutilation of a body part, which can be a limb or part of a limb. For the World Health Organization (WHO), amputees represent approximately 0.25% and 1.25% of the world's population. Electrodes are used to capture biological signals or biosignals from specific parts of a user's body for optimal development of devices such as upper limb prostheses. In this review, various electrodes used in upper limb prostheses were identified in categories that are indispensable for their development, such as commercial name or electrode references, the type of electrode (invasive or noninvasive), the number of electrodes used, the control biosignals that are captured by the electrodes and that activate the operation of the prostheses, the location of the electrodes, the advantages and disadvantages, and the level of amputation of the limb. According to the research, the two advantages most mentioned by the authors are that the electrodes are cheap and accurate, while the disadvantages are that they are noisy and imprecise. These depend on the application or objective that the author wanted the electrode to have for the design of bionic hand for people who have suffered an upper limb amputation.
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  • Robot Arm Control System for Assisted Feeding of People with Disabilities in their Upper Limbs

    Arnáez, Daniella; Manco, Fiorela; Oliden, José; Kemper, Guillermo (International Institute of Informatics and Cybernetics, 2022-01-01)
    This work proposes a robot arm control system for assisted feeding of people with reduced functionality in their upper limbs. It aims at improving their quality of life by helping users recover their independence when feeding, aided by the proposed system. Previous research presents solutions that often lack functionality to meet the user’s needs, such as a lack of emergency functions or the use of passive feeding techniques, due to the absence of adequate human-robot interaction. The proposed solution involves the design of an interface adapter between the robot arm and the spoon, for the correct transport and positioning of the food. Moreover, a PD-type electronic controller is implemented for the robot arm; it includes gravity compensation and trajectories defined from the detection of the user's position. Additionally, the system has two safety features: an emergency button and a proximity warning that triggers when undesired objects are too close to the robot arm. The proposed system was validated through position tests and interaction with people using rice and oatmeal. When carrying out the tests with rice, 80% success was obtained, while in the case of oatmeal, 98.9% success was achieved.
  • MPEG-1 psychoacoustic model emulation using multiscale convolutional neural networks

    Kemper, Guillermo; Sanchez, Alonso; Serpa, Sergio (Springer, 2023-01-01)
    The Moving Picture Experts Group - 1 (MPEG-1) perceptual audio compression scheme is a successful family of audio codecs described in standard ISO/IEC 11172–3. Currently, there is no general framework to emulate nor MPEG-1 neither any other psychoacoustic model, which is a core piece of many perceptual codecs. This work presents a successful implementation of a convolutional neural network which emulates psychoacoustic model 1 from the MPEG-1 standard, termed “MCNN-PM” (Multiscale Convolutional Neural Network – Psychoacoustic Model). It is then implemented as part of the MPEG-1, Layer I codec. Using the objective difference grade (ODG) to evaluate audio quality, the MCNN-PM MPEG-1, Layer I codec outperforms the original MPEG-1, Layer I codec by up to 17% at 96 kbps, 14% at 128 kbps and performs almost equally at 192 kbps. This work shows that convolutional neural networks are a viable alternative to standard psychoacoustic models and can be used as part of perceptual audio codecs successfully.
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  • An algorithm for obtaining the frequency and the times of respiratory phases from nasal and oral acoustic signals

    Kemper, Guillermo; Oshita, Angel; Parra, Ricardo; Herrera, Carlos (Institute of Advanced Engineering and Science, 2023-02-01)
    This work proposes a computational algorithm which extracts the frequency, timings and signal segments corresponding to respiratory phases, through buccal and nasal acoustic signal processing. The proposal offers a computational solution for medical applications which require on-site or remote patient monitoring and evaluation of pulmonary pathologies, such as coronavirus disease 2019 (COVID-19). The state of the art presents a few respiratory evaluation proposals through buccal and nasal acoustic signals. Most proposals focus on respiratory signals acquired by a medical professional, using stethoscopes and electrodes located on the thorax. In this case the signal acquisition process is carried out through the use of a low cost and easy to use mask, which is equipped with strategically positioned and connected electret microphones, to maximize the proposed algorithm's performance. The algorithm employs signal processing techniques such as signal envelope detection, decimation, fast Fourier transform (FFT) and detection of peaks and time intervals via estimation of local maxima and minima in a signal's envelope. For the validation process a database of 32 signals of different respiratory modes and frequencies was used. Results show a maximum average error of 2.23% for breathing rate, 2.81% for expiration time and 3.47% for inspiration time.
    Acceso abierto
  • An Electronic Equipment for Automatic Identification of Forest Seed Species

