Measurements by A LEAP-Based Virtual Glove for the hand rehabilitation

Hand rehabilitation is fundamental after stroke or surgery. Traditional rehabilitation requires a therapist and implies high costs, stress for the patient, and subjective evaluation of the therapy effectiveness. Alternative approaches, based on mechanical and tracking-based gloves, can be really effective when used in virtual reality (VR) environments. Mechanical devices are often expensive, cumbersome, patient specific and hand specific, while tracking-based devices are not affected by these limitations but, especially if based on a single tracking sensor, could suffer from occlusions. In this paper, the implementation of a multi-sensors approach, the Virtual Glove (VG), based on the simultaneous use of two orthogonal LEAP motion controllers, is described. The VG is calibrated and static positioning measurements are compared with those collected with an accurate spatial positioning system. The positioning error is lower than 6 mm in a cylindrical region of interest of radius 10 cm and height 21 cm. Real-time hand tracking measurements are also performed, analysed and reported. Hand tracking measurements show that VG operated in real-time (60 fps), reduced occlusions, and managed two LEAP sensors correctly, without any temporal and spatial discontinuity when skipping from one sensor to the other. A video demonstrating the good performance of VG is also collected and presented in the Supplementary Materials. Results are promising but further work must be done to allow the calculation of the forces exerted by each finger when constrained by mechanical tools (e.g., peg-boards) and for reducing occlusions when grasping these tools. Although the VG is proposed for rehabilitation purposes, it could also be used for tele-operation of tools and robots, and for other VR applications

A Light CNN for detecting COVID-19 from CT scans of the chest

OVID-19 is a world-wide disease that has been declared as a pandemic by the World Health Organization. Computer Tomography (CT) imaging of the chest seems to be a valid diagnosis tool to detect COVID-19 promptly and to control the spread of the disease. Deep Learning has been extensively used in medical imaging and convolutional neural networks (CNNs) have been also used for classification of CT images. We propose a light CNN design based on the model of the SqueezeNet, for the efficient discrimination of COVID-19 CT images with other CT images (community-acquired pneumonia and/or healthy images). On the tested datasets, the proposed modified SqueezeNet CNN achieved 83.00\% of accuracy, 85.00\% of sensitivity, 81.00\% of specificity, 81.73\% of precision and 0.8333 of F1Score in a very efficient way (7.81 seconds medium-end laptot without GPU acceleration). Besides performance, the average classification time is very competitive with respect to more complex CNN designs, thus allowing its usability also on medium power computers. In the next future we aim at improving the performances of the method along two directions: 1) by increasing the training dataset (as soon as other CT images will be available); 2) by introducing an efficient pre-processing strategy

A Virtual Glove System for the Hand Rehabilitation based on Two Orthogonal LEAP Motion Controllers

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Chemosensory Event-Related Potentials and Power Spectrum could be A Possible Biomarker in 3M Syndrome Infants?

none10no3M syndrome is a rare disorder that involves the gene cullin-7 (CUL7). CUL7 modulates odour detection, conditions the olfactory response (OR) and plays a role in the development of the olfactory system. Despite this involvement, there are no direct studies on olfactory functional effects in 3M syndrome. The purpose of the present work was to analyse the cortical OR through chemosensory event-related potentials (CSERPs) and power spectra calculated by electroencephalogram (EEG) signals recorded in 3M infants: two twins (3M-N) and an additional subject (3M-O). The results suggest that olfactory processing is diversified. Comparison of N1 and Late Positive Component (LPC) indicated substantial differences in 3M syndrome that may be a consequence of a modified olfactory processing pattern. Moreover, the presence of delta rhythms in 3M-O and 3M-N clearly indicates their involvement with OR, since the delta rhythm is closely connected to chemosensory perception, in particular to olfactory perception.openInvitto, Sara; Grasso, Alberto; Lofrumento, Dario Domenico; Ciccarese, Vincenzo; Paladini, Angela; Paladini, Pasquale; Marulli, Raffaella; Pascalis, Vilfredo De; Polsinelli, Matteo; Placidi, GiuseppeInvitto, Sara; Grasso, Alberto; Lofrumento, Dario Domenico; Ciccarese, Vincenzo; Paladini, Angela; Paladini, Pasquale; Marulli, Raffaella; Pascalis, Vilfredo De; Polsinelli, Matteo; Placidi, Giusepp

Data integration by two-sensors in a LEAP-based Virtual Glove for human-system interaction

