This work presents the development of an augmented and alternative communication system, consisting of a multimodal interface controlled by different types of signals (electromyography, electrooculography, accelerometer and ON/OFF button). The multimodal interface was tested in volunteers with severe motor disabilities and without motor restrictions. The device was applied for controlling an alternative communication software based on automatic scanning and single ON/OFF commands. The volunteers used the system for more than 30 days and demonstrate a significant evolution in the control of the multimodal system developed. The multimodal interface developed presented stability and reliability using only an adaptive channel to receive both EMG signals as EOG. Also, the possibility of adding external sensors ON/OFF type extends the amount of people who could be assisted with the use of the system, which makes it more useful and versatile.

Magnetic resonance imaging (MRI) usage is increasing during last years and has become the method of choice for the investigation of neuroanatomy in vivo. At either end of the MRI analysis spectrum, there are manual and automated approaches; manual approaches are user-dependent and time-consuming, but are considered to be the gold standard of MR image analysis techniques. We compared the brain, white matter (WM), and gray matter (GM) volumes of automated segmentation in order to evaluate the process of segmentation. We analyzed 59 health subject images between 31 and 82 years old from an image bank, obtaining volumes by two of the most used software for brain processing, FSL and FreeSurfer. We also proposed a new method to improve brain segmentation volume with FSL. A difference of 35%, 46%, and 9% for brain, white matter, and gray matter volumes, respectively, was shown. The volumes obtained with FSL and FreeSurfer were significantly correlated (p < 0.05) with correlation coefficients of 0.39, 0.79, and 0.52 for the brain, WM, and GM. After the proposed method for FSL brain volume correction (FSLcorr), the correlation coefficient improved from 0.39 to 0.77. Volumes obtained with FreeSurfer were significantly smaller than those with FSL (p < 0.05) and using FSLcorr method, it was possible to improve FSL measurements.

This article presents an innovative system that provides assistance to individuals with Alzheimer’s disease (AD) through the use of a mobile augmented reality (MAR) interface. By using MAR with voice commands and virtual buttons adapted to enable a better human-computer interaction (HCI), this study proposes a system that assists in pharmacological and non-pharmacological treatment. The authors have conducted an experiment to evaluate the acceptance of this system in individuals with AD in the early stages. Six individuals with AD participated in the experiment. Three sessions were held to assess acceptance of the system in assistance with pharmacological treatment. Three other sessions were conducted to evaluate non-pharmacological activity. We measured the time spent and the number of interventions required for the participant to complete the task. We performed an observational analysis of the experiment and applied a questionnaire at the end of the evaluation so that the caregiver could answer questions together with the research participant. All participants were able to execute the proposed activities. During the sessions, most participants showed improvements in their performance. The observational analysis and questionnaire results show a good acceptance of this system in terms of the volunteers. This work proposed an alternative for assisting individuals with AD during their pharmacological and non-pharmacological treatment, innovating by means of interacting with medications and photo albums. Through the use of voice commands and a custom MAR interface, a good acceptance of this system by individuals with AD in early stage has been identified.

In this study, three different methods to identify event-related electrodermal responses (ER.EDRs) were applied, comparing noxious heat stimulation at four different levels. Although the level of heat leading to subjective pain is individual, temperatures above 40 to 45 °C are normally experienced as painful due to the stimulation of heat-sensitive nociceptors. Skin conductance (SC) data from 96 healthy participants aged between 18 and 65 years old were analyzed by means of continuous decomposition analysis (CDA), discrete decomposition analysis (DDA), and trough-to-peak analysis (TTP). Several EDR features estimated from these methods were statistically compared. Within the analysis window following each single stimulation, CDA and DDA methods identified more electrodermal responses between 1 and 9 s after noxious stimulation than TTP for all stimulation intensities. However, the occurrence rates of at least one ER.EDR after noxious stimulation were similar among the three methods and tended to increase with increasing intensities. Among the common features, amplitude sum had better discriminative power for differentiating noxious stimulation intensities regardless of the method. The results suggest that all investigated methods performed similarly in identifying electrodermal changes in response to high-intensity thermal noxious stimuli. In the experimental conditions of cutaneous heat stimulation of nociceptors, good discrimination among stimulation intensities was found using the amplitude sum feature. In conclusion, although CDA and DDA were more sensitive than TTP for identifying ER.EDRs, neither CDA nor DDA brought further discriminative power to the study of noxious stimulation intensities when compared with the traditional TTP method.

