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In the context of three-dimensional acquisition and elaboration, it is essential to maintain a balanced approach between model accuracy and required resources. As a possible solution to this problem, the present paper proposes a method to obtain accurate and lightweight meshes of a real environment using the Microsoft® HoloLens™ as a device for point clouds acquisition. Firstly, we describe an empirical procedure to improve 3D models, with the use of optimal parameters found by means of a genetic algorithm. Then, a systematic review of the indexes for evaluating the quality of meshes is developed, in order to quantify and compare the goodness of the obtained outputs. Finally, in order to check the quality of the proposed approach, a reconstructed model is tested in a virtual scenario implemented in Unity® 3D Game Engine.

Assessment and rating of Parkinson’s Disease (PD) are commonly based on the medical observation of several clinical manifestations, including the analysis of motor activities. In particular, medical specialists refer to the Movement Disorder Society – sponsored revision of Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), the most widely used scale for rating PD. The UPDRS scale also considers the observation of some subtle motor phenomena that are either difficult to capture with human eyes or subjectively considerate abnormal. In this scenario, an automatic system able to capture the considered motor exercises and rate the PD severity could be used as a support system for the healthcare sector. In this work, we implemented a simple and low-cost clinical tool that can extract motor features of two main exercises required by the UPDRS scale (the finger tapping and the foot tapping) to classify and rate the PD severity. Sixty two participants were enrolled for the purpose of the present study: thirty three PD patients and twenty nine healthy paired subjects. Results showed that an SVM using the features extracted by both considered exercises was able to classify healthy subjects and PD patients with great performances by reaching 87.1% of accuracy. The results of the classification between mild and moderate PD patients indicated that the foot tapping features were the most representative ones to discriminate (81.0% of accuracy). We can conclude that developed tool can support medical specialists in the assessment and rating of PD patients in a real clinical scenario.

Blepharospasm is characterized by bilateral, synchronous, and symmetric involuntary orbicularis oculi muscle spasms leading to partial/total eyelid closure. We proposed a comprehensive method for assessing the severity of blepharospasm cases, based on expert observation of fixed-length video recordings and natural feature detection algorithms. The developed software detects involuntary spasms and blinks in order to evaluate BSP severity. In this work we have considered a new BSP severity scale to realize an objective evaluation of BSP severity.

In this paper, we propose a deep learning approach for breast lesions classification, by processing breast images obtained using an innovative acquisition system, the Tomosynthesis, a medical instrument able to acquire high-resolution images using a lower radiographic dose than normal Computed Tomography (CT). The acquired images were processed to obtain Regions Of Interest (ROIs) containing lesions of different categories. Subsequently, several pre-trained Convolutional Neural Network (CNN) models were evaluated as feature extractors and coupled with non-neural classifiers for discriminate among the different categories of lesions. Results showed that the use of CNNs as feature extractor and the subsequent classification using a non-neural classifier reaches high values of Accuracy, Sensitivity and Specificity.

Keratoconus is a bilateral progressive corneal disease characterized by thinning and apical protrusion; its early diagnosis is fundamental since it allows one to treat this rare disease by cross-linking approach, thus preventing a major corneal deformation and avoiding more invasive and risky surgical therapies, such as cornea transplant. Ophthalmology improvements have allowed a more rapid, precise and painless acquisition of corneal biometric parameters which are useful to evaluate alterations and abnormalities of eye’s outer structure. This paper presents a study about Keratoconus diagnosis based on a machine learning approach using corneal physical and morphological parameters obtained through Precisio™ tomographic examination. Artificial Neural Networks (ANNs) have been used for classification; in particular, a mono-objective Genetic Algorithm has been used to obtain the best topology for the neural classifiers for different input datasets obtained from features ranking. High levels of accuracy (higher than 90%) have been reached for all types of classification; in particular, binary classification has showed the best discrimination capability for Keratoconus identification.

The assessment of tremor features of subjects affected by Parkinson’s disease supports physicians in defining customized rehabilitation treatment which, in turn, can lead to better clinical outcome. In the standard assessment protocol patient performed many exercises that are useful to physicians to rate disease. But the rating is subjective since is based on an observational evaluation. In this paper, we introduce a novel method for achieving objective assessment of movement conditions by directly measuring the magnitude of involuntary tremors with a set of sensors. We focused on one of the standard tasks of the Unified Parkinson’s Disease Rating Scale: finger-to-nose maneuver. During the task, data related to patient finger position are stored and then some tremor’s features are extracted. Finally, we employ a Support Vector Machine to measure the relevance of the extracted features in classify healthy subjects and patients.