Virtual Glove (VG) is a low-cost computer vision system that utilizes two orthogonal LEAP motion sensors to provide detailed 4D hand tracking in real-time. VG can find many applications in the field of human-system interaction, such as remote control of machines or tele-rehabilitation. An innovative and efficient data-integration strategy, based on the velocity calculation, for selecting data from one of the LEAPs at each time, is proposed for VG. The position of each joint of the hand model, when obscured to a LEAP, is guessed and tends to flicker. Since VG uses two LEAP sensors, two spatial representations are available each moment for each joint: the method consists of the selection of the one with the lower velocity at each time instant. Choosing the smoother trajectory leads to VG stabilization and precision optimization, reduces occlusions (parts of the hand or handling objects obscuring other hand parts) and/or, when both sensors are seeing the same joint, reduces the number of outliers produced by hardware instabilities. The strategy is experimentally evaluated, in terms of reduction of outliers with respect to a previously used data selection strategy on VG, and results are reported and discussed. In the future, an objective test set has to be imagined, designed, and realized, also with the help of an external precise positioning equipment, to allow also quantitative and objective evaluation of the gain in precision and, maybe, of the intrinsic limitations of the proposed strategy. Moreover, advanced Artificial Intelligence-based (AI-based) real-time data integration strategies, specific for VG, will be designed and tested on the resulting dataset. (c) 2021, The Author(s)

Fertility specialists’ views, behavior, and attitudes towards the use of endometrial scratching in Italy

Background: Endometrial scratching (ES) or injury is intentional damage to the endometrium performed to improve reproductive outcomes for infertile women desiring pregnancy. Moreover, recent systematic reviews with meta-analyses and randomized controlled trials demonstrated that ES is not effective, data on the safety are limited, and it should not be recommended in clinical practice. The aim of the current study was to assess the view and behavior towards ES among fertility specialists throughout infertility centers in Italy, and the relationship between these views and the attitudes towards the use of ES as an add-on in their commercial setting. Methods: Online survey among infertility centers, affiliated to Italian Society of Human Reproduction (SIRU), was performed using a detailed questionnaire including 45 questions with the possibility to give "closed" multi-choice answers for 41 items and "open" answers for 4 items. Online data from the websites of the infertility centers resulting in affiliation with the specialists were also recorded and analyzed. The quality of information about ES given on infertility centers websites was assessed using a scoring matrix including 10 specific questions (scored from 0 to 2 points), and the possible scores ranged from 0 to 13 points ('excellent' if the score was 9 points or more, 'moderate' if the score was between 5 and 8, and 'poor' if it was 4 points or less). Results: The response rate was of 60.6% (43 questionnaires / 71 infertility SIRU-affiliated centers). All included questionnaires were completed in their entirety. Most physicians (~ 70%) reported to offer ES to less than 10% of their patients. The procedure is mainly performed in the secretory phase (69.2%) using pipelle (61.5%), and usually in medical ambulatory (56.4%) before IVF cycles to improve implantation (71.8%) without drugs administration (e.g., pain drugs, antibiotics, anti-hemorrhagics, or others) before (76.8%) or after (64.1%) the procedure. Only a little proportion of infertility centers included in the analysis proposes formally the ES as an add-on procedure (9.3%), even if, when proposed, the full description of the indications, efficacy, safety, and costs is never addressed. However, the overall information quality of the websites was generally "poor" ranging from 3 to 8 and having a low total score (4.7 ± 1.6; mean ± standard deviation). Conclusions: In Italy, ES is a procedure still performed among fertility specialists for improving the implantation rate in IVF patients. Moreover, they have a poor attitude in proposing ES as an add-on in the commercial setting

Forces calculation module for the leap-based virtual glove

Hand rehabilitation is fundamental after stroke or surgery. Traditional rehabilitation implies high costs, stress for the patient, and subjective evaluation of the therapy effectiveness. Mechanical devices based approaches are often expensive, cumbersome and patient specific, while tracking-based devices are not affected by these limitations, though they could suffer from occlusions. In recent works, the procedure used for implementing a multi-sensors approach, the Virtual Glove (VG), based on the simultaneous use of two orthogonal LEAP motion controllers, was described. In this paper, an engineered version of VG was calibrated and measurements were performed. This article presents a model extension to be used for the off-line calculation of the hand kinematics and of the flexion/extension forces exerted by each finger when constrained by calibrated elastic tools

BCI driven by self-induced emotions: a multi-class study

Brain Computer Interfaces (BCIs) use measurements of the voluntary brain activity for driving a communication system, by means of the activation of mental tasks. In recent literature, a novel activation paradigm, based on the self-induction of emotions, has been proposed and some classification strategies for self-induced emotions have been designed, together with a modular framework for the implementation of binary BCIs. We extended the BCI system, to manage the multi-class scenario, in order to increase the number of recognizable commands, thus improving the efficacy of the communication. The objective was to provide a correction function that would allow the increase of the accuracy, without the overhead of a verification method. A poll oriented classification algorithm was used in conjunction with a matrix based graphic interface to allow the user to communicate through three self-induced emotional states: the disgust produced by remembering a bad odor, the good sensation produced by remembering the odor of a good fragrance and a relaxing state. The proposed system was tested on a healthy subject. Preliminary results were reported and discussed