    Tupac, Miguel; Armas, Reiner; Kemper, Guillermo (Springer Science and Business Media Deutschland GmbH, 2023-01-01)
    This work proposes an electronic equipment which identifies forest seeds for academic and research purposes. Existing integral solutions are prohibitively costly for silviculture laboratories used in forestry teaching. Thus, they must identify the seed by visual inspection, causing visual fatigue and results with low reliability. The state of the art proposes solutions using support vector machines, achieving a 98.82% accuracy for sunflower seeds. Other solutions extract morphological attributes of mussel seeds to identify up to 5 species with an accuracy of 95%. Most solutions only identify a single seed type with similar sizes. In this context, an electronic equipment is developed. It consists of an image acquisition enclosure, an electromechanical device to move a camera so different sizes of seeds can be imaged at different distances, and a single-board computer to control the image processing and artificial intelligence (convolutional neural network) algorithms. The equipment achieves an accuracy of 95%, which is satisfactory for potential users and silviculture specialists.
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  • An Electronic Equipment for Measuring Color Difference Between Tissues Based on Digital Image Processing and Neural Networks

    Calderón, José; Lipa, Benghy; Kemper, Guillermo (Springer Science and Business Media Deutschland GmbH, 2023-01-01)
    This work proposes an electronic equipment aimed at measuring the color difference between fabrics for a homogeneous selection of these. The proposed method analyzes tissues with different illuminants so that, by taking measurements with a camera, the impact of effects such as metamerism can be reduced, resulting in a more accurate method that represents reality. For this, an enclosure was designed and built to allow constant lighting conditions and spotlights with standard lighting were installed. On the software side, the camera was manually configured to ensure persistent measurements over time and the colors obtained from the RGB color space were converted to CIELAB. For the calculation of the color difference, the Delta E (CMC) color difference equation was used because this is the standard used in the textile industry. Finally, a neural network was trained to estimate the color difference between the fabrics. To validate the results, the measurements obtained with the proposed equipment were compared with those obtained using a colorimeter to measure the colors of the fabrics and calculate whether their color difference is significant. The developed equipment achieved an accuracy of 95% in the correct identification of fabrics that are homogenous in color.
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  • An Electronic Equipment for Monitoring, Detection and Warning of Pitch Motion of Vehicle Drivers

    Alzamora, Luis; Espino, Josue; Acosta, Rubén; Márquez, Manuel; Kemper, Guillermo (Springer Science and Business Media Deutschland GmbH, 2023-01-01)
    Long and monotonous journeys during the transport of goods can generate tiredness and/or fatigue in the driver, generating a potential vehicle accident where the driver can enter a state of micro-sleep, losing control of the vehicle for a period of time. Within the state of the art, it is observed that most of the solutions have as their fundamental axis the analysis of the ocular muscle, being these vulnerable to the variation of light intensity and facial accessories that the driver can use. On the other hand, other studies analyze EEG signals being intrusive and disturbing the driving skills of the driver. This work presents a driver fatigue monitoring system based on the angular movement of the head, the main characteristic prior to the state of micro-sleep, located in a safety helmet. To do this, an analysis of the angular movement of the driver’s head is carried out, thus avoiding the use of cameras, potential lighting problems and intrusive driving disturbances. The device will detect the nodding symptom and will issue an auditory alert with a message via Telegram to a third party to alert the presence of driver fatigue with an error rate of less than 22%, having as auditory alert response time a period of 500 ms equivalent to the distance of 8 m if the vehicle moves at 60 km/h. The validation was carried out by comparing the angles between the device located in the helmet and a reference accelerometer.
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  • A low-rate encoder for image transmission using LoRa communication modules

