1,721,003 research outputs found
Optimising the continuous control of brain-actuated robotic devices
Full text linkBrain-machine interfaces (BMIs) are alternative communication channels that have allowed healthy and disabled people to control external devices from brain signals. In the last decades, the growing attention towards neurorobotics has led to the proliferation of several BMI-based systems for controlling different devices including telepresence robots, powered wheelchairs, robotic arms, and upper/lower-limb exoskeletons. Despite the potentialities of these systems, it has emerged the necessity to create new forms of interaction between the human and the robot in order to increase the granularity of the user's commands which are, in turn, translated into specific robot's actions. In this preliminary work, we present how artificial intelligence can be exploited to design and tune a model able to convert the user's intention into continuous robot's movements
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Environment-Adaptive Gait Planning through Reinforcement Learning for Lower-Limb Exoskeletons
No full textPowered lower limb exoskeletons (LLEs) have demonstrated significant potential in augmenting mobility and providing rehabilitative support for individuals with gait impairments. However, most assistive exoskeletons rely on predetermined gait trajectories, limiting their effectiveness in unstructured environments. To address this limitation, Environment Adaptive Gait Planning (EAGP) strategies have emerged, focusing on real-time trajectory adaptation based on environmental perception. This work introduces a novel approach to EAGP using Deep Reinforcement Learning (DRL) for generating adaptive foot trajectories, specifically targeting obstacle avoidance during ground walking. The proposed method optimizes trajectory smoothness, environmental interaction, and compliance with exoskeleton kinematic constraints, as validated by simulations. This study advances the state-of-the-art of adaptive gait planning by leveraging the generalization capabilities of DRL, paving the way for enhanced mobility in real-world applications
Decoding EEG Signals during the Observation of Robotic Arm Movements
Full text linkRecent studies in the domain of invasive brain-computer interfaces (BCIs) have revealed that neural activity recorded during the observation of robotic movements in a reach-and-grasp task carries information that can be utilized to improve the active online decoding of motor intention. In the non-invasive domain, the spectral characteristics of human brain activity during the observation of robotic movements has been widely investigated. However, focusing only on the frequency components of electroencephalography (EEG) for motor control decoding is a poorly suitable strategy due to its scarce temporal resolution. Following a different approach, we explored temporal features of EEG filtered in the delta band (Low-Frequency EEG, or LF-EEG) for the continuous decoding of control-oriented kinematic trajectories. We designed an experimental paradigm aimed at investigating how the observation of center-out target-oriented reaching movements executed by a robotic arm in the 2D plane is encoded in low-frequency EEG signals. By employing machine learning algorithms and novel approaches, we were able to continuously decode the LF-EEG into movement trajectories, achieving performance significantly above chance-level. This confirms that low-frequency neural activity measured non-invasively during a movement observation task contains adequate amounts of movement-related information for BCI applications
Sintesi di un analogo selettivamente fluoresceinato di un peptide del PND che aumenta l'infettività del virus HIV-1
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Real-Time Free Space Semantic Segmentation for Detection of Traversable Space for an Intelligent Wheelchair
No full textThe last decades saw a great innovation in computer vision. Recently, the field has been fundamental in the development of autonomous navigation systems. Modern assistive technologies, like smart wheelchairs, could employ autonomous navigation to assist users during operation. A prerequisite for such systems is to recognise the navigable space in real-time. The current research features an off-the-shelf powered wheelchair customised into an intelligent robot, which perceives the environment using Point Cloud Semantic Segmentation (PCSS). The implemented algorithm is used to distinguish between two conditions, traversable and nontraversable space, in real-time, using the aforementioned conditions as the two labelled classes. The accuracy of traversable space detection resulted as 99.64% while the accuracy of nontraversable space detection was 91.79%. The performance of the suggested method was invariant to changes in wheelchair velocity indicating that the latency of the suggested algorithm is within the tolerable limits for real-time operation
Real-time EEG Feedback on Alpha Power Lateralization Leads to Behavioral Improvements in a Covert Attention Task
No full textBrain-Computer Interface for children: State-of-the-art and challenges
No full textThis work proposes an overview of the recent applications of brain-computer interface (BCI) technology for pediatric populations. Current BCIs have demonstrated the possibility to provide an alternative communication and interaction channel for people suffering from severe motor disabilities. However, to date research has been predominantly conducted in adults, only a few systems have been applied to pediatric population. A survey was carried out to show the ongoing trends of using BCI systems with children. We discuss three areas of applications where BCI might be helpful to children - "Communication Control", "BCI Gaming for Neurofeedback Training" and "Rehabilitation" - highlighting the current limitations and the possible future challenges
The Effect of User Learning for Online EEG Decoding of Upper-Limb Movement Intention
Full text linkElectroencephalography (EEG) based braincomputer interfaces (BCIs) offer a promising way for individuals with motor impairments to control prosthetic or rehabilitation devices. Accurately decoding movement intention (MI) is crucial for translating subjects' motor execution plans into action. Common challenges in EEG-based BCIs include performance discrepancies, often requiring frequent recalibration of decoding algorithms. The objective of this study was enhancing BCI decoding performance of upper-limb MI identification by exploiting both machine and subjects learning and maintaining stable decoding algorithms. Significant performance improvements were observed across most subjects from the first to the last session of the experiment. Some subjects also demonstrated stable performance without requiring any model recalibration between sessions. All subjects achieved high efficacy in online decoding of movement intention, as reflected in improvement of the F1 score from 0.5810.26 in the first session, to 0.84±0.13 in the final session. We emphasize the critical importance of allowing users sufficient time to improve their performance in BCIs for upper-limb MI decoding. Unlike existing studies, we specifically evaluate the effect of stable decoding strategies in online and longitudinal BCI sessions, which are key to achieving more reliable and effective BCIs
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