Danilo Avola

Design of an efficient framework for fast prototyping of customized human-computer interfaces and virtual environments for rehabilitation

Rehabilitation is often required after stroke, surgery, or degenerative diseases. It has to be specific for each patient and can be easily calibrated if assisted by human-computer interfaces and virtual reality. Recognition and tracking of different human body landmarks represent the basic features for the design of the next generation of human-computer interfaces. The most advanced systems for capturing human gestures are focused on vision-based techniques which, on the one hand, may require compromises from real-time and spatial precision and, on the other hand, ensure natural interaction experience. The integration of vision-based interfaces with thematic virtual environments encourages the development of novel applications and services regarding rehabilitation activities. The algorithmic processes involved during gesture recognition activity, as well as the characteristics of the virtual environments, can be developed with different levels of accuracy. This paper describes the architectural aspects of a framework supporting real-time vision-based gesture recognition and virtual environments for fast prototyping of customized exercises for rehabilitation purposes. The goal is to provide the therapist with a tool for fast implementation and modification of specific rehabilitation exercises for specific patients, during functional recovery. Pilot examples of designed applications and preliminary system evaluation are reported and discussed.

Overall design and implementation of the virtual glove

Post-stroke patients and people suffering from hand diseases often need rehabilitation therapy. The recovery of original skills, when possible, is closely related to the frequency, quality, and duration of rehabilitative therapy. Rehabilitation gloves are tools used both to facilitate rehabilitation and to control improvements by an evaluation system. Mechanical gloves have high cost, are often cumbersome, are not re-usable and, hence, not usable with the healthy hand to collect patient-specific hand mobility information to which rehabilitation should tend. The approach we propose is the virtual glove, a system that, unlike tools based on mechanical haptic interfaces, uses a set of video cameras surrounding the patient hand to collect a set of synchronized videos used to track hand movements. The hand tracking is associated with a numerical hand model that is used to calculate physical, geometrical and mechanical parameters, and to implement some boundary constraints such as joint dimensions, shape, joint angles, and so on. Besides being accurate, the proposed system is aimed to be low cost, not bulky (touch-less), easy to use, and re-usable. Previous works described the virtual glove general concepts, the hand model, and its characterization including system calibration strategy. The present paper provides the virtual glove overall design, both in real-time and in off-line modalities. In particular, the real-time modality is described and implemented and a marker-based hand tracking algorithm, including a marker positioning, coloring, labeling, detection and classification strategy, is presented for the off-line modality. Moreover, model based hand tracking experimental measurements are reported, discussed and compared with the corresponding poses of the real hand. An error estimation strategy is also presented and used for the collected measurements. System limitations and future work for system improvement are also discussed.

Basis for the implementation of an EEG-based single-trial binary brain computer interface through the disgust produced by remembering unpleasant odors

In order to implement an EEG-based brain computer interface (BCI), a very large number of strategies (ranging from sensory-motor, p300, auditory based, visually based) can be used. However, no technique exists which is based on the olfactory stimulation or, better, based on the imagination of olfactory stimuli.The present paper describes an innovative paradigm, that is the voluntary brain activation with the disgust produced by remembering unpleasant odors, and a simple and robust classification method on which a single trial binary BCI can be implemented. In order to classify the signal, mainly the channels P4, C4, T8 and P8 have been used, by spanning the frequency band between 32 and 42Hz, that is a subset of the gamma band external to the bands usually occupied by other tasks (the interval between 1 and 30Hz), and the alpha band between 8 and 12Hz.Right hemisphere of the brain and gamma band of frequencies are particularly sensitive when experiencing negative emotions, such as the disgust produced by smelling or remembering unpleasant odors, while the alpha band is usually modified with concentration. This constitutes an advantage for the proposed classification technique because it is made intrinsically easy by the localization into particular positions and frequencies: different features are mostly based on different frequency bands.The choice of disgust produced by remembering unpleasant odors is twofold: smelling is an ancestral sensation which is so strong that its EEG signal is produced also in persons affected by hyposmia when they imagine an olfactory situation; it can be used without external stimulation, that is the user can decide freely when and if activate it.The proposed method and the experimental setup are described and a series of experimental measurements are presented and discussed. The accuracy of the proposed method is also evaluated and the reached levels are about 90%. The proposed system can be a useful communication alternative for disabled people that cannot use other BCI paradigms.

A low-cost real time virtual system for postural stability assessment at home

BACKGROUND AND OBJECTIVE The degeneration of the balance control system in the elderly and in many pathologies requires measuring the equilibrium conditions very often. In clinical practice, equilibrium control is commonly evaluated by using a force platform (stabilometric platform) in a clinical environment. In this paper, we demonstrate how a simple movement analysis system, based on a 3D video camera and a 3D real time model reconstruction of the human body, can be used to collect information usually recorded by a physical stabilometric platform. METHODS The algorithm used to reconstruct the human body model as a set of spheres is described and discussed. Moreover, experimental measurements and comparisons with data collected by a physical stabilometric platform are also reported. The measurements were collected on a set of 6 healthy subjects to whom a change in equilibrium condition was stimulated by performing an equilibrium task. RESULTS The experimental results showed that more than 95% of data collected by the proposed method were not significantly different from those collected by the classic platform, thus confirming the usefulness of the proposed system. CONCLUSIONS The proposed virtual balance assessment system can be implemented at low cost (about 500

A novel semi-immersive virtual reality visuo-motor task activates ventrolateral prefrontal cortex: a functional near-infrared spectroscopy study.

