Acquiring biopotentials with fidelity using low-cost circuits is a significant challenge in biomedical instrumentation. In this perspective, our goal is to investigate the characteristics of the widely applied AD8232^®, an analog front-end for biopotential acquisition. We designed and evaluated circuits to acquire the most common biosignals: electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG). Our findings show that the circuit is suitable for ECG and EMG instrumentation, although it has limitations for EEG signals, particularly concerning the gain. The entire project of the boards is also a contribution of this work as we intend to corroborate open-source do-it-yourself biomedical instrumentation.

Sign Language recognition systems aid communication among deaf people, hearing impaired people, and speakers. One of the types of signals that has seen increased studies and that can be used as input for these systems is surface electromyography (sEMG). This work presents the recognition of a set of alphabet gestures from Brazilian Sign Language (Libras) using sEMG acquired from an armband. Only sEMG signals were used as input. Signals from 12 subjects were acquired using a Myo^TM armband for the 26 signs of the Libras alphabet. Additionally, as the sEMG has several signal processing parameters, the influence of segmentation, feature extraction, and classification was considered at each step of the pattern recognition. In segmentation, window length and the presence of four levels of overlap rates were analyzed, as well as the contribution of each feature, the literature feature sets, and new feature sets proposed for different classifiers. We found that the overlap rate had a high influence on this task. Accuracies in the order of 99% were achieved for the following factors: segments of 1.75 s with a 12.5% overlap rate; the proposed set of four features; and random forest (RF) classifiers.

Among consumers, there is a growing concern with the monitoring of the consumption of each individual equipment or appliance, rather than the consumption measurement of the whole unit. One way of individualizing the consumption is NonIntrusive Load Monitoring (NILM) based on power signature analysis. The main challenges with NILM approaches are the selection of relevant signature features for load identification (classification). Several approaches have been proposed in the literature using steady-state and transient characteristics. Among these methods, Voltage-Current (V-I) trajectories can be highlighted, mainly in steady-state conditions. In this work, we extend the features extracted from V-I trajectories, including new steady-state and transient features. We compare our approach to two other methods based on V-I trajectories recently presented in literature, using three different machine learning classifiers for load identification and a publicly available dataset as a benchmark (COOLL - Controlled ON-OFF Loads Library). Our results show that the proposed approach presents comparable results to those presented in the literature, achieving more than 99% of load recognition accuracy while significantly reducing the number of features. Most of the new proposed features – including transient-related – were selected in a feature selection procedure, demonstrating their relevance in the load identification and classification.

Introduction: Low- to high-energy impact trauma may cause from small fissures up to extended bone losses, which can be classified as closed or opened injuries (when they are visible at a naked eye). Objective: The aim of this study was to investigate the feasibility of clinical diagnosis of bone trauma through medical infrared thermography, in a hospital emergency room. Methods: Forty-five patients with suspected diagnosis of bone fracture were evaluated by means of medical infrared images, and the data correlated with the gold standard radiographic images, in the anteroposterior, lateral, and oblique views, at the orthopedic emergency department. The control group consisted of thermal images of the contralateral reference limb of the volunteers themselves. Data were acquired with a medical grade infrared camera in the regions of interest (ROIs) of leg, hand, forearm, clavicle, foot, and ankle. Results: In all patients evaluated with a diagnosis of bone fracture, the mean temperature of the affected limb showed a positive difference greater than 0.9 °C (towards the contralateral), indicating the exact location of the bone trauma according, while the areas diagnosed with reduced blood supply, showed a mean temperature with a negative variation. Conclusion: Clinical evaluation using infrared imaging indicates a high applicability potential as a tool to support quick diagnosis of bone fractures in patients with acute orthopedic trauma in an emergency medical setting. The thermal results showed important physiological data related to vascularization of the bone fracture and areas adjacent to the trauma well correlated to radiographic examinations.