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We present the development of a novel smart ink pen instrumented with force and motion sensors designed for the quantitative and ecological assessment of daily-life handwriting. This work aims at testing the pen’s sensors and algorithms and the use of the smartpen to detect age-related changes in writing and tremor parameters during daily-life handwriting. A comparison against reference instruments was carried out to validate the pen tip force during static and dynamic conditions, the pen’s tilt angle, and the algorithm for the segmentation of the force signal into strokes. The smartpen was tested on 43 healthy adults divided into three age groups (young, middle-old, and old) during unconstrained handwriting on paper. The validation of the pen’s sensors and algorithms reported excellent results. A solid test–retest reliability was found in the writing and tremor indicators extracted from both young and old adults. As for the age-related analysis, the older age groups were characterized by an increase of temporal writing measures, a more uniform writing pressure, and more repetitive and predictable tremor oscillation components. The greatest accomplishment of our smart ink pen is the ability to combine the advantages of the digitizing tablet technology with the natural “feel,” the ease of use, and the ecological validity of the traditional pen-and-paper approach. The proposed solution represents an important step toward a simple, ecologically valid, reliable quantitative assessment of daily-life handwriting. Our smartpen is particularly suitable for daily-life telemonitoring applications in a number of important health-related fields. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel procedure for the on-the-field autocalibration of triaxial micro accelerometers, which requires neither any equipment nor a controlled environment and allows increasing the accuracy of this kind of microsensor. The procedure exploits the fact that, in static conditions, the modulus of the accelerometer output vector matches that of the gravity acceleration. The calibration model incorporates the bias and scale factor for each axis and the cross-axis symmetrical factors. The parameters are computed through nonlinear optimization, which is solved in a very short time. The calibration procedure was quantitatively tested by comparing the orientation produced by MEMS with that measured by a motion capture system. Results show that the MEMS output, after the calibration procedure, is far more accurate with respect to the output obtained using factory calibration data and almost one order of magnitude more accurate with respect to using traditional calibration models. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A procedure for the real-time construction of three-dimensional (3-D) multiscale meshes from not evenly sampled 3D points is described and discussed in this paper. The process is based on the connectionist model named hierarchical radial basis functions network (RRBF), which has been proved effective in the re-construction of smooth surfaces from sparse noisy data points. The network goal is to achieve a uniform reconstruction error, equal to measurement error, by stacking noncomplete grids of Gaussians at decreasing scales. It is shown here how the HRBF properties can be used to develop a configuration algorithm, which produces a continuous surface in real time. In addition, the model is extended to automatically convert the continuous surface into a 3-D mesh according to an adequate error measure. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Presents a study which reframed the hierarchical radial basis functions (HRBF) configuration procedure for regular sampled data in signal processing. Methodological background; Theoretical and experimental comparison of multiresolution analysis theory and HRBF; Results and conclusions.

Provides information on a study which presented a portable and flexible system for three-dimensional scanning. Methodology of the study; Results and discussion on the study; Conclusion.