) and, for this reason, can be considered a home use medical device. On the contrary, astabilometric platform has a cost of about 10,000

A virtual ball task driven by forearm movements for neuro-rehabilitation

and requires periodical calibration. The proposed system does not require periodical calibration, as is necessary for stabilometric force platforms, and it is easy to use. In future, the proposed system with little integration can be used, besides being an emulator of a stabilometric platform, also to recognize and track, in real time, head, legs, arms and trunk, that is to collect information actually obtained by sophisticated optoelectronic systems.

A keypoint-based method for background modeling and foreground detection using a PTZ camera

OBJECTIVE In the last few years, the interest in applying virtual reality systems for neurorehabilitation is increasing. Their compatibility with neuroimaging techniques, such as functional near-infrared spectroscopy (fNIRS), allows for the investigation of brain reorganization with multimodal stimulation and real-time control of the changes occurring in brain activity. The present study was aimed at testing a novel semi-immersive visuo-motor task (VMT), which has the features of being adopted in the field of neurorehabilitation of the upper limb motor function. APPROACH A virtual environment was simulated through a three-dimensional hand-sensing device (the LEAP Motion Controller), and the concomitant VMT-related prefrontal cortex (PFC) response was monitored non-invasively by fNIRS. Upon the VMT, performed at three different levels of difficulty, it was hypothesized that the PFC would be activated with an expected greater level of activation in the ventrolateral PFC (VLPFC), given its involvement in the motor action planning and in the allocation of the attentional resources to generate goals from current contexts. Twenty-one subjects were asked to move their right hand/forearm with the purpose of guiding a virtual sphere over a virtual path. A twenty-channel fNIRS system was employed for measuring changes in PFC oxygenated-deoxygenated hemoglobin (O2Hb/HHb, respectively). MAIN RESULTS A VLPFC O2Hb increase and a concomitant HHb decrease were observed during the VMT performance, without any difference in relation to the task difficulty. SIGNIFICANCE The present study has revealed a particular involvement of the VLPFC in the execution of the novel proposed semi-immersive VMT adoptable in the neurorehabilitation field.

Adaptive Sampling and Reconstruction for Sparse Magnetic Resonance Imaging

The present study was aimed at describing a semi-immersive virtual reality environment, driven by a 3D hand sensing device (LEAP Motion Controller), to define a virtual task based on a virtual ball moving on a virtual path. The prefrontal cortex haemodynamic responses during the execution of this demanding task were evaluated by a 16-channel functional near-infrared spectroscopy (fNIRS) system. A bilateral ventrolateral prefrontal cortex activation was found during the virtual task. Although the proposed task has not been yet applied in the neuro-rehabilitation field, it has the potential to be adopted in the upper limb functional assessment and rehabilitation treatment.

SketchSPORE: A Sketch Based Domain Separation and Recognition System for Interactive Interfaces

Background modeling and foreground detection for PTZ cameras.Grid strategy for spatio-temporal tracking of keypoints.Background initialization and estimation.Background updating and reconstruction.Panoramic background reconstruction. Display Omitted The automatic scene analysis is still a topic of great interest in computer vision due to the growing possibilities provided by the increasingly sophisticated optical cameras. The background modeling, including its initialization and its updating, is a crucial aspect that can play a main role in a wide range of application domains, such as vehicle tracking, person re-identification and object recognition. In any case, many challenges still remain partially unsolved, including camera movements (i.e., pan/tilt), scale changes (i.e., zoom-in/zoom-out) and deletion of the initial foreground elements from the background model. This paper describes a method for background modeling and foreground detection able to address all the mentioned challenges. In particular, the proposed method uses a spatio-temporal tracking of sets of keypoints to distinguish the background from the foreground. It analyses these sets by a grid strategy to estimate both camera movements and scale changes. The same sets are also used to construct a panoramic background model and to delete the possible initial foreground elements from it. Experiments carried out on some challenging videos from three different datasets (i.e., PBI, VOT and Airport MotionSeg) demonstrate the effectiveness of the method on PTZ cameras. Other videos from a further dataset (i.e., FBMS) have been used to measure the accuracy of the proposed method with respect to some key works of the current state-of-the-art. Finally, some videos from another dataset (i.e., SBI) have been used to test the method on stationary cameras.

A multipurpose autonomous robot for target recognition in unknown environments

An adaptive acquisition sequence for Sparse 2D Magnetic Resonance Imaging (MRI) is presented. The method combines random sampling of Cartesian trajectories with an adaptive 2D acquisition of radial projections. It is based on the evaluation of the information content of a small percentage of the k-space data collected randomly to identify radial blades of k-space coefficients having maximum information content. The information content of each direction is evaluated by calculating an entropy function defined on the power spectrum of the projections. The images are obtained by using a non linear reconstruction strategy, based on the homotopic \(\mathrm{L}_{0}\)-norm, on the sparse data. The method is tested on MRI images and it is also compared to the weighted Compressed Sensing. Some results are reported and discussed.