Fear is a multifaceted emotion, challenging to define and assess accurately. Biologically, it is an innate response to threats, whereas psychologically, it is shaped by individual experiences and societal influences, sometimes evolving into specific phobias. One such phobia, arachnophobia (the fear of spiders), is particularly widespread and can vary widely in intensity among individuals. This paper presents a prototypal system that classifies the severity of arachnophobia using Machine Learning (ML) algorithms within a Virtual Reality (VR) game-based environment. The proposed system utilizes a two-stage clustering approach to analyze the behavioral data collected during VR exposure. Additionally, participants' self-reported fear levels are measured using the Spider Phobia Questionnaire (SPQ), to provide a comprehensive assessment. Preliminary results suggest that this method could be effectively used to classify arachnophobia intensity level, thus offering potential applications in both clinical settings and video games.

In the realm of video games, achieving a state of flow is paramount for player engagement, striking a delicate balance between boredom and anxiety. Under such circumstances, monitoring player's stress level plays a crucial role. Here, by addressing state-space dynamics, we model stress unfolding over time, allowing for both its continuous and discrete assessment. This study builds upon our previous research, advancing stress assessment techniques to enhance tailored and immersive gaming experiences through Virtual Reality in horror games. We provide detailed insights into our collected data, which includes physiological measurements, motion behavioural data, and participants' self-reported stress levels. Additionally, we conduct an in-depth analysis on individual participants to delve deeper into the dynamics of stress experienced by each player. Results achieved give evidence that the measurement of motion behavioural data, exclusively collected from the headset, well compares to that based on electrodermal activity (EDA), more classically related to stress assessment. Data are made publicly available athttps://zenodo.org/records/15025199.

We introduce a novel approach for scalable domain adaptation in cloud robotics scenarios where robots rely on third–party AI inference services powered by large pre– trained deep neural networks. Our method is based on a downstream proposal–refinement stage running locally on the robots, exploiting a new lightweight DNN architecture, R2SNet. This architecture aims to mitigate performance degradation from domain shifts by adapting the object detection process to the target environment, focusing on relabeling, rescoring, and suppression of bounding–box proposals. Our method allows for local execution on robots, addressing the scalability challenges of domain adaptation without incurring significant computational costs. Real–world results on mobile service robots performing door detection show the effectiveness of the proposed method in achieving scalable domain adaptation.

In this work, we explored the advantages of dynamic game balancing using players’ affective state introducing StrEx, a plugin designed to modulate the stress level induced by a video game in an unobtrusive way. The proposed plugin is also intended to validate results obtained by [1]. Our system collects motion behavioral data and updates a model of the player’s stress level to guide the transitions of a Finite State Machine which regulates the stress level induced by the game through the generation of game content. We designed and developed a virtual reality horror-survival game, with the aim of testing the system functioning. Our approach shows promising potential to create more immersive and engaging video games exploiting affective-based adaptation.

The employment of Social Assistive Robots (SARs) for monitoring elderly users represents a valuable gateway for at-home assistance. Their deployment in the house of the users can provide effective opportunities for early detection of Mild Cognitive Impairment (MCI), a condition of increasing impact in our aging society, by means of digitalized cognitive tests. In this work, we present a system where a specific set of cognitive tests is selected, digitalized, and integrated with a robotic assistant, whose task is the guidance and supervision of the users during the completion of such tests. The system is then evaluated by means of an experimental study involving potential future users, in order to assess its acceptability and identify key directions for technical improvements.

The ageing of the population deeply impacts on the social costs relative to health care. The use of modern technologies is one of the most promising approaches, under current study, to reduce such impact. In this demonstration, we propose a framework that can be employed for at-home assessment of Mild Cognitive Impairment (MCI). It is composed by a set of digitalized cognitive tests, developed from their paper-and-pencil counterparts, and by a Virtual Caregiver, which oversees the test execution and provides instructions.

Computer games are a promising tool to support intensive rehabilitation. However, at present, they do not incorporate the supervision provided by a real therapist and do not allow safe and effective use at a patient's home. We show how specifically tailored computational intelligence based techniques allow extending exergames with functionalities that make rehabilitation at home effective and safe. The main function is in monitoring the correctness of motion, which is fundamental in avoiding developing wrong motion patterns, making rehabilitation more harmful than effective. Fuzzy systems enable us to capture the knowledge of the therapist and to provide real-time feedback of the patient's motion quality with a novel informative color coding applied to the patient's avatar. This feedback is complemented with a therapist avatar that, in extreme cases, explains the correct way to carry out the movements required by the exergames. The avatar also welcomes the patient and summarizes the therapy results to him/her. Text to speech and simple animation improve the engagement. Another important element is adaptation. Only the proper level of challenge exercises can be both effective and safe. For this reason exergames can be fully configured by therapists in terms of speed, range of motion, or accuracy. These parameters are then tuned during exercise to the patient's performance through a Bayesian framework that also takes into account input from the therapist. A log of all the interaction data is stored for clinicians to assess and tune the therapy, and to advise patients. All this functionality has been added to a classical game engine that is extended to embody a virtual therapist aimed at supervising the motion, which is the final goal of the exergames for rehabilitation. This approach can be of broad interest in the serious games domain. Preliminary results with patients and therapists suggest that the approach can maintain a proper challenge level while keeping the patient motivated, safe, and supervised.

