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Alterations of gait and balance are a significant cause of falls, injuries, and consequent hospitalizations in the elderly. In addition to age-associated motor decline, other factors can impact gait and stability, including the motor dysfunctions caused by neurological diseases such as Parkinson's disease or hemiplegia after stroke. Monitoring changes and deterioration in gait patterns and balance is crucial for activating rehabilitation treatments and preventing serious consequences. This work presents a Kinect-based solution, suitable for domestic contexts, for assessing gait and balance in individuals at risk of falling. The system captures body movements during home acquisition sessions scheduled by clinicians at definite times of the day and automatically estimates specific functional parameters to objectively characterize the subjects' performance. The system includes a graphical user interface designed to ensure usability in unsupervised contexts: the human-computer interaction mainly relies on natural body movements to support the self-management of the system, if the motor conditions allow it. This work presents the system's features and facilities, and the preliminary results on healthy volunteers' trials.

A system for the automatic assessment of motor impairments in Parkinson's Disease (PD) is presented. The interface, built around optical RGB-Depth devices, allows for tracking of hands and body movements during the performance of standard upper and lower limb tasks, as specified by the Unified Parkinson's Disease Rating Scale (UPDRS). The assessment of the different tasks is performed by machine learning techniques. Selected kinematic parameters characterizing the movements are input to trained classifiers to rate the motor performance. The accurate tracking and characterization of the movements allows for an automatic and objective assessment of the UPDRS tasks, making feasible the monitoring of motor fluctuations also at-home for telemedicine or neurorehabilitation purposes.

A self-managed, home-based system for the automated assessment of a selected set of Parkinson&rsquo, s disease motor symptoms is presented. The system makes use of an optical RGB-Depth device both to implement its gesture-based human computer interface and for the characterization and the evaluation of posture and motor tasks, which are specified according to the Unified Parkinson&rsquo, s Disease Rating Scale (UPDRS). Posture, lower limb movements and postural instability are characterized by kinematic parameters of the patient movement. During an experimental campaign, the performances of patients affected by Parkinson&rsquo, s disease were simultaneously scored by neurologists and analyzed by the system. The sets of parameters which best correlated with the UPDRS scores of subjects&rsquo, performances were then used to train supervised classifiers for the automated assessment of new instances of the tasks. Results on the system usability and the assessment accuracy, as compared to clinical evaluations, indicate that the system is feasible for an objective and automated assessment of Parkinson&rsquo, s disease at home, and it could be the basis for the development of neuromonitoring and neurorehabilitation applications in a telemedicine framework.

This work presents a non-invasive low-cost system suitable for the at home assessmentof the neurological impairment of patients affected by Parkinson's Disease(PD). The assessment is automatic and it is based on the accurate tracking of hands and fingers movements of the patient during the execution of standard upper limb tasks specified by the Unified Parkinson's Disease Rating Scale (UPDRS). The system is based on a human computer interface made by light gloves and an optical tracking RGB-Depth device. The accurate tracking and characterization of hands and fingers movements allows both the automatic and objective assessment of UPDRS tasks and the gesture-based management of the system, making it suitable for motor impaired users, as are PD patients. The assessment of UPDRS tasks is performed by a machine learning approach, whichusesthe kinematic parameters that characterize the patient movements,as input to trained classifiers,with the aim ofautomatically rating the UPDRS scores of the performance. The classifiers have been trained by an experimental campaign,where cohortsof PD patients were contemporary assessed by a neurologist and the system. Results on the accuracy of the system assessments, as compared to the neurologist's ones, are given, along with preliminary results on monitoring experiments at home. Details about the user interfaces of the system, specifically designed for home-monitoring, are provided. The clinimetric properties of the system and its usability have been evaluatedand reported. The results confirm that the system is suitable for the remote monitoring of PD patients at-home.

A system for the management of the automatic assessment of Parkinson's Disease (PD) at-home is presented. The system is based on a non-contact and natural human computer interface which is suitable for motor impaired users, as are PD patients. The interface, built around optical RGB-Depth devices, allows for both gesture-based interaction with the system and tracking of hands and body movements during the performance of standard upper and lower limb tasks, as specified by the Unified Parkinson's Disease Rating Scale (UPDRS). The accurate tracking and characterization of the movements allows for an automatic and objective assessment of the UPDRS tasks, making feasible the monitoring of motor fluctuations at-home and on daily basis, which are important features in the management of the disease progression. The assessment of the different tasks is performed by machine learning techniques. Selected kinematic parameters characterizing the movements are input to trained classifiers to rate the motor performance. Results on monitoring experiments at-home and on the system accuracy as compared to clinical evaluations are presented and discussed.

