Abstract-Human gait is an important biometric feature for automatic people recognition. Biometric methodologies are generally intrusive and require the collaboration of the subject in order to perform accurate data acquisition. Gait, instead, can be captured at a distance and without collaboration. This makes it an unobtrusive method for recognizing people in video surveillance systems. In this paper we propose a method to characterize walking gait using three-dimensional skeleton information acquired by the Microsoft Kinect sensor. A set of static and dynamic features correlated to human gait are extracted by the estimated skeleton joint positions. Moreover, we proposed to describe joints positions in a coordinate reference system oriented according to the walking direction to better represents the movement of human body. Using unsupervised clustering over a set of 20 subjects we analyze the effectiveness of the selected features in discriminating people gaits. It turns out that a few dynamic parameters involving the movement of knees, elbows and head are good candidates for robust gait characterization.

Smart living and well aging represent key challenges for our society. The precursor state of adverse outcomes that characterize aging has been recognized from scientific community with the frailty syndrome, determined by the loss of physical and psychological capacities. In this paper we define gait and posture indexes that can be effectively and unobtrusively measured using computer vision and RGBD sensors, e.g. the popular MS Kinect. In this study we present preliminary results showing evidence that the proposed approach can pave the way to the design of an automatic and objective tool for detection and early prevention of frailty.

Abstract. Gait exhibits several advantages with respect to other biometrics features: acquisition can be performed through cheap technology, at a distance and without people collaboration. In this paper we perform gait analysis using skeletal data provided by the Microsoft Kinect sensor. We defined a rich set of physical and behavioral features aiming at identifying the more relevant parameters for gait description. Using SVM we showed that a limited set of behavioral features related to the movements of head, elbows and knees is a very effective tool for gait characterization and people recognition. In particular, our experimental results shows that it is possible to achieve 96% classification accuracy when discriminating a group of 20 people.Keywords: Gait characterization, Gait analysis, Kinect, Support Vector Machine Introduction and Related WorkBiometrics is the science that studies the human characteristics for anthropometry research, people identification, access control and many more. Biometric features are measurable data classified as physical or behavioral [1]. The former are related to the body and its shape. Some examples are face, hand, iris, retina and fingerprint. Behavioral characteristics are associated to particular human action, for instance handwriting and walking. Automatic recognition systems are often expensive, intrusive and require the cooperation of the subject during the acquisition. The latter cannot be always guaranteed, for instance in a video surveillance context. In this case, it is useful to recognize people through biometric parameters that can be captured at a distance and without the collaboration of the person, such as gait [2].Gait analysis finds interest in video surveillance systems [3,4] and forensics science [5,6]. Furthermore, many applications analyze the gait in order to discover pathologies of the body movement [7], rehabilitation therapy [8], identify the fall risk in elderly population in order to assess the frailty syndrome [9,10]. All these applications are based on the analysis of video and 2D images. Images and videos are processed in order to collect gait parameters applying both model-based approaches, using the definition of a 3D model of the body in movement [11][12][13], or by model-free approaches, that process the silhouette of a walking person [14]. In this paper we implemented a model-based approach using the Microsoft Kinect sensor. Kinect is more that a simple RGB camera, since it is equipped with a depth sensor providing 3D information related to the movements of body joints. The 3D data are more precise compared to the 2D information extracted from images but, on the contrary, the depth sensor, based on the infrared rays, does not work in outside environment and the depth range is quite limited. In literature exists some applications that exploit Kinect 3D data for people recognition and classification. Preis et al. [15] used only anthropometric features, such as height, length of limbs, stride length and speed, for gait characterization. They te...

Gait has been recently proposed as a biometric feature that, with respect to other human characteristics, can be captured at a distance without requiring the collaboration of the observed subject. Therefore, it turns out to be a promising approach for people identification in several scenarios, e.g. access control and forensic applications. In this paper, we propose an automatic gait recognition system based on a set of features acquired using the 3D skeletal tracking provided by the popular Kinect sensor. Gait features are defined in terms of distances between selected sets of joints and their vertical and lateral sway with respect to walking direction. Moreover we do not rely on any geometrical assumptions on the position of the sensor. The effectiveness of the defined gait features is shown in the case of person identification based on supervised classification, using the principal component analysis and the support vector machine. A rich set of experiments is provided in two scenarios: a controlled identification setup and a classical video-surveillance setting, respectively. Moreover, we investigate if gait can be considered invariant over time for an individual, at least in a time interval of few years, by comparing gait samples of several subjects three years apart. Our experimental analysis shows that the proposed method is robust to acquisition settings and achieves very competitive identification accuracy with respect to the state of the art.

Conventional radiology is performed by means of digital detectors, with various types of technology and different performance in terms of efficiency and image quality. Following the arrival of a new digital detector in a radiology department, all the staff involved should adapt the procedure parameters to the properties of the detector, in order to achieve an optimal result in terms of correct diagnostic information and minimum radiation risks for the patient. The aim of this study was to develop and validate a software capable of simulating a digital X-ray imaging system, using graphics processing unit computing. All radiological image components were implemented in this application: an X-ray tube with primary beam, a virtual patient, noise, scatter radiation, a grid and a digital detector. Three different digital detectors (two digital radiography and a computed radiography systems) were implemented. In order to validate the software, we carried out a quantitative comparison of geometrical and anthropomorphic phantom simulated images with those acquired. In terms of average pixel values, the maximum differences were below 15%, while the noise values were in agreement with a maximum difference of 20%. The relative trends of contrast to noise ratio versus beam energy and intensity were well simulated. Total calculation times were below 3 seconds for clinical images with pixel size of actual dimensions less than 0.2 mm. The application proved to be efficient and realistic. Short calculation times and the accuracy of the results obtained make this software a useful tool for training operators and dose optimisation studies.