Structural brain correlates of defective gesture performance in schizophrenia

INTRODUCTION The neural correlates of impaired performance of gestures are currently unclear. Lesion studies showed variable involvement of the ventro-dorsal stream particularly left inferior frontal gyrus (IFG) in gesture performance on command. However, findings cannot be easily generalized as lesions may be biased by the architecture of vascular supply and involve brain areas beyond the critical region. The neuropsychiatric syndrome of schizophrenia shares apraxic-like errors and altered brain structure without macroanatomic lesions. Schizophrenia may therefore qualify as a model disorder to test neural correlates of gesture impairments. METHODS We included 45 schizophrenia patients and 44 healthy controls in the study to investigate the structural brain correlates of defective gesturing in schizophrenia using voxel based morphometry. Gestures were tested in two domains: meaningful gestures (transitive and intransitive) on verbal command and imitation of meaningless gestures. Cut-off scores were used to separate patients with deficits, patients without deficits and controls. Group differences in gray matter (GM) volume were explored in an ANCOVA. RESULTS Patients performed poorer than controls in each gesture category (p < .001). Patients with deficits in producing meaningful gestures on command had reduced GM predominantly in left IFG, with additional involvement of right insula and anterior cingulate cortex. Patients with deficits differed from patients without deficits in right insula, inferior parietal lobe (IPL) and superior temporal gyrus. CONCLUSIONS Impaired performance of meaningful gestures on command was linked to volume loss predominantly in the praxis network in schizophrenia. Thus, the behavioral similarities between apraxia and schizophrenia are paralleled by structural alterations. However, few associations between behavioral impairment and structural brain alterations appear specific to schizophrenia.

User-independent accelerometer-based gesture recognition for mobile devices

Many mobile devices embed nowadays inertial sensors. This enables new forms of human-computer interaction through the use of gestures (movements performed with the mobile device) as a way of communication. This paper presents an accelerometer-based gesture recognition system for mobile devices which is able to recognize a collection of 10 different hand gestures. The system was conceived to be light and to operate in a user -independent manner in real time. The recognition system was implemented in a smart phone and evaluated through a collection of user tests, which showed a recognition accuracy similar to other state-of-the art techniques and a lower computational complexity. The system was also used to build a human -robot interface that enables controlling a wheeled robot with the gestures made with the mobile phone.

Authentication in mobile devices through hand gesture recognition

This article proposes an innovative biometric technique based on the idea of authenticating a person on a mobile device by gesture recognition. To accomplish this aim, a user is prompted to be recognized by a gesture he/she performs moving his/her hand while holding a mobile device with an accelerometer embedded. As users are not able to repeat a gesture exactly in the air, an algorithm based on sequence alignment is developed to correct slight differences between repetitions of the same gesture. The robustness of this biometric technique has been studied within 2 different tests analyzing a database of 100 users with real falsifications. Equal Error Rates of 2.01 and 4.82% have been obtained in a zero-effort and an active impostor attack, respectively. A permanence evaluation is also presented from the analysis of the repetition of the gestures of 25 users in 10 sessions over a month. Furthermore, two different gesture databases have been developed: one made up of 100 genuine identifying 3-D hand gestures and 3 impostors trying to falsify each of them and another with 25 volunteers repeating their identifying 3- D hand gesture in 10 sessions over a month. These databases are the most extensive in published studies, to the best of our knowledge.

Gesture recognition using mobile phone's inertial sensors

The availability of inertial sensors embedded in mobile devices has enabled a new type of interaction based on the movements or “gestures” made by the users when holding the device. In this paper we propose a gesture recognition system for mobile devices based on accelerometer and gyroscope measurements. The system is capable of recognizing a set of predefined gestures in a user-independent way, without the need of a training phase. Furthermore, it was designed to be executed in real-time in resource-constrained devices, and therefore has a low computational complexity. The performance of the system is evaluated offline using a dataset of gestures, and also online, through some user tests with the system running in a smart phone.

Diseño e implementación de un controlador de ratón gestual para Microsoft Windows con Kinect - Touchless Gesture User Interface

El dispositivo Microsoft Kinect for Windows y similares, han introducido en el mundo del PC una nueva forma de interacción denominada “Touchless Gesture User Interface” o TGUI (Interfaz de Usuario por Gestos sin Contacto) [Gentile et al. 2011]. Se trata de una tecnología novedosa en proceso de evolución. La tecnología de Kinect detecta la presencia de un usuario y monitoriza la posición en el espacio de sus articulaciones principales. Esta información permite desarrollar aplicaciones que posibiliten interactuar al usuario con una computadora mediante gestos y sin la necesidad de estar en contacto con periférico alguno. Desde la invención del periférico ratón en los años 60, resulta curioso que con la frenética evolución que ha experimentado el mundo de la informática en todos estos años, este dispositivo no haya sufrido cambios significativos o no haya sido incluso sustituido por otro periférico. En este proyecto se ha abordado el reto de desarrollar un controlador de ratón gestual para Windows utilizando Microsoft Kinect, de tal forma que se sustituya el uso del típico ratón y sea el propio usuario el que actúe como controlador mediante gestos y movimientos de sus manos. El resultado es llamativo y aporta numerosas mejoras y novedades frente a aplicaciones similares, aunque deja en evidencia algunas de las limitaciones de la tecnología implementada por Kinect a día de hoy. Es de esperar que cuando evolucione su tecnología, su uso se convierta en cotidiano.

