N. Alberto Borghese

Calibrating a video camera pair with a rigid bar

Abstract In this paper a new procedure to determine all the geometrical parameters of a stereo-system is presented. It is based on surveying a rigid bar carrying two markers on its extremities moved inside the working volume and it does not require grids or complex calibration structures. The external parameters are estimated through the epipolar geometry up to a scale factor which is determined from the true length of the bar. The focal lengths are determined using the properties of the absolute conic in the projective space. The principal points are computed through a non-linear minimisation carried out through an evolutionary optimisation. The accuracy of the method is assessed on real data and it compares favourably with that obtained through classical approaches based on control points of known 3D coordinates.

Exergaming and rehabilitation: A methodology for the design of effective and safe therapeutic exergames☆

Abstract We present here a comprehensive definition of therapeutic exergames from which a methodology to create safe exergames for real therapy pathways is derived. Three main steps are identified. (I) A clear identification of all the exercise requirements, not only in terms of goals of the therapy, but also in terms of additional constraints. Characteristic parameters for determining the challenge level and to assess progression are also defined in this phase. (II) The exercise is transformed into a Virtual Exercise, in which all the exercise elements are implemented inside a simple virtual environment. In this step the discussion between clinical and ICT teams allows maximizing the effectiveness of exergames implementation. (III) The final exergame is realized by introducing on top of the exercise all the game elements suggested by good game design to maximize entertainment. A clear line between exercises and games is drawn here. We illustrate the methodology with exergames designed for (1) balance and posture and (2) neglect rehabilitation, implemented and tested with post-stroke patients training autonomously at home. The methodology can have a broader impact as it can be applied also in other gaming fields in which the requirements go beyond entertainment.

An intelligent game engine for the at-home rehabilitation of stroke patients

The recent availability of advanced video game interfaces (such as the Microsoft Kinect, the Nintendo WiiMote and Balance Board) is creating interesting opportunities to provide low-cost rehabilitation at-home for patients. In this context, video games are rising as promising tools to guide patients through their recovery experience and to increase their motivation throughout the rehabilitation path. However, to be applied to clinical scenarios, video games must be designed to adhere to the clinical requirements and to meet doctors/patients expectations. They also need to be integrated within multi-level platforms that can allow different levels of monitoring, e.g., at a personal level by the therapist, at the hospital level by the doctors, and at the regional level by the government agencies. In this paper, we overview an intelligent game engine for the at-home rehabilitation of stroke patients The engine provides several games that implement actual rehabilitation exercises and have been developed in strict collaboration with therapists. It is integrated in a patient station that provides several types of monitoring and feedback using virtual and/or human therapists.

Multiscale models for data processing: an experimental sensitivity analysis

Hierarchical radial basis functions (HRBFs) networks have been recently introduced as a tool for adaptive multiscale image reconstruction from range data. These are based on local operation on the data and are able to give a sparse approximation. In this paper, HRBFs are reframed for the regular sampling case, and they are compared with wavelet decomposition. Results show that HRBFs, thanks to their constructive approach to approximation, are much more tolerant on errors in the parameters when errors occur in the configuration phase.

Mesh refinement with color attributes

Abstract Although many sophisticated solutions have been proposed to reduce the geometry complexity of a 3D mesh, few of these take into account color and texture fields, which are fundamental for a visual appearance of high quality. We propose here an innovative solution which combines the concept of hierarchy in refining a mesh with color attributes with mesh regularization. It operates subdividing recursively the faces of a 3D mesh, which was previously simplified according to geometrical criteria only. New vertices are introduced by splitting a face until a maximum color error is achieved. The error is evaluated in CIE-L*u*v* iso-perceptual metric. Splitting is carried out using a new strategy, which chooses between either binary or quaternary subdivision schema, in order to achieve 3D mesh regularization. In this respect our method improves other hierarchical subdivision techniques (applied for example to radiosity and height fields). If the unsimplified version of the mesh is available, the procedure positions the new vertices on this mesh, decreasing, the topological error besides the color one. The method presented here is quite general and allows to create models of high pictorial quality (≈20,000 faces), which can be manipulated in real-time on machines with no hardware accelerator for texture mapping. Main strength of the method is locality, which allows very fast operations as well as the possibility of local refinements. Results show how, at the expense of few more polygons, the final appearance of the model can be greatly improved in a very short time. The produced models are suitable to progressive transmission: first a light mesh can be transmitted with its per-vertex colors, then, in a second stage, the position and color value of the vertices added in the refinement stage can be sent to improve the pictorial appearance.

Multi-scale Support Vector Regression

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.

Computing camera focal length by zooming a single point

In this paper we present a novel simple procedure to compute the focal length of a camera. The method is based on zooming in and out only a single point. The same approach allows computing the principal point when only two points are available on a pair of images surveyed with a different focal length. Experimental results show that the method is as accurate as classical full calibration methods. Moreover, its application to augmented reality produces more accurate results than those obtained when the simple pin-hole model is considered.

On the assessment of coordination between upper extremities: towards a common language between rehabilitation engineers, clinicians and neuroscientists

Well-developed coordination of the upper extremities is critical for function in everyday life. Interlimb coordination is an intuitive, yet subjective concept that refers to spatio-temporal relationships between kinematic, kinetic and physiological variables of two or more limbs executing a motor task with a common goal. While both the clinical and neuroscience communities agree on the relevance of assessing and quantifying interlimb coordination, rehabilitation engineers struggle to translate the knowledge and needs of clinicians and neuroscientists into technological devices for the impaired. The use of ambiguous definitions in the scientific literature, and lack of common agreement on what should be measured, present large barriers to advancements in this area. Here, we present the different definitions and approaches to assess and quantify interlimb coordination in the clinic, in motor control studies, and by state-of-the-art robotic devices. We then propose a taxonomy of interlimb activities and give recommendations for future neuroscience-based robotic- and sensor-based assessments of upper limb function that are applicable to the everyday clinical practice. We believe this is the first step towards our long-term goal of unifying different fields and help the generation of more consistent and effective tools for neurorehabilitation.

Interacting with an artificial partner: modeling the role of emotional aspects

In this paper we introduce a simple model based on probabilistic finite state automata to describe an emotional interaction between a robot and a human user, or between simulated agents. Based on the agent’s personality, attitude, and nature, and on the emotional inputs it receives, the model will determine the next emotional state displayed by the agent itself. The probabilistic and time-varying nature of the model yields rich and dynamic interactions, and an autonomous adaptation to the interlocutor. In addition, a reinforcement learning technique is applied to have one agent drive its partner’s behavior toward desired states. The model may also be used as a tool for behavior analysis, by extracting high probability patterns of interaction and by resorting to the ergodic properties of Markov chains.

Intelligent Game Engine for Rehabilitation (IGER)

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 patients 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 patients motion quality with a novel informative color coding applied to the patients 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 patients 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.