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 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)

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