Non-invasive muscle-machine interface open source project: wearable hand myoelectrical orthosis (MES-FES).

The paper describes the development of an open-source, low-cost, wearable hand myoelectrical orthosis (neuro-orthosis) device for people with hand disabilities. The device uses functional electrical stimulation (FES) driven by myoelectrical signals (MES) to assist hand movements, enabling users to perform daily activities with greater ease and independence. The device comprises a forearm-wearable device developed using the 3D additive manufacturing principle, allowing user customization. Fixed non-disposable electrodes are attached to the myoelectrical orthosis, aiding the correct positioning for the user. The whole control system is stand-alone, and parameters can be controlled by Bluetooth communication, making the device wireless. The paper describes the MES-FES device's design, development, and testing, including its technical specifications, usability, and effectiveness. The open-source project aims to provide an accessible and affordable solution for people with spinal cord lesions while contributing to the growing research on noninvasive muscle-machine interfaces.

Analysis of Influence of Segmentation, Features, and Classification in sEMG Processing: A Case Study of Recognition of Brazilian Sign Language Alphabet.

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 MyoTM 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.

Sensors and Systems for Physical Rehabilitation and Health Monitoring-A Review.

The use of wearable equipment and sensing devices to monitor physical activities, whether for well-being, sports monitoring, or medical rehabilitation, has expanded rapidly due to the evolution of sensing techniques, cheaper integrated circuits, and the development of connectivity technologies. In this scenario, this paper presents a state-of-the-art review of sensors and systems for rehabilitation and health monitoring. Although we know the increasing importance of data processing techniques, our focus was on analyzing the implementation of sensors and biomedical applications. Although many themes overlap, we organized this review based on three groups: Sensors in Healthcare, Home Medical Assistance, and Continuous Health Monitoring; Systems and Sensors in Physical Rehabilitation; and Assistive Systems.

A Prototype to Detect the Alcohol Content of Beers Based on an Electronic Nose.

Due to the emergence of new microbreweries in the Brazilian market, there is a need to construct equipment to quickly and accurately identify the alcohol content in beverages, together with a reduced marketing cost. Towards this purpose, the electronic noses prove to be the most suitable equipment for this situation. In this work, a prototype was developed to detect the concentration of ethanol in a high spectrum of beers presents in the market. It was used cheap and easy-to-acquire 13 gas sensors made with a metal oxide semiconductor (MOS). Samples with 15 predetermined alcohol contents were used for the training and construction of the models. For validation, seven different commercial beverages were used. The correlation (R2) of 0.888 for the MLR (RMSE = 0.45) and the error of 5.47% for the ELM (RMSE = 0.33) demonstrate that the equipment can be an effective tool for detecting the levels of alcohol contained in beverages.