The assessment of gait features of subjects affected by Multiple Sclerosis supports physicians in defining customized rehabilitation treatment which, in turn, can lead to better clinical outcome. In the standard assessment protocol, an optoelectronic motion system, surface electromyography sensors, and a set of piezoelectric sensors on a force platform acquire large amount of data which is evaluated by physicians for defining treatment. In this paper, we introduce an automatic procedure based on Fuzzy-Granular Computing for evaluating gait metrics: three features extracted from each muscle involved in gait enable to summarize, quantify, and simplify the assessment protocol. Finally, we employ a Support Vector Machine to measure the relevance of the extracted features in classifying healthy subjects and patients using the simplified set of features.

The purpose of this work is to investigate the olfactory response to a neuter and a smell stimulation through Olfactory Event Related Potentials (OERP). We arranged an experiment of olfactory stimulation by analyzing Event Related Potential during perception of 2 odor stimuli: pleasant (Rose, 2-phenyl ethanol C2H4O2) and neuter (Neuter, Vaseline Oil CH2). We recruited 15 adult safe non-smokers volunteers. In order to record OERP, we used VOS EEG, a new device dedicated to odorous stimulation in EEG. After the OERP task, the subject filled a visual analogic scale, regarding the administered smell, on three dimensions: pleasantness (P), arousing (A) and familiarity (F). We performed an artificial neural network analysis that highlighted three groups of significant features, one for each amplitude component. Three neural network classifiers were evaluated in terms of accuracy on both full and restricted datasets, showing the best performance with the latter. The improvement of the accuracy rate in all VAS classifications was: 13.93% (A), 64.81% (F), 9.8% (P) for P300 amplitude (Fz); 16.28% (A), 49.46% (F), 24% (P) for N400 amplitude (Cz, Fz, O2, P8); 110.42% (A), 21.19% (F), 24.1% (P) for N600 amplitude (Cz, Fz). Main results suggested that in smell presentation we can observe the involvement of slow Event-Related-Potentials, like N400 and N600, ERP involved in stimulus encoding.

In this paper, we propose a forearm rehabilitation system based on a serious game in Augmented Reality (AR). We designed and developed a simplified AR arcade brick breaking game to induce rehabilitation of the forearm muscles. We record the electromyographic signals using a low cost device to evaluate the applied force. We collected and analysed data in order to find a relationship between the applied force and the difficulty of the game. This research focuses on the de-hospitalization of subjects in the middle or final stages of their rehabilitation where the new technologies, like Virtual and Augmented Reality, may improve the experience of repetitive exercises.

We present the design and a prototype of a projective AR workbench for an effective use of the AR in industrial applications, in particular for Manual Working Stations. The proposed solution consists of an aluminum structure that holds a projector and a camera that is intended to be mounted on manual working stations. The camera, using a tracking algorithm, computes in real time the position and orientation of the object while the projector displays the information always in the desired position. We also designed and implemented the data structure of a database for the managing of AR instructions, and we were able to access this information interactively from our application.

Influenza vaccination has been implicated in Guillain Barre Syndrome (GBS) although the evidence for this link is controversial. A case–control study was conducted between October 2010 and May 2011 in seven Italian Regions to explore the relation between influenza vaccination and GBS. The study included 176 GBS incident cases aged ≥18 years from 86 neurological centers. Controls were selected among patients admitted for acute conditions to the Emergency Department of the same hospital as cases. Each control was matched to a case by sex, age, Region and admission date. Two different analyses were conducted: a matched case–control analysis and a self-controlled case series analysis (SCCS). Case–control analysis included 140 cases matched to 308 controls. The adjusted matched odds ratio (OR) for GBS occurrence within 6 weeks after influenza vaccination was 3.8 (95 % CI: 1.3, 10.5). A much stronger association with gastrointestinal infections (OR = 23.8; 95 % CI 7.3, 77.6) and influenza-like illness or upper respiratory tract infections (OR = 11.5; 95 % CI 5.6, 23.5) was highlighted. The SCCS analysis included all 176 GBS cases. Influenza vaccination was associated with GBS, with a relative risk of 2.1 (95 % CI 1.1, 3.9). According to these results the attributable risk in adults ranges from two to five GBS cases per 1,000,000 vaccinations.

In this paper, we present an innovative framework useful for clustering patients affected by a mild cognitive impairment and designed to improve the living environment and the lifestyle of patients in order to delay their cognitive state decline. The cognitive state changes are evaluated by means the event - related potentials elicited by environmental stimuli administered in several Virtual Reality scenarios. In particular, we formerly describe our innovative Virtual Reality environment, the protocol of stimuli administration, the procedure to measure the P300 latency in the response signal and finally the Self Organizing Map used to cluster the data. This research finds application in the fields of re-qualification of the environments for patients and healthcare introducing a new method for evaluation of best living conditions through VR.