The increment of the prevalence of neurological diseases due to the trend in population aging demands for new strategies in disease management. In Parkinson&rsquo, s disease (PD), these strategies should aim at improving diagnosis accuracy and frequency of the clinical follow-up by means of decentralized cost-effective solutions. In this context, a system suitable for the remote monitoring of PD subjects is presented. It consists of the integration of two approaches investigated in our previous works, each one appropriate for the movement analysis of specific parts of the body: low-cost optical devices for the upper limbs and wearable sensors for the lower ones. The system performs the automated assessments of six motor tasks of the unified Parkinson&rsquo, s disease rating scale, and it is equipped with a gesture-based human machine interface designed to facilitate the user interaction and the system management. The usability of the system has been evaluated by means of standard questionnaires, and the accuracy of the automated assessment has been verified experimentally. The results demonstrate that the proposed solution represents a substantial improvement in PD assessment respect to the former two approaches treated separately, and a new example of an accurate, feasible and cost-effective mean for the decentralized management of PD.

A system for the management of the automatic assessment of Parkinson’s Disease (PD) at-home is presented. The system is based on a non-contact and natural human computer interface which is suitable for motor impaired users, as are PD patients. The interface, built around optical RGB-Depth devices, allows for both gesture-based interaction with the system and tracking of hands and body movements during the performance of standard upper and lower limb tasks, as specified by the Unified Parkinson’s Disease Rating Scale (UPDRS). The accurate tracking and characterization of the movements allows for an automatic and objective assessment of the UPDRS tasks, making feasible the monitoring of motor fluctuations at-home and on daily basis, which are important features in the management of the disease progression. The assessment of the different tasks is performed by machine learning techniques. Selected kinematic parameters characterizing the movements are input to trained classifiers to rate the motor performance. Results on monitoring experiments at-home and on the system accuracy as compared to clinical evaluations are presented and discussed.

A home-based, reliable, objective and automated assessment of motor performance of patients affected by Parkinson&rsquo, s Disease (PD) is important in disease management, both to monitor therapy efficacy and to reduce costs and discomforts. In this context, we have developed a self-managed system for the automated assessment of the PD upper limb motor tasks as specified by the Unified Parkinson&rsquo, s Disease Rating Scale (UPDRS). The system is built around a Human Computer Interface (HCI) based on an optical RGB-Depth device and a replicable software. The HCI accuracy and reliability of the hand tracking compares favorably against consumer hand tracking devices as verified by an optoelectronic system as reference. The interface allows gestural interactions with visual feedback, providing a system management suitable for motor impaired users. The system software characterizes hand movements by kinematic parameters of their trajectories. The correlation between selected parameters and clinical UPDRS scores of patient performance is used to assess new task instances by a machine learning approach based on supervised classifiers. The classifiers have been trained by an experimental campaign on cohorts of PD patients. Experimental results show that automated assessments of the system replicate clinical ones, demonstrating its effectiveness in home monitoring of PD.

Sono presentati i risultati preliminari dell'applicazione di soluzioni ICT basate su dispositivi ottici di tipo RGB-D e abbinati a tecniche di Computer Vision e Machine Learning per la valutazione posturale nel contesto della malattia di Parkinson. Il documento riporta la descrizione dettagliata dei task funzionali esaminati e del protocollo di acquisizione adottato ed i risultati ottenuti su alcuni esercizi di postura abitualmente utilizzati per l'assessment neurologico della postura, le cui alterazioni sono uno dei sintomi caratterizzanti la malattia durante la sua evoluzione.

Vengono presentati i risultati del confronto di prestazioni tra alcuni sistemi di cattura del movimento della mano nel contesto di alcuni esercizi ampiamente utilizzati in campo medico per la valutazione dello stato neuromotorio di un soggetto. In particolare verranno presi in considerazione il prototipo da noi sviluppato (CNR-HCI) basato su camera RGB-D, guanto colorato ed algoritmi di tracking con "sensor-fusion" di informazioni RGB e DEPTH; ed il dispositivo commerciale Leap Motion, con algoritmo di tracking e modello della mano proprietario. L'obiettivo è verificare le prestazioni di questi due sistemi nell'ipotesi di un loro utilizzo come strumenti di valutazione oggettiva del movimento, in cui requisito fondamentale è l'accuratezza nella cattura del movimento, senza la quale si potrebbero trarre conclusioni errate sulla compromissione motoria di un soggetto. Per la validazione i due sistemi sono stati confrontati con un sistema opto-elettronico (BTS SMART) considerato come il "gold reference" per la cattura del movimento.

