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.

This paper presents a hybrid equalization (EQ) topology of lithium-ion batteries (LIB). Currently, LIBs are widely used for electric mobility due to their characteristics of high energy density and multiple recharge cycles. In an electric vehicle (EV), these batteries are connected in series and/or parallel until the engine reaches the voltage and energy capacity required. For LIBs to operate safely, a battery management system (BMS) is required. This system monitors and controls voltage, current, and temperature parameters. Among the various functions of a BMS, voltage equalization is of paramount importance for the safety and useful life of LIBs. There are two main voltage equalization techniques: passive and active. Passive equalization dissipates energy, and active equalization transfers energy between the LIBs. The passive has the advantage of being simple to implement; however, it has a longer equalization time and energy loss. Active is complex to implement but has fast equalization time and lower energy loss. This paper proposes the combination of these two techniques to implement simultaneously to control a pack of LIBs, equalizing voltage between stacks and at the cell level. For this purpose, a pack of LIBs was simulated with sixty-four cells connected in series and divided into eight stacks with eight battery cells each. The rated voltage of each cell is 3.7 V, with a capacity of 106 Ah. The total pack has a voltage of 236.8 V and 25 kW. Some LIBs were fitted with different SOC values to simulate an imbalance between cells. In the simulations, different topologies were evaluated: passive and active topology at the cell level and combined active and passive equalization at the pack level. Results are compared as a response time and state of charge (SOC) level. In addition, equalization topologies are applied in an EV model with the FTP75 conduction cycle. In this way, it is possible to evaluate the autonomy of each equalization technique simulated in this work. The hybrid topology active at the stack level and passive at the module level showed promising results in equalization time and autonomy compared with a purely active or passive equalization technique. This combination is a solution to achieve low EQ time and satisfactory SOC when compared to a strictly active or passive EQ.

The application of induction motors in industry is widespread. Thus, several studies have presented strategies for the diagnosis and prediction of failures in these motors. One technique used is based on the recent utilization of intelligent systems for detecting faults in electric motors. Thus, this paper proposes an alternative tool to traditional techniques for fault detection of a short circuit between the inter-turns of the stator winding using artificial neural networks to analyze stator current signals in the time domain. Experimental results are presented to validate the proposed approach.

Electric vehicles (EVs) rely on a battery pack as their primary energy source, making it a critical component for their operation. To guarantee safe and correct functioning, a Battery Management System (BMS) is employed, which uses variables such as State of Charge (SOC) to set charge/discharge limits and to monitor pack health. In this article, we propose a Multilayer Perceptron (MLP) network to estimate the SOC of a 14.8 V battery pack installed in a robotic vacuum cleaner. Both offline and online (real-time) tests were conducted under continuous load and with rest intervals. The MLP’s output is compared against two commonly used approaches: NARX (Nonlinear Autoregressive Exogenous) and CNN (Convolutional Neural Network). Performance is evaluated via statistical metrics, Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), and we also assess computational cost using Operational Intensity. Finally, we map these results onto a Roofline Model to predict how the MLP would perform on an automotive-grade microcontroller unit (MCU). A generalization analysis is performed using Transfer Learning and optimization using MLP–Kalman. The best performers are the MLP–Kalman network, which achieved an RMSE of approximately 13% relative to the true SOC, and NARX, which achieved approximately 12%. The computational cost of both is very close, making it particularly suitable for use in BMS.

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.

Purpose: In incomplete spinal cord injury (SCI), there is a partial decrease in motor or sensory or autonomic function. Mainly due to the motor impairment in SCI, a muscle–machine interface is a tool that can bring functional benefits to this population. Objective: To investigate the feasibility of the non-invasive myoelectric signal–functional electrical stimulation (MES-FES) interface on the response of the quadriceps muscle in an individual with incomplete SCI. Methods: This is a quasi-experimental, uncontrolled, longitudinal case report study carried out with an individual with incomplete SCI in the chronic phase. The assessments performed before (pre) and after eight (post₈) interventions were neuromuscular assessment (surface electromyography (EMG) in rectus femoris (RF) and vastus lateralis (VL) muscles); muscle strength (load cell); knee extension range of motion (goniometry); spasticity (Modified Ashworth Scale); and quality of life (Spinal Cord Injury Quality-of-Life Questionnaire (SCI-QoL.Br)). The MES-FES interface was associated with physical therapy exercises on the extension knee joint muscle group. Results: Improvement in neuromuscular activation (normalized increase in EMG_RMS of 2% (RF) and 3.3% (VL)) and synchronism of the motor units (normalized reduction in EMG_MDF of 22.8% (RF) and 5.9% (VL)); 1.4 kgf increase in quadriceps strength; 10.6° increase in knee joint extension amplitude; 1 point spasticity reduction; improved quality of life, confirmed by a 12-point reduction in the SCI-QoL.Br score. Moreover, along with interventions, the participant increased the correct FES activation rate, indicating a user learning curve ($\rho$ = 0.78, p-value = 0.04). Conclusions: The MES-FES interface associated with physical therapy promotes neuromuscular and quality of life improvements in the SCI participant.