Short-Term Thermal Effect of Continuous Ultrasound from 3 MHz to 1 and 0.5 W/cm2 Applied to Gastrocnemius Muscle.

Continuous ultrasound is recognized for its thermal effect and use in the tissue repair process. However, there is controversy about its dosage and efficacy. This study used infrared thermography, a non-invasive technique, to measure the short-term thermal effect of 3 MHz continuous ultrasound vs. a placebo, referencing the intensity applied. It was a single-blind, randomized clinical trial of 60 healthy volunteers (19-24 years old) divided into three equal groups. Group 1:1 W/cm2 for 5 min; Group 2: 0.5 W/cm2 for 10 min; and Group 3: the placebo for 5 min. The temperature was recorded through five thermographic images per patient: pre- and post-application, 5, 10, and 15 min later. After statistical analysis, a more significant decrease in temperature (p<0.05 ) was observed in the placebo group compared with the remaining groups after the application of continuous ultrasound. Group 1 was the one that generated the highest significant thermal effect (p<0.001), with an increase of 3.05 °C at 15 min, compared with the other two groups. It is concluded that to generate a thermal effect in the muscle, intensities of ≥1 W/cm2 are required, since the dosage maintained a temperature increase for more than 5 min.

Knee skin temperature response of patients with bilateral patellofemoral syndrome before and after heat and cold stress.

Patellofemoral Pain Syndrome is characterized by the presence of pain in the front area of the knee, which occurs when performing common activities such as climbing stairs, and bending the knees, among others. The objective of this research was to evaluate the detection capability of infrared thermography in patients with Patellofemoral Pain Syndrome, in the baseline state, as well as after the application of thermal stress. The investigation was conducted in 48 patients, who were subdivided into four groups (n = 12). Two subgroups were healthy patients and two with Patellofemoral Pain Syndrome. For the diagnosis of the syndrome, a manual evaluation was performed using the Zohlen test and Q angle measurement. Subsequently, cold stress was applied for 10 min to a healthy subgroup and an experimental subgroup. The remaining two subgroups were subjected to heat stress for 15 min. Thermographic images of the lower extremities were acquired at seven time points, at baseline, immediately after application of thermal stress and then every 3 min until 15 min were completed. It was observed that patients presented Patellofemoral Pain Syndrome bilaterally. After statistical analysis, it was found that there were no significant differences in baseline temperature between the groups. However, for heat stress, a higher temperature was observed in the group with Patellofemoral Pain Syndrome (p < 0.05) in the recovery period, and in the case of cold stress, only a lower temperature in the left knee immediately after the application. In conclusion, it is not possible to detect patellofemoral syndrome bilaterally in the baseline state by thermography and neither is it evident in cold stress. However, after heat stress, thermal recovery is lower for the PFPS group, so it would be susceptible to detection.

Automatic Segmentation of Facial Regions of Interest and Stress Detection Using Machine Learning.

Stress is a factor that affects many people today and is responsible for many of the causes of poor quality of life. For this reason, it is necessary to be able to determine whether a person is stressed or not. Therefore, it is necessary to develop tools that are non-invasive, innocuous, and easy to use. This paper describes a methodology for classifying stress in humans by automatically detecting facial regions of interest in thermal images using machine learning during a short Trier Social Stress Test. Five regions of interest, namely the nose, right cheek, left cheek, forehead, and chin, are automatically detected. The temperature of each of these regions is then extracted and used as input to a classifier, specifically a Support Vector Machine, which outputs three states: baseline, stressed, and relaxed. The proposal was developed and tested on thermal images of 25 participants who were subjected to a stress-inducing protocol followed by relaxation techniques. After testing the developed methodology, an accuracy of 95.4% and an error rate of 4.5% were obtained. The methodology proposed in this study allows the automatic classification of a person's stress state based on a thermal image of the face. This represents an innovative tool applicable to specialists. Furthermore, due to its robustness, it is also suitable for online applications.

Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms.

Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications.

Mini-AUV Hydrodynamic Parameters Identification via CFD Simulations and Their Application on Control Performance Evaluation.

This manuscript presents a fully detailed methodology in order to identify the hydrodynamic parameters of a mini autonomous underwater vehicle (mini-AUV) and evaluate its performance using different controllers. The methodology consists of close-to-reality simulation using a Computed Fluid Dynamics (CFD) module of the ANSYS™ Workbench software, the processing of the data, obtained by simulation, with a set of Savistky-Golay filters; and, the application of the Least Square Method in order to estimate the hydrodynamic parameters of the mini-AUV. Finally, these parameters are considered to design the three different controllers that are based on the robot manipulators theory. Numerical simulations are carried out to evaluate the performance of the controllers.

Natural Frequencies Identification by FEM Applied to a 2-DOF Planar Robot and Its Validation Using MUSIC Algorithm.

In this paper, the natural frequencies (NFs) identification by finite element method (FEM) is applied to a two degrees-of-freedom (2-DOF) planar robot, and its validation through a novel experimental methodology, the Multiple Signal Classification (MUSIC) algorithm, is presented. The experimental platforms are two different 2-DOF planar robots with different materials for the links and different types of actuators. The FEM is carried out using ANSYS™ software for the experiments, with vibration signal analysis by MUSIC algorithm. The advantages of the MUSIC algorithm against the commonly used fast Fourier transform (FFT) method are also presented for a synthetic signal contaminated by three different noise levels. The analytical and experimental results show that the proposed methodology identifies the NFs of a high-resolution robot even when they are very closed and when the signal is embedded in high-level noise. Furthermore, the results show that the proposed methodology can obtain a high-frequency resolution with a short sample data set. Identifying the NFs of robots is useful for avoiding such frequencies in the path planning and in the selection of controller gains that establish the bandwidth.

