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Applied Metrology for the digital transition in Healthcare
Full text linkL'abstract è presente nell'allegato / the abstract is in the attachmen
Design, implementation, and metrological characterization of a wearable, integrated AR-BCI hands-free system for health 4.0 monitoring
Full text linkAn integrated real-time monitoring system based on Augmented Reality (AR) and Brain–Computer Interface (BCI) for hands-free acquisition and visualization of remote data is proposed. As a case study, the monitoring of patients’ vitals in the operating room (OR) is considered; in particular, through the suitable combination of BCI and AR, the anesthetist can monitor in real-time (through a set of AR glasses), the patient’s vitals acquired from the electromedical equipment. Healthcare-related applications are particularly demanding in terms of real-time requirements; hence, the considered scenario represents an interesting and challenging testbed for the proposed system. Experimental tests were carried out at the University Hospital Federico II (Naples, Italy), employing pieces of equipment that are generally available in the OR. After the preliminary functional validation, accuracy and delay were measured, demonstrating the effectiveness and reliability of the proposed AR-BCI-based monitoring system
Wearable Brain-Computer Interface instrumentation for robot-based rehabilitation by Augmented Reality
No full textExploring the Relationship Between Performance and Environmental Sustainability in Measurement Systems: A Preliminary Study
No full textHighly wearable SSVEP-based BCI: Performance comparison of augmented reality solutions for the flickering stimuli rendering
Full text linkA highly-wearable single-channel Brain- Computer Interface (BCI) based on Steady-State Visually Evoked Potentials (SSVEPs) and Augmented Really (AR) is proposed. The SSVEP elicitation is provided by three AR head-mounted displays (HMD), namely Epson Moverio BT-350, Oculus Rift S, and Microsoft HoloLens. Four flickering stimuli, ranging from 8 Hz to 15 Hz, are used. The goal of the work is to carry out a performance comparison of the three aforementioned devices, in terms of stimuli visualization and SSVEPs detection. To this aim, classification accuracy and time response were assessed involving nine healthy volunteers during the experimental activity. The obtained results demonstrate that choosing an adequate HMD to render the flickering stimuli is decisive for obtaining adequate performances
Enhancement of SSVEPs Classification in BCI-based Wearable Instrumentation Through Machine Learning Techniques
No full textThis work addresses the adoption of Machine Learning classifiers and Convolutional Neural Networks to improve the performance of highly wearable, single-channel instrumentation for Brain-Computer Interfaces. The proposed measurement system is based on the classification of Steady-State Visually Evoked Potentials (SSVEPs). In particular, Head-Mounted Displays for Augmented Reality are used to generate and display the flickering stimuli for the SSVEPs elicitation. Four experiments were conducted by employing, in turn, a different Head-Mounted Display. For each experiment, two different algorithms were applied and compared with the state-of-the-art-techniques. Furthermore, the impact of different Augmented Reality technologies in the elicitation and classification of SSVEPs was also explored. The experimental metrological characterization demonstrates (i) that the proposed Machine Learning-based processing strategies provide a significant enhancement of the SSVEP classification accuracy with respect to the state of the art, and (ii) that choosing an adequate Head-Mounted Display is crucial to obtain acceptable performance. Finally, it is also shown that the adoption of inter-subjective validation strategies such as the Leave-One-Subject-Out Cross Validation successfully leads to an increase in the inter-individual 1-σ reproducibility: this, in turn, anticipates an easier development of ready-to-use systems
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
Adoption of Machine Learning Techniques to Enhance Classification Performance in Reactive Brain-Computer Interfaces
Full text linkThis paper proposes the adoption of an innovative algorithm to enhance the performance of highly wearable, reactive Brain-Computer Interfaces (BCIs), which exploit the Steady-State Visually Evoked Potential (SSVEP) paradigm. In particular, a combined time-domain/frequency-domain processing is performed in order to reduce the number of features of the brain signals acquired. Successively, these features are classified by means of an Artificial Neural Network (ANN) with a learnable activation function. In this way, the user intention can be translated into commands for external devices. The proposed algorithm was initially tested on a benchmark data set, composed by 35 subjects and 40 simultaneous flickering stimuli, obtaining performance comparable with the state of the art. Successively, the algorithm was also applied to a data set realized with highly wearable BCI equipment. In particular, (i) Augmented Reality (AR) smart glasses were used to generate the flickering stimuli necessary to the SSVEPs elicitation, and (ii) a single-channel EEG acquisition was conducted for each volunteer. The obtained results showed that the proposed strategy provides a significant enhancement in SSVEPs classification with respect to other state-of-the-art algorithms. This can contribute to improve reliability and usability of brain computer interfaces, thus favoring the adoption of this technology also in daily-life applications
A Wearable AR-based BCI for Robot Control in ADHD Treatment: Preliminary Evaluation of Adherence to Therapy
Full text linkA wearable, single-channel Brain-Computer Interface (BCI) based on Augmented Reality (AR) and Steady-State Visually Evoked Potentials (SSVEPs) for robot control is
proposed as an innovative therapy for robot-based Attention Deficit Hyperactivity Disorder (ADHD) rehabilitation of children. The system manages to overcome the challenges
regarding immersivity and wearability, providing a direct path between human brain and social robots, already successfully employed for ADHD treatment. Through the proposed system, even without training, the user can drive a robot, in real-time, by brain signals. A preliminary evaluation of the children adherence to the therapy was conducted as a case study on 18 subjects, at an accredited rehabilitation center. After investigating the children acceptance of the proposed system, different tasks were assigned to the volunteers aiming to observe their level of involvement. The experimental activity showed encouraging results, where almost all the participants were satisfied with the
experience and keen to repeat it again in the future
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