In neurodegenerative clinical studies, patients are usually followed up for several years frequently examined in different MRI scanners and base magnetic fields. Technical specification differences in MRI scanners, e.g., acquisition protocols and spatial resolution, are two crucial limitations for longitudinal long-term cerebral atrophy investigations. Although widely known that MRI base magnetic field (B0) boldly affects brain tissue volume measurements, no systematic study has been proposed to maintain brain volume consistency through longitudinal exams. We propose a method to convert the measured volumes of segmented brain compartments from one MRI scanner with lower B0 and resolutions to volumes consistent with a higher resolution. The proposed partial volume transfer (PVT) method is consistent with partial volume effect to correct the volume measures. The PVT demands at least one pair of simultaneous acquisitions on both MRI scanners to estimate three unknown coefficients completing the PVT system. We selected ten healthy subjects from a standard dataset on the web, ten other healthy subjects, and ten patients with multiple sclerosis diseases scanned by two different scanners, i.e., 1.5 T and 3.0 T from local hospital dataset, to evaluate the PVT. We obtained the mean relative errors ranging from 0.28 to 1.14% of total brain volume using the PVT conversion for brain compartments, significantly less than interpolation methods which were more than 6.54%. By providing a real longitudinal sequence of images during 9-years follow-up of a patient, the PVT conversion was used to accurately convert cerebral tissues volumes acquired in different spatial resolutions.

Abstract Introduction There is a need to develop methods to evaluate public health interventions. Therefore, this work proposed an intervention analysis on time series of breast cancer mortality rates to assess the effects of an action of the Brazilian Screening Programme. Methods The analysed series was the monthly female breast cancer mortality rates from January 1996 to March 2016. The intervention was the establishment of the National Information System on Breast Cancer in June 2009. The Box-Tiao approach was used to build a Global Intervention Model (GIM) composed of a component that fits the series without the intervention, and a component that fits the effect with the intervention. The intervention’s response time was estimated and used to define the length of the residual series to assess the predictive accuracy of the GIM, which was compared to a one-step-ahead forecasting approach. Results The pre-intervention period was fitted to a SARIMA (0,1,2) (1,1,1)12 model and the intervention’s effect to an ARIMA (1,1,0) model. The intervention led to an increase in the mortality rates, and its response time was 24 months. The forecast error (MAPE) for the GIM was 3.14%, and for the one-step-ahead forecast it was 2.15%. Conclusion This work goes one step further in relation to the studies carried out to evaluate the Breast Cancer Screening Programme in Brazil, considering that it was possible to quantify the effects and the response time of the intervention, demonstrating the potential of the proposed method to be used to evaluate health interventions.

Abstract Introduction The use of the same imaging and quantification techniques in small animals and clinical studies presents the opportunity for direct translational research in drug discovery and development, in neuropharmacological basis of neurological and psychiatric diseases, and in optimization of drug therapy. Thus, positron emission tomography (PET) studies in rodents can bridge the gap between pre-clinical and clinical research. The aim should be to find a method with capability to measure, without compromising accuracy, glucose distribution in the structures of the brain, which can also be used in pathological situations and with applicability for other substances than glucose analogue. Methods This is a systematic review of several assessment techniques available, including visual and quantitative methods that enable the investigation of the transport mechanisms and enzymes involved in glucose metabolism in the brain. In addition to the ex vivo methods, PET with glucose analogues allows in vivo analyses using qualitative, semiquantitative and quantitative methods. Results These techniques provide different results, and the applicability of a specific method is related to the purpose of the study and the multiple factors that may interfere in the process. Conclusion This review provides a solid background of tools and quantification methods for medical physicists and other professionals interested in cerebral glycolytic metabolism quantification in experimental animals. It also addresses the main factors related to animals, equipment and techniques that are used, as well as how these factors should be understood to better interpret the results obtained from experiments.