In questo rapporto tecnico viene presentato il confronto tra due diversi sistemi di acquisizione per la cattura e l'analisi del movimento degli arti inferiori ed in particolare per la stima degli angoli di ginocchio e gamba durante l'esecuzione di specifici esercizi motori largamente utilizzati per la riabilitazione motoria. Il confronto è tra un sistema optoelettronico, considerato il "gold reference" nel campo dell'analisi del movimento, ed un sistema ottico a basso costo basato su camera RGB-D (Microsoft Kinect). L'obiettivo è l'individuazione di una soluzione di riabilitazione domiciliare che sia sufficientemente accurata nella cattura del movimento, non invasiva, di semplice utilizzo e di basso costo.

This paper presents features and performances of a portable stereo active vision system (AVS), designed and built for data acquisition and monitoring of medium-scale monuments and environments of interest for cultural heritage. The research activity was partially carried out in the framework of PARNASO-SIINDA project 1 , a national project aimed at the development of new techniques for cultural heritage preservation, and the case-of-study of the activity was the Roman Theatre of Aosta (IT). Reliable data acquisition and monitoring of the surface alterations related to the monument degrade was a project demand that motivated the stereo vision approach because of its high spatial sampling rate, high accuracy and close correlation of shape and color measurements. The AVS system architecture is designed around three CCD cameras mounted on three independent pan-tilt stages under computer control. The cameras are equipped with different focal lens objectives, allowing the system to acquire both geometric and colorimetric information of an architectural surface (monuments, frescoes…) at different scales over areas of tens of square meters. Two cameras form a stereo pair and are equipped with long-focal objectives to sample a limited portion of the scene at high spatial resolution, while the third one is equipped with a wide-angle objective and it is intended for surveying the whole scene at low-resolution. Geometric measurements are carried out both by forward triangulation of the optical axes of the stereo pair during fixation of an object and by three-dimensional (3D) object reconstruction from the images of the stereo pair. The fixation process is performed by a PC-controlled image-based servo loop. The fixation approach supplies sparse but accurate 3D measurement in the AVS reference system, while the stereo acquisition of image pairs allows a dense 3D local reconstruction of the surface around the fixation point. Colorimetric measurements are performed at pixel level by processing the images of one of the stereo cameras, which is equipped with tristimulus filters. Results are supplied in terms of CIE-Lab components. A user-friendly interface for the system management allows performing several measurements automatically. On-site data processing allows the user to decide for further investigations immediately, if necessary, without expensive delays. The features of the AVS system, namely the working range of depths (2–10 m), the high spatial sampling rate, the accuracy and the close correlation of shape and color measurements, makes the system competitive with respect to other available instruments and techniques for data acquisition and monitoring of small and medium-scale works of art. In the following the AVS architecture is presented, and laboratory tests made to asses its performances are described. Results of the tests and of the acquisition campaign, made on the Roman Theatre of Aosta (IT), are reported and discussed in comparison with other techniques commonly employed in cultural heritage surveys.

In Parkinson's disease, the clinical assessment of the upper-limb motor test severity occurs through the UPDRS (Unified Parkinson's Disease Rating Scale). With the perspective of building a system for at-home monitoring of Parkinson's patient status, we investigate algorithms for an automatic scoring of the UPDRS upper-limb motor tests. Building on a previuous work, where we developed a system for the automatic evaluation of kinematic parameters relevant for Parkinson's desease, we adopt a machine learning approach to the automatic scoring problem. A pool of Parkinson patients have been scored by neurologists for UPDRS upper-limb motor tests and the kinematic parameters of their performances acquired. The most significant kinematic parameters are identified by a PCA analysis. Different classifiers have been trained with the neurologists UPDRS scores and the associated kinematic parameters of the patients. The results of the investigation are presented.