    Guerra, Kevin; Casavilca, Jhon; Huamán, Samuel; López, Lidia; Sanchez, Alonso; Kemper, Guillermo (Springer Science and Business Media B.V., 2023-02-01)
    The present work proposes an encoder for image transmission via LoRa communication modules. These enable long-range, low-power transmission schemes and are ideal for monitoring in places with no mobile network connectivity. Nonetheless, this technology has a low transmission bitrate, which limits its use to high bandwidth applications. The state-of-the-art has numerous image encoders, but few achieve an adequate balance between image quality, compression, sequential decoding, and computational complexity. The proposed encoder uses the YCoCg color model and chromatic subsampling followed by wavelet subband decomposition, which extracts relevant subbands in the image to then reconstruct it sequentially. Each subband is quantized independently and then enters an adaptive entropic encoder. This encoder is compared to the JPEG2000 encoder using the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) quality metrics. Results show that the proposal obtains a reconstructed image quality close to that of JPEG2000 with a higher compression rate. Moreover, it improves the transmission time of images through a LoRa link by 99.09%.
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  • A Conversion Algorithm for ECG signals on a 2D array based on Digital Signal Processing

    Cabanillas, Julio Cesar; Kemper, Guillermo; Del Carpio, Christian (Institute of Electrical and Electronics Engineers Inc., 2022-01-01)
    This work proposes a computational algorithm to convert digital files containing electrocardiogram (ECG) information into 1D signals. Many medical databases have in storage files containing ECG information that is not easy to process for computational algorithms. Digitization by the proposed method makes it possible to modernize the databases of many health centers in order to perform post-processing of the signals obtained. This method is based on applying digital signal processing techniques to images obtained from a PDF file produced by an electrocardiograph. The proposed algorithm takes into consideration the thickness of the printed signal in the PDF image so that it does not introduce distortion in the final 1D signal. Due to the distribution of the ECG signals on the PDF files the algorithm identifies and segments the signals on 2 dimensions. The results show that the proposed method can correctly reproduce the information of the ECG waves captured in the PDF file regardless of the elements outside the ECG signal such as the background grid or the different information indicators, whether they are labels or references of the ECG signals. The algorithm has an accuracy of 95% based on the statistical analysis performed for all samples.
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  • An SVM-based Intelligible Signal Presence Detection Algorithm for FM Signals Demodulated via SDR

    Linares, Arturo; Mejia, Bernabe; Sanchez, Alonso; Kemper, Guillermo (Institute of Electrical and Electronics Engineers Inc., 2022-01-01)
    This work proposes a computational algorithm which monitors voice/audio signals demodulated from a FM receptor and detects whether they are intelligible or not. Data analytics applications which require the continuous storage of radio broadcasted audio signals into a database can benefit from this algorithm. In many instances, the broadcasted signals arrive at the receptor with heavy distortion and noise content, limiting the data analysis due to poor data quality. Moreover, radio spectrum supervisory agencies can also take advantage of this work, since broadcasted signals can be efficiently and continuously monitored to detect whether a broadcaster has stopped transmitting for an extended period. First, the algorithm processes the demodulated signals block by block, extracting its MFCC coefficients, spectral centroid, the arithmetic and geometric means of the frequency magnitude spectrum and the zero-crossing rate in the time domain. Then, these parameters enter a classification algorithm based on three successive support vector machines (SVM), which output one of four possible classes for each block: intelligible clean signal, intelligible noisy signal, unintelligible noisy signal, and noise/silence signal. The algorithm has a 99.85% accuracy for intelligible clean signal versus unintelligible noisy/noise/silence signals; 97.34% accuracy for intelligible noisy signal versus noise/silence signals; and 96.36% accuracy for intelligible voice versus noise/silence.
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  • Development of a hybrid system for automatic identification of brushed direct current motors

