This paper explores the potential benefits of integrating a brain–computer interface (BCI) utilizing the visual-evoked potential paradigm (SSVEP) with a six-degrees-of-freedom (6-DOF) robotic arm to enhance rehabilitation tools. The SSVEP-BCI employs electroencephalography (EEG) as a method of measuring neural responses inside the occipital lobe in reaction to pre-established visual stimulus frequencies. The BCI offline and online studies yielded accuracy rates of 75% and 83%, respectively, indicating the efficacy of the system in accurately detecting and capturing user intent. The robotic arm achieves planar motion by utilizing a total of five control frequencies. The results of this experiment exhibited a high level of precision and consistency, as indicated by the recorded values of ±0.85 and ±1.49 cm for accuracy and repeatability, respectively. Moreover, during the performance tests conducted with the task of constructing a square within each plane, the system demonstrated accuracy of 79% and 83%. The use of SSVEP-BCI and a robotic arm together shows promise and sets a solid foundation for the development of assistive technologies that aim to improve the health of people with amyotrophic lateral sclerosis, spina bifida, and other related diseases.

This paper presents the implementation of nonlinear canonical correlation analysis (NLCCA) approach to detect steady-state visual evoked potentials (SSVEP) quickly. The need for the fast recognition of proper stimulus to help end an SSVEP task in a BCI system is justified due to the flickering external stimulus exposure that causes users to start to feel fatigued. Measuring the accuracy and exposure time can be carried out through the information transfer rate—ITR, which is defined as a relationship between the precision, the number of stimuli, and the required time to obtain a result. NLCCA performance was evaluated by comparing it with two other approaches—the well-known canonical correlation analysis (CCA) and the least absolute reduction and selection operator (LASSO), both commonly used to solve the SSVEP paradigm. First, the best average ITR value was found from a dataset comprising ten healthy users with an average age of 28, where an exposure time of one second was obtained. In addition, the time sliding window responses were observed immediately after and around 200 ms after the flickering exposure to obtain the phase effects through the coefficient of variation (CV), where NLCCA obtained the lowest value. Finally, in order to obtain statistical significance to demonstrate that all approaches differ, the accuracy and ITR from the time sliding window responses was compared using a statistical analysis of variance per approach to identify differences between them using Tukey’s test.

The development of prostheses that accurately reproduce fine motor skills remains a key challenge for daily assistance applications. This research presents the development of a soft robotic hand prosthesis prototype inspired by the natural behavior of muscles and tendons, incorporating internal vacuum-based reinforcement and textured fingertip surfaces to enhance friction and grasp adaptability, without relying on force sensors. The prosthesis reproduces open-hand and tripod pinch movements through myoelectric signals (EMG) acquired via a wearable armband equipped with eight surface electrodes. The signals are processed in real-time and classified by a lightweight dense neural network implemented on a low-power microcontroller. Tendon-driven actuation enables biomimetic motion with smooth and compliant behavior. The proposed system was validated through laboratory-based functional tests using user-specific models, showing response times ranging from 0.49 to 2.00 s and an overall grasping effectiveness of approximately 80% when manipulating small everyday objects with different geometries. These results indicate that the prototype constitutes an accessible and functional solution for fine motor assistance, with potential applicability in low-cost and resource-constrained myoelectric prosthetic systems.

The ergonomic risks associated with posture in conventional office workstations have been extensively studied, but there is limited research available on these risks in the context of home-based work environments. Most available studies rely solely on questionnaire-based statistical analyses, leaving a gap in understanding the specific conditions of home-based work environments. This study focuses on evaluating the effects of workstation conditions on posture and muscular efforts across three anatomical segments: head-neck, trunk-upper trapezius, and arm-deltoid. The analysis is conducted by simulating workstation setups commonly associated with academic activities performed by students during the COVID-19 pandemic. The conditions examined in this study include inadequate desk height, the use of chairs without armrests, and the use of laptops. Eighteen volunteers, comprising nine women and nine men, participated in experiments conducted under scenarios designed using a $2^k$ statistical approach. In all experiments, participants completed questionnaires, and text-writing activities were performed to evaluate the effects of these conditions. This research introduces a new non-invasive technique for ergonomic assessment that integrates photogrammetry and surface electromyography (sEMG) to simultaneously evaluate posture and muscular effort. The developed methodology allows precise, contactless analysis of ergonomic conditions and can be adapted for various professional and academic teleworking environments. Significant effects were observed in the posture (°) of the trunk and head, with both small and large effects identified at significance levels of p < 0.001 under the furniture conditions studied. In terms of EMG activity, moderate effects were observed at p < 0.01 levels between table height and upper trapezius activation, while small effects were detected at p < 0.05 levels between the use of chairs without armrests and neck. Similarly, small to moderate effects were observed in the arm-deltoid segment under the same furniture conditions. These findings reveal information about the posture and muscular effort patterns associated with the studied tasks, offering knowledge that can be referenced for similar tasks in other technical fields where telematics activities are performed.