In Parkinson's disease, the clinical assessment of the disease severity occurs through the UPDRS (Unified Parkinson's Disease Rating Scale). In particular, in the upper-limb motor test part of UPDRS the scoring procedure is related to some kinematic aspects of the hand/finger movements while performing specific motor exercises (e.g. Fingertapping) The intent of this report is to illustrate methods and algorithms used to detect kinematic parameters relevant for Parkinson's desease. Kinematic parameters such as amplitude, velocity, rate, rhythm, anomalies etc. are automatically evaluated. The parameters are obtained by an acquisition system which is based on RGB-Depth cameras, capable of tracking the tri-dimensional trajectories of passive-markers placed on thumb and index fingertips.

The paper illustrates a prototype of an end-to-end solution to support knee rehabilitation at home. The solution has been designed by a multidisciplinary team, including engineers, physical therapists, physicians and ergonomists. The correct execution of the rehabilitation exercises is evaluated in real time by monitoring the range of the knee joint angles by means of Inertial Measurement Units (IMUs) worn by the patient on the shank and thigh. The parameters of the kinematic model of the lower limb, necessary for knee angle estimation, are estimated by an innovative functional calibration algorithm, which is detailed in the paper. The main advantages of this algorithm is its robustness to incorrect IMU placement and to the specific anatomical parameter of the users. Details of the solution architecture and its sub-systems are presented along with preliminary results of the comparison with a reference optoelectronic motion capture system.

Viene presentato lo stato dell'arte sui sistemi di acquisizione del movimento della mano finalizzato al monitoraggio di parametri cinematici nella malattia di Parkinson. Vengono presentate i protocolli e le procedure statistiche per la selezione dei parametri ottimali ai fini della classificazione automatica della gravità della malattia.

In this paper we present an upper-limb tele-rehabilitation solution suitable for patients suffering motor impairments due to neurological diseases (e.g. Parkinson's disease). The solution is based on patient subsystems connected to a clinician subsystem through a secure cloud platform. The patient subsystem is built around a novel Human Computer Interface (HCI) based on RGBDepth camera (e.g. Kinect©), a monitor and soft gloves with color markers. Important features of the HCI are the accuracy of realtime and fine-grained three-dimensional (3D) tracking of hands and fingers, low cost, suitability for motor impaired patients and large working volume. The patient subsystem software implements both standard UPDRS motor tests and exergames, providing automatic evaluations of patients' performances about motor and cognitive functions. The clinician subsystem interface enables at any time the neurologists and the therapists to access data about their patients, evaluate their condition and get in touch with them if needed. Data produced by the patient-side subsystem during the rehabilitation sessions are stored in the data archiving sub-system on the cloud and can be reviewed by clinicians through the clinician subsystem.

Image processing techniques are successfully applied to the automated recognition of motor vehicle number-plates under a large variety of conditions. Recent progress in this field allows one to record information even when human observers fail. New video equipment is on the market and there is an increasing demand on testing them to compare their performances under experimental conditions as similar as possible to the operational ones. In the absence of international standards, this report describes a new method for assessing the performance of video equipment for automated number-plate recognition. The experimental set-up is presented for evaluating both the capability in recognizing alphanumeric characters of number-plates in standard condition and the robustness against noise sources. The method consists of laboratory tests only and avoids field trials, so that different equipment can be characterized with an improved accuracy and compared.

Il documento descrive i formalismi matematici della rappresentazione delle rotazioni in tre dimensioni. Vengono analizzate le rappresentazioni dei sistemi di riferimento e le trasformazioni tra sistemi di riferimento. Inoltre vengono analizzate le composizioni di rotazioni, in particolare le rotazioni implicite ed esplicite, attive e passive. Sono presentati i vari formalismi di descrizione e rappresentazione delle rotazioni in 3D : Matrici di rotazione , Asse-angolo, Angoli di Eulero, Quaternioni, descrivendo le formule di conversione tra le rappresentazioni. Vengono discussi i pro e contra delle varie rappresentazioni, e fornito del codice Matlab per alcune conversioni. Il presente Rapporto di Ricerca è stato redatto per soddisfare parte degli impegni contrattuali stabiliti dal contratto di ricerca tra Telecomitalia e IEIIT-CNR - 2012-13 "Algoritmi di signal processing per la tele-riabilitazione".

l documento descrive i vari modelli adottati dalla bioingegneria per l' articolazione del ginocchio, dell'anca e della caviglia. In particolare si analizza il modello Joint Coordinate System, definito dalla International Society of Biomechanics (ISB). Vengono confrontati i modelli e le diverse notazioni. Viene analizzata la stima degli angoli articolari del ginocchio, le calibrazioni iniziali e le ipotesi adottate nella stima. In particolare vengono analizzate la calibrazione statica, quella strumentale e quella funzionale ( o dinamica) e i loro vantaggi e svantaggi. Il presente Rapporto di Ricerca è stato redatto per soddisfare parte degli impegni contrattuali stabiliti dal contratto di ricerca tra Telecomitalia e IEIIT-CNR - 2012-13 "Algoritmi di signal processing per la tele-riabilitazione".