Rehabilitation for stroke patients with postural instability or balance disorders can be enhanced using game-based rehabilitative strategies. Our work explored the potential of commercially available haptic devices for a home rehabilitation system. One group of participants used a low-cost device (Falcon) while another group used a high-end device (Omega). Both groups performed various tasks with the haptic device, with the eyes either closed or open. Results showed that participants interacted somewhat differently with the two devices, although the analysis of the center of pressure showed qualitatively similar balance performance for the two groups. Light touch task increased the stability, whereas the force tasks decreased it. This study showed that a low-cost haptic device can affect postural stability in a controllable way.

The existence of multiple solutions in clustering, and in hierarchical clustering in particular, is often ignored in practical applications. However, this is a non-trivial problem, as different data orderings can result in different cluster sets that, in turns, may lead to different interpretations of the same data. The method presented here offers a solution to this issue. It is based on the definition of an equivalence relation over dendrograms that allows developing all and only the significantly different dendrograms for the same dataset, thus reducing the computational complexity to polynomial from the exponential obtained when all possible dendrograms are considered. Experimental results in the neuroimaging and bioinformatics domains show the effectiveness of the proposed method.

Support vector regression (SVR) is based on a linear combination of displaced replicas of the same function, called a kernel. When the function to be approximated is nonstationary, the single kernel approach may be ineffective, as it is not able to follow the variations in the frequency content in the different regions of the input space. The hierarchical support vector regression (HSVR) model presented here aims to provide a good solution also in these cases. HSVR consists of a set of hierarchical layers, each containing a standard SVR with Gaussian kernel at a given scale. Decreasing the scale layer by layer, details are incorporated inside the regression function. HSVR has been widely applied to noisy synthetic and real datasets and it has shown the ability in denoising the original data, obtaining an effective multiscale reconstruction of better quality than that obtained by standard SVR. Results also compare favorably with multikernel approaches. Furthermore, tuning the SVR configuration parameters is strongly simplified in the HSVR model.

A multi-kernel Support Vector Machine model, called Hierarchical Support Vector Regression (HSVR), is proposed here. This is a self-organizing (by growing) multiscale version of a Support Vector Regression (SVR) model. It is constituted of hierarchical layers, each containing a standard SVR with Gaussian kernel, at decreasing scales. HSVR have been applied to a noisy synthetic dataset. The results illustrate their power in denoising the original data, obtaining an effective multiscale reconstruction of better quality than that obtained by standard SVR. Furthermore with this approach the well known problem of tuning the SVR parameters is strongly simplified.

In this paper, a novel real-time online network model is presented. It is derived from the hierarchical radial basis function (HRBF) model and it grows by automatically adding units at smaller scales, where the surface details are located, while data points are being collected. Real-time operation is achieved by exploiting the quasi-local nature of the Gaussian units: through the definition of a quad-tree structure to support their receptive field local network reconfiguration can be obtained. The model has been applied to 3-D scanning, where an updated real-time display of the manifold to the operator is fundamental to drive the acquisition procedure itself. Quantitative results are reported, which show that the accuracy achieved is comparable to that of two batch approaches: batch HRBF and support vector machines (SVMs). However, these two approaches are not suitable to real-time online learning. Moreover, proof of convergence is also given.

A new filter to restore radiographic images corrupted by impulsive noise is proposed. It is based on a switching scheme where all the pulses are first detected and then corrected through a median filter. The pulse detector is based on the hypothesis that the major contribution to image noise is given by the photon counting process, with some pixels corrupted by impulsive noise. Such statistics is described by an adequate mixture model. The filter is also able to reliably estimate the sensor gain. Its operation has been verified on both synthetic and real images; the experimental results demonstrate the superiority of the proposed approach in comparison with more traditional methods.

An online procedure for configuring the parameters of a hierarchical radial basis functions (HRBF) network is presented here. The proposed procedure has been implemented and applied to a problem of real-time surface reconstruction. Results show that the algorithm trained online well compares with the batch version.

