Current Issue : April-June Volume : 2024 Issue Number : 2 Articles : 5 Articles
Recently, motor imagery brain–computer interfaces (BCIs) have been developed for use in motor function assistance and rehabilitation engineering. In particular, lower-limb motor imagery BCI systems are receiving increasing attention in the field of motor rehabilitation, because these systems could accurately and rapidly identify a patient’s lower-limb movement intention, which could improve the practicability of the motor rehabilitation. In this study, a novel lower-limb BCI system combining visual stimulation, auditory stimulation, functional electrical stimulation, and proprioceptive stimulation was designed to assist patients in lower-limb rehabilitation training. In addition, the Riemannian local linear feature construction (RLLFC) algorithm is proposed to improve the performance of decoding by using unsupervised basis learning and representation weight calculation in the motor imagery BCI system. Three in-house experiment were performed to demonstrate the effectiveness of the proposed system in comparison with other state-of-the-art methods. The experimental results indicate that the proposed system can learn low-dimensional features and correctly characterize the relationship between the testing trial and its k-nearest neighbors....
Facial emotion recognition (FER) has a huge importance in the field of human–machine interface. Given the intricacies of human facial expressions and the inherent variations in images, which are characterized by diverse facial poses and lighting conditions, the task of FER remains a challenging endeavour for computer-based models. Recent advancements have seen vision transformer (ViT) models attain state-of-the-art results across various computer vision tasks, encompassing image classification, object detection, and segmentation. Moreover, one of the most important aspects of creating strong machine learning models is correcting data imbalances. To avoid biased predictions and guarantee reliable findings, it is essential to maintain the distribution equilibrium of the training dataset. In this work, we have chosen two widely used open-source datasets, RAF-DB and FER2013. As well as resolving the imbalance problem, we present a new, balanced dataset, applying data augmentation techniques and cleaning poor-quality images from the FER2013 dataset. We then conduct a comprehensive evaluation of thirteen different ViT models with these three datasets. Our investigation concludes that ViT models present a promising approach for FER tasks. Among these ViT models, Mobile ViT and Tokens-to-Token ViT models appear to be the most effective, followed by PiT and Cross Former models....
Many robots that play with humans have been developed so far, but developing a robot that physically contacts humans while playing is challenging. We have developed robots that play tag with humans, which find players, approach them, and move away from them. However, the developed algorithm for approaching a player was insufficient because it did not consider how the arms are attached to the robot. Therefore, in this paper, we assume that the arms are fixed on both sides of the robot and develop a new algorithm to approach the player and touch them with an arm. Since the algorithm aims to move along a circular orbit around a player, we call this algorithm “the go-round mode”. To investigate the effectiveness of the proposed method, we conducted two experiments. The first is a simulation experiment, which showed that the proposed method outperformed the previous one. In the second experiment, we implemented the proposed method in a real robot and conducted an experiment to chase and touch the player. As a result, the robot could touch the player in all the trials without collision....
The efficient translation of brain signals into an output device is an essential characteristic to establish a Brain-computer Interface (BCI) link. This research investigates the applicability of diverse correlation indices for the differentiation of specific movements (left, right, both, or none) and states (real or imaginary) in a private BCI dataset, including EEG recordings of 32 participants. As such, the recorded brain activation data were employed to illustrate the differences between visual- and auditory-event-related responses during task performance. Our methodology involved a two-pronged approach. Firstly, EEG data were collected, capturing both the visual- and auditoryevent- related signals that corresponded to each of the four movement classes. Secondly, we performed a comparative analysis of the collected dataset using various correlation algorithms, such as Pearson, Spearman, and Kendall, among others, to evaluate their effectiveness in differentiating between movements and states. The results demonstrated distinctive correlation patterns, as the selected indices effectively distinguished between real and imaginary movements, as well as between different lower limp movements in most cases. Moreover, the correlation schemas of certain individuals presented greater sensitivity in discerning nuances within the dataset. In this regard, it can be inferred that the chosen correlation indices can provide valuable insights into the aforementioned differentiation in EEG data. The results open up potential paths for improving BCI interfaces and contributing to more accurate prediction models....
In the emergent field of digital therapeutics (DTx), this study examines the impact of virtual agent design on usability and therapeutic outcomes. Emphasizing the virtual agent’s role, our research highlights a marked therapeutic effect tied to the DTx’s developed parameters. Continuous usage, influenced by perceived usefulness, user attitudes, and intrinsic enjoyment, emerges as a crucial determinant for desired outcomes. The study finds anthropomorphism and agent likeability as pivotal factors in enhancing user experience and promoting sustained DTx use. Although focusing on mental health, particularly depression, the implications suggest varied results across DTx types. Given these insights, our findings advocate for a deeper exploration into agent-centric DTx designs, particularly in mental health applications. The nuances of user engagement with these therapeutic tools, especially in treating conditions like depression, demonstrate a diverse range of effects and underscore the importance of personalized approaches in digital therapeutics. This study’s outcomes not only shed light on the significant role of virtual agents but also call for continuous innovation and research in this evolving domain....
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