    Hamann, Franz; Mesones, Gustavo (Institute of Electrical and Electronics Engineers Inc., 2020-09-01)
    This work proposes a low-cost hybrid hardware and software system that, through a set of methods and nested while loop fitting algorithms, allows to automatically identify the electrical and mechanical parameters of a brushed direct current motor. The aim is to facilitate a tool that contributes to the development of motion control projects in which this type of actuator is used, automating and speeding up the identification process of the motor system aiming to reach 98% accuracy, in order to guarantee a good electrical and mechanical parameter estimates for the brushed direct current motor. To achieve the objective, a platform was developed consisting of a main interface programmed in Matlab and a data acquisition hardware based on a single-phase incremental optical encoder, an H-bridge, an optocoupler circuit, and a C language-programmed DSPIC30F2010. Both parts of the platform are interconnected through the authors' own serial communication protocol.
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  • Algorithm for Detection of Raising Eyebrows and Jaw Clenching Artifacts in EEG Signals Using Neurosky Mindwave Headset

    Vélez, Luis; Kemper, Guillermo (2021-01-01)
    The present work proposes an algorithm to detect and identify the artifact signals produced by the concrete gestural actions of jaw clench and eyebrows raising in the electroencephalography (EEG) signal. Artifacts are signals that manifest in the EEG signal but do not come from the brain but from other sources such as flickering, electrical noise, muscle movements, breathing, and heartbeat. The proposed algorithm makes use of concepts and knowledge in the field of signal processing, such as signal energy, zero crossings, and block processing, to correctly classify the aforementioned artifact signals. The algorithm showed a 90% detection accuracy when evaluated in independent ten-second registers in which the gestural events of interest were induced, then the samples were processed, and the detection was performed. The detection and identification of these devices can be used as commands in a brain–computer interface (BCI) of various applications, such as games, control systems of some type of hardware of special benefit for disabled people, such as a chair wheel, a robot or mechanical arm, a computer pointer control interface, an Internet of things (IoT) control or some communication system.
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  • Correspondence Between TOVA Test Results and Characteristics of EEG Signals Acquired Through the Muse Sensor in Positions AF7–AF8

    Castillo, Ober; Sotomayor, Simy; Kemper, Guillermo; Clement, Vincent (2021-01-01)
    This paper seeks to study the correspondence between the results of the test of variable of attention (TOVA) and the signals acquired by the Muse electroencephalogram (EEG) in the positions AF7 and AF8 of the cerebral cortex. There are a variety of research papers that estimates an index of attention in which the different characteristics in discrete signals of the brain activity were used. However, many of these results were obtained without contrasting them with standardized tests. Due to this fact, in the present work, the results will be compared with the score of the TOVA, which aims to identify an attention disorder in a person. The indicators obtained from the test are the response time variability, the average response time, and the d′ prime score. During the test, the characteristics of the EEG signals in the alpha, beta, theta, and gamma subbands such as the energy, average power, and standard deviation were extracted. For this purpose, the acquired signals are filtered to reduce the effect of the movement of the muscles near the cerebral cortex and then went through a subband decomposition process by applying transformed wavelet packets. The results show a well-marked correspondence between the parameters of the EEG signal of the indicated subbands and the visual attention indicators provided by TOVA. This correspondence was measured through Pearson’s correlation coefficient which had an average result of 0.8.
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  • Algorithm Oriented to the Detection of the Level of Blood Filling in Venipuncture Tubes Based on Digital Image Processing

    Castillo, Jorge; Apfata, Nelson; Kemper, Guillermo (2021-01-01)
    This article proposes an algorithm oriented to the detection of the level of blood filling in patients, with detection capacity in millimeters. The objective of the software is to detect the amount of blood stored into the venipuncture tube and avoid coagulation problems due to excess fluid. It also aims to avoid blood levels below that required, depending on the type of analysis to be performed. The algorithm acquires images from a camera positioned in a rectangular structure located within an enclosure, which has its own internal lighting to ensure adequate segmentation of the pixels of the region of interest. The algorithm consists of an image improvement stage based on gamma correction, followed by a segmentation stage of the area of ​​pixels of interest, which is based on thresholding by HSI model, in addition to filtering to accentuate the contrast between the level of filling and staining, and as a penultimate stage, the location of the filling level due to changes in the vertical tonality of the image. Finally, the level of blood contained in the tube is obtained from the detection of the number of pixels that make up the vertical dimension of the tube filling. This number of pixels is then converted to physical dimensions expressed in millimeters. The validation results show an average percentage error of 0.96% by the proposed algorithm.
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