Modern scanners are able to deliver huge quantities of three-dimensional (3-D) data points sampled on an object's surface, in a short time. These data have to be filtered and their cardinality reduced to come up with a mesh manageable at interactive rates. We introduce here a novel procedure to accomplish these two tasks, which is based on an optimized version of soft vector quantization (VQ). The resulting technique has been termed enhanced vector quantization (EVQ) since it introduces several improvements with respect to the classical soft VQ approaches. These are based on computationally expensive iterative optimization; local computation is introduced here, by means of an adequate partitioning of the data space called hyperbox (HB), to reduce the computational time so as to be linear in the number of data points N, saving more than 80% of time in real applications. Moreover, the algorithm can be fully parallelized, thus leading to an implementation that is sublinear in N. The voxel side and the other parameters are automatically determined from data distribution on the basis of the Zador's criterion. This makes the algorithm completely automatic. Because the only parameter to be specified is the compression rate, the procedure is suitable even for nontrained users. Results obtained in reconstructing faces of both humans and puppets as well as artifacts from point clouds publicly available on the web are reported and discussed, in comparison with other methods available in the literature. EVQ has been conceived as a general procedure, suited for VQ applications with large data sets whose data space has relatively low dimensionality.

We present a new algorithm, called the soft-tissue filter, that can make both soft and bone tissue clearly visible in digital cephalic radiographies under a wide range of exposures. It uses a mixture model made up of two Gaussian distributions and one inverted lognormal distribution to analyze the image histogram. The image is clustered in three parts: background, soft tissue, and bone using this model. Improvement in the visibility of both structures is achieved through a local transformation based on gamma correction, stretching, and saturation, which is applied using different parameters for bone and soft-tissue pixels. A processing time of 1 s for 5 Mpixel images allows the filter to operate in real time. Although the default value of the filter parameters is adequate for most images, real-time operation allows adjustment to recover under- and overexposed images or to obtain the best quality subjectively. The filter was extensively clinically tested: quantitative and qualitative results are reported here.

An approximating neural model, called hierarchical radial basis function (HRBF) network, is presented here. This is a self-organizing (by growing) multiscale version of a radial basis function (RBF) network. It is constituted of hierarchical layers, each containing a Gaussian grid at a decreasing scale. The grids are not completely filled, but units are inserted only where the local error is over threshold. This guarantees a uniform residual error and the allocation of more units with smaller scales where the data contain higher frequencies. Only local operations, which do not require any iteration on the data, are required; this allows to construct the network in quasi-real time. Through harmonic analysis, it is demonstrated that, although a HRBF cannot be reduced to a traditional wavelet-based multiresolution analysis (MRA), it does employ Riesz bases and enjoys asymptotic approximation properties for a very large class of functions. HRBF networks have been extensively applied to the reconstruction of three-dimensional (3-D) models from noisy range data. The results illustrate their power in denoising the original data, obtaining an effective multiscale reconstruction of better quality than that obtained by MRA.

A procedure for real-time 3D meshing reconstruction from sparse data is presented. The approach is based on hierarchical radial basis functions networks, which allow for an effective reconstruction of multi-scale surfaces. This model is extended to provide not only the continuous surface description, but also real-time operation. To this purpose, the HRBF network differential properties have been exploited to produce a denser mesh in regions where geometry is more detailed.

Calibration is a crucial step to obtaining 3D measurement using video camera-based stereo systems. All the parameters can be determined except for the pair of principal points, which poses a considerable drawback. Whereas in low-accuracy systems such points can be assumed to lie at the image center without degrading the overall 3D accuracy; in high-accuracy systems their true position must be computed accurately. In this case, all the calibration parameters can still be estimated through epipolar geometry, but it is necessary to minimize a highly nonlinear cost function. It is shown here that by combining two evolutionary optimization strategies this minimization can be carried out, both efficiently and reliably. The resulting strategy, which we call enhanced evolutionary search (EES), allows the full calibration of a stereo system using only a rigid bar. Moreover, EES can be applied to a wide range of applications where the cost function contains complex nonlinear relationships among the optimization variables.

In this paper a method to accelerate soft vector quantisation (VQ), making it a quasi-real time procedure, is described. Through the local analysis of the data density a criterion to set a reasonable value of the parameters and to initialise the position of the reference vectors (hyper-box preprocessing), allows to cut about 75% of the iterations and to make the computational cost of each iteration constant, independent of the number of sampled points. Moreover, it makes soft VQ of possible implementation on parallel machines. Overall the processing time with hyper-box pre-processing can be brought down to 3%. This method, in conjunction with Delaunay tessellation, has been extensively applied to the construction of 3D triangular meshes from dense noisy data. Results on the reconstruction of 3D models of human faces are reported and discussed.