Abstract Introduction Premature Ventricular Contraction (PVC) is among the most common types of ventricular cardiac arrhythmia. However, it only poses danger if the person suffers from a heart disease, such as heart failure. Hence, this is an important factor to consider in heart disease people. This paper presents an ECG real-time analysis system for PVC detection. Methods This system is based on threshold adaptive methods and Redundant Discrete Wavelet Transform (RDWT), with a real-time approach. This analysis is based on wavelet coefficients energy for PVC detection. It is presented also a study to find the most indicated wavelet mother for ECG analysis application among the following wavelet families: Daubechies, Coiflets and Symlets. The system detection performance was validated on the MIT-BIH Arrhythmia Database. Results The best results were verified with db2 wavelet mother: the Sensitivity Se = 99.18%, Positive Predictive Value P+ = 99.15% and Specificity Sp = 99.94%, on 80.872 annotated beats, and 61.2 s processing speed for a half-hour record. Conclusion The proposed system exhibits reliable PVC detection, with real-time approach, and a simple algorithmic structure that can be implemented in many platforms.

Abstract Introduction Polymer optical fibers (POF) are lightweight, present high elastic strain limits, fracture toughness, flexibility in bend, and are not influenced by electromagnetic fields. These characteristics enable the application of POF as curvature sensor and can overcome the limitations of the conventional technologies, especially for wearable and soft robotics devices. Nevertheless, POF based curvature sensors can suffer from environmental and light source power deviations. This paper presents a compensation technique for the environmental and light source power deviations in a POF curvature sensor. Methods The curvature sensor was submitted to variations of temperature, humidity and light source power to characterize the sensor response and evaluate the proposed compensation technique. In addition, tests with the simultaneous variation of the angle and light source power variation were performed. Results Results show that temperature and humidity effects do not lead to significative errors on the sensor measurement for wearable devices application, where a hardware-based compact and portable compensation technique of the light source deviation is applied. Moreover, the sensor with the compensation technique developed is compared with a potentiometer for dynamic measurements and the root-mean-square error of about 1° is obtained, which is lower than sensors based on similar operation principle presented in the literature and some commercially available devices. Conclusions The compensation technique proposed was able to compensate power deviations applied and resulted in a sensor with low errors with the additional advantages of compactness and low-cost, which enable its application as wearable sensors and on the instrumentation of wearable robots.

Abstract Introduction Breast cancer is the most common cancer in women and one of the major causes of death from cancer among female around the world. The early detection and treatment are the major way to healing. The use of mammary thermography in Mastology is increasing as a complementary imaging technique to early detect lesions. Its use as a screening exam to identify breast disorders has been investigated. The aim of this study is to investigate the behavior of different classification methods while grouping the thermographic images into specific types of lesions. Methods To evaluate our proposal, we built classifiers based on artificial neural networks, decision trees, Bayesian classifiers, and Haralick and Zernike attributes. The image database is composed by thermographic images acquired at the University Hospital of the Federal University of Pernambuco. These images are clinically classified into the classes cyst, malignant and benign. Moments of Zernike and Haralick were used as attributes. Results Extreme Learning Machines (ELM) and Multilayer Perceptron networks (MLP) proved to be quite efficient classifiers for classification of breast lesions in thermographic images. Using 75% of the database for training, the maximum value obtained for accuracy was 73.38%, with a Kappa index of 0.6007. This result indicated to a sensitivity of 78% and specificity of 88%. The overall efficiency of the system was 83%. Conclusion ELM showed to be a promising classifier to be used in the differentiation of breast lesions in thermographic images, due to its low computational cost and robustness.