Il documento descrive la natura del segnale Elettromiografico (EMG) e le problematiche relative alla sua acquisizione, filtraggio, elaborazione ed interpretazione. La descrizione è quindi contestualizzata alla misura dell'EMG nei muscoli dell'arto inferiore tramite sensori EMG wireless su piattaforma Shimmer@, e agli algoritmi in Matlab@ (con codice sorgente incluso) sviluppati per l'acquisizione, il filtraggio, l'elaborazione e la successiva interpretazione nel contesto della misura dell'attivazione muscolare. Il presente Rapporto di Ricerca è stato redatto per soddisfare parte degli impegni contrattuali stabiliti dal contratto di ricerca tra Telecomitalia e IEIIT-CNR - 2012-13 "Algoritmi di signal processing per la tele-riabilitazione".

The paper illustrates a prototype of an end-to-end service to support unassisted rehabilitation of motor functions. The core of the developed solution is a system able to analyze the patient movements during the execution of the prescribed exercises. The motion analysis relies on real-time evaluations of biomechanical parameters, derived from inertial and magnetic data provided by a wireless body sensor network, worn by the patient while he is doing his exercises. The method uses an approach based on complementary filters and addresses a number of challenges, such as compensating the incorrect positioning of motes and managing perturbations of the Earth magnetic field. Besides, the solution provides the patient with a number of "coaching functions", aimed at helping him in getting the best from his training at home, and with a videoconferencing tool to be used whenever the direct evaluation of the therapist is needed. Although this system can have a wider application field, in this work the focus is on the knee rehabilitation after the anterior cruciate ligament (ACL) reconstruction, in order to demonstrate the suitability of this solution to address specific clinical requirements. Preliminary results on this case study are provided.

In a nation-wide project named SIINDA, a novel instrument was developed and tested, intended for monitoring and diagnosing monuments. The instrument -named AVS- is portable to operate on field, and is capable of measuring 3D and colorimetric coordinates simultaneously. The paper describes the AVS, with a focus on its geometric measurement capability. Particular attention is given to the software compensation of the geometrical errors: the model is given, and the experimental plan to derive the model parameters is described, including the artefact designed and made for this purpose. Experimental results of laboratory and of on field tests are reported.

A technique for the detection of characteristic points and local description of an image is presented. This technique is based on the use of Gabor wavelets and allows point-to- point correspondence between two images of the same scene taken from different points of view. In order to evaluate its usefulness, the technique is compared with the classic local correlation approach.

Path planning of a vehicle running in a structured environment requires road boundaries evaluation for mapping its position and reducing the search area for obstacle detection. This paper describes a real time system that has been developed in the framework of the EUREKA PROMETHEUS European project and is presently under test on a Mobile Laboratory (MOBLAB). The road boundaries are detected by highlighting the large homogeneous region that lies in the bottom of the image, in front of the vehicle. Edge detection, local thresholding and morphological filtering techniques are used to define this region. Its boundaries are tracked in the sequence, relying on hypotheses of continuity of color and shape of the road to overcome drawbacks due to shadows, intersections, hidden boundaries. The proposed technique has been implemented on an integrated system based on a real time imaging processor and a workstation.

The telemeter is a high precision positional tracking device that works by triangulation over the angular measurements provided by two or more telegoniometers. Each of the latter is composed of a TV camera with a teleobjective, looking at the working space through a pair of orthogonal mirrors. A problem with this kind of structure is the automation of the initial pointing of the telegoniometers on the target. We propose to add a third camera with a wide- angle lens framing the whole workspace to provide the line of sight on which the target must lie. In this work we present this `three-eyes' structure and discuss its calibration, as well as the problems related to the strategy for scanning the line of sight and for detecting the tracking condition. Two possible applications to robot qualification and autonomous navigation also are presented.

This work presents a system for the automatic selective harvesting of asparagus in open field being developed in the framework of the Italian National Project on Robotics. It is composed of a mobile robot, equipped with a suitable manipulator, and driven by a stereo-vision module. In this paper we discuss in detail the problems related to the vision module.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.