Smart Sensor Based on Biofeedback to Measure Child Relaxation in Out-of-Home Care.

Children from out-of-home care are a vulnerable population that faces high stress and anxiety levels due to stressful experiences, such as being abused, being raped, and violence. This problem could have negative effects on their bio-psycho-social well-being if they are not provided with comprehensive psychological treatment. Numerous methods have been developed to help them relax, but there are no current approaches for assessing the relaxation level they reach. Based on this, a novel smart sensor that can evaluate the level of relaxation a child experiences is developed in this paper. It evaluates changes in thermal biomarkers (forehead, right and left cheek, chin, and maxillary) and heart rate (HR). Then, through a k-nearest neighbors (K-NN) intelligent classifier, four possible levels of relaxation can be obtained: no-relax, low-relax, relax, and very-relax. Additionally, an application (called i-CARE) for anxiety management, which is based on biofeedback diaphragmatic breathing, guided imagery, and video games, is evaluated. After testing the developed smart sensor, an 89.7% accuracy is obtained. The smart sensor used provides a reliable measurement of relaxation levels and the i-CARE application is effective for anxiety management, both of which are focused on children exposed to out-of-home care conditions.

Physiological stressor impact on peripheral facial temperature, Il-6 and mean arterial pressure, in young people.

Nowadays, stress is part of everyday life, whose long-term effects can trigger health risks. Among the main alterations that occur in the human body we can find the variation of inflammatory activity, blood pressure, and facial peripheral temperature. The objective of this work is to show the facial thermal behavior for men and women, as well as the differences in vascular and inflammatory responses induced by the effect of acute social stress. The Trier Social Stress Test was applied to 15 women and 15 men, free of disease, with an average age of 23.8 years and a standard deviation of 5.52. After capturing the baseline state, and at the end of the test, the inflammatory activity was measured through salivary interleukin-6; the mean blood pressure, and the capture of facial thermographic images. For the thermal images, six regions of interest (biothermomarkers) were analyzed: forehead, right cheek, left cheek, chin, nose, and corrugator muscle. The results obtained after analyzing the information were: an increase in inflammatory activity, an increase in mean blood pressure, and significant temperature changes in different areas of interest of the face, depending on gender. For men, it only appeared in the region of the nose and women's forehead, cheeks, and nose. Furthermore, the correlation between the three variables (il-6, blood pressure, and temperature) was performed and no significant values were found. Regarding the relationship between genders, only significant values were found for il-6.

Effects of the Trier Social Stress Test on the distributions of IL-6 and MAP levels.

Trier Social Stress Test (TSST) is an experimental psychological test that induces changes in autonomic, endocrinological and immunological activity. Two measures used to evaluate the inflammatory activity induced by this test are the interleukin 6 (IL-6), a cytokine sensitive to changes in sympathetic nervous activity, and the mean arterial pressure (MAP), a measure sensitive to changes in autonomic activity. This study had two goals: first, the study examined whether TSST increases IL-6 and MAP levels; second, pre- and post-TSST IL-6 levels were compared for participants whose IL-6 levels increased or decreased due to the TSST. Saliva samples of IL-6 and MAP were taken from 42 participants clinically healthy, without psychiatric history, and data were analysed via quantile comparisons. The results showed that TSST did not lead to an increase in sympathetic activity as indexed by IL-6. Instead, TSST led to increases in MAP. Also, there were significant differences between the IL-6 distributions of people whose IL-6 levels changed from low to high (63%) and from high to low (37%) before and after the TSST. These findings suggest that the TSST will not have the same effect on all participants; that is, individual differences can be assessed using a biomarker to identify people with specialized psychological care needs.

A Hilbert transform-based smart sensor for detection, classification, and quantification of power quality disturbances.

Power quality disturbance (PQD) monitoring has become an important issue due to the growing number of disturbing loads connected to the power line and to the susceptibility of certain loads to their presence. In any real power system, there are multiple sources of several disturbances which can have different magnitudes and appear at different times. In order to avoid equipment damage and estimate the damage severity, they have to be detected, classified, and quantified. In this work, a smart sensor for detection, classification, and quantification of PQD is proposed. First, the Hilbert transform (HT) is used as detection technique; then, the classification of the envelope of a PQD obtained through HT is carried out by a feed forward neural network (FFNN). Finally, the root mean square voltage (Vrms), peak voltage (Vpeak), crest factor (CF), and total harmonic distortion (THD) indices calculated through HT and Parseval's theorem as well as an instantaneous exponential time constant quantify the PQD according to the disturbance presented. The aforementioned methodology is processed online using digital hardware signal processing based on field programmable gate array (FPGA). Besides, the proposed smart sensor performance is validated and tested through synthetic signals and under real operating conditions, respectively.

Morphological rational operator for contrast enhancement.

Contrast enhancement is an important task in image processing that is commonly used as a preprocessing step to improve the images for other tasks such as segmentation. However, some methods for contrast improvement that work well in low-contrast regions affect good contrast regions as well. This occurs due to the fact that some elements may vanish. A method focused on images with different luminance conditions is introduced in the present work. The proposed method is based on morphological transformations by reconstruction and rational operations, which, altogether, allow a more accurate contrast enhancement resulting in regions that are in harmony with their environment. Furthermore, due to the properties of these morphological transformations, the creation of new elements on the image is avoided. The processing is carried out on luminance values in the u'v'Y color space, which avoids the creation of new colors. As a result of the previous considerations, the proposed method keeps the natural color appearance of the image.