New generation of human machine interfaces for controlling UAV through depth based gesture recognition

New forms of natural interactions between human operators and UAVs (Unmanned Aerial Vehicle) are demanded by the military industry to achieve a better balance of the UAV control and the burden of the human operator. In this work, a human machine interface (HMI) based on a novel gesture recognition system using depth imagery is proposed for the control of UAVs. Hand gesture recognition based on depth imagery is a promising approach for HMIs because it is more intuitive, natural, and non-intrusive than other alternatives using complex controllers. The proposed system is based on a Support Vector Machine (SVM) classifier that uses spatio-temporal depth descriptors as input features. The designed descriptor is based on a variation of the Local Binary Pattern (LBP) technique to efficiently work with depth video sequences. Other major consideration is the especial hand sign language used for the UAV control. A tradeoff between the use of natural hand signs and the minimization of the inter-sign interference has been established. Promising results have been achieved in a depth based database of hand gestures especially developed for the validation of the proposed system.

Human-computer interaction based on visual hand-gesture recognition using volumetric spatiograms of local binary patterns

A more natural, intuitive, user-friendly, and less intrusive Human–Computer interface for controlling an application by executing hand gestures is presented. For this purpose, a robust vision-based hand-gesture recognition system has been developed, and a new database has been created to test it. The system is divided into three stages: detection, tracking, and recognition. The detection stage searches in every frame of a video sequence potential hand poses using a binary Support Vector Machine classifier and Local Binary Patterns as feature vectors. These detections are employed as input of a tracker to generate a spatio-temporal trajectory of hand poses. Finally, the recognition stage segments a spatio-temporal volume of data using the obtained trajectories, and compute a video descriptor called Volumetric Spatiograms of Local Binary Patterns (VS-LBP), which is delivered to a bank of SVM classifiers to perform the gesture recognition. The VS-LBP is a novel video descriptor that constitutes one of the most important contributions of the paper, which is able to provide much richer spatio-temporal information than other existing approaches in the state of the art with a manageable computational cost. Excellent results have been obtained outperforming other approaches of the state of the art.

Development of a hand-gesture recognition system for human-computer interaction

The aim of this Master Thesis is the analysis, design and development of a robust and reliable Human-Computer Interaction interface, based on visual hand-gesture recognition. The implementation of the required functions is oriented to the simulation of a classical hardware interaction device: the mouse, by recognizing a specific hand-gesture vocabulary in color video sequences. For this purpose, a prototype of a hand-gesture recognition system has been designed and implemented, which is composed of three stages: detection, tracking and recognition. This system is based on machine learning methods and pattern recognition techniques, which have been integrated together with other image processing approaches to get a high recognition accuracy and a low computational cost. Regarding pattern recongition techniques, several algorithms and strategies have been designed and implemented, which are applicable to color images and video sequences. The design of these algorithms has the purpose of extracting spatial and spatio-temporal features from static and dynamic hand gestures, in order to identify them in a robust and reliable way. Finally, a visual database containing the necessary vocabulary of gestures for interacting with the computer has been created.

Hand gesture recognition based on morphologic features

Comunicación presentada en el IX Simposium Nacional de Reconocimiento de Formas y Análisis de Imágenes, Benicàssim, Mayo, 2001.

Control and Guidance of Low-Cost Robots via Gesture Perception for Monitoring Activities in the Home

This paper describes the development of a low-cost mini-robot that is controlled by visual gestures. The prototype allows a person with disabilities to perform visual inspections indoors and in domestic spaces. Such a device could be used as the operator's eyes obviating the need for him to move about. The robot is equipped with a motorised webcam that is also controlled by visual gestures. This camera is used to monitor tasks in the home using the mini-robot while the operator remains quiet and motionless. The prototype was evaluated through several experiments testing the ability to use the mini-robot’s kinematics and communication systems to make it follow certain paths. The mini-robot can be programmed with specific orders and can be tele-operated by means of 3D hand gestures to enable the operator to perform movements and monitor tasks from a distance.

Gesture performance in first- and multiple-episode patients with schizophrenia spectrum disorders

BACKGROUND/AIM Gesturing plays an important role in social behavior and social learning. Deficits are frequent in schizophrenia and may contribute to impaired social functioning. Information about deficits during the course of the disease and presence of severity and patterns of impairment in first-episode patients is missing. Hence, we aimed to investigate gesturing in first- compared to multiple-episode schizophrenia patients and healthy controls. METHODS In 14 first-episode patients, 14 multiple-episode patients and 16 healthy controls matched for age, gender and education, gesturing was assessed by the comprehensive Test of Upper Limb Apraxia. Performance in two domains of gesturing - imitation and pantomime - was recorded on video. Raters of gesture performance were blinded. RESULTS Patients with multiple episodes had severe gestural deficits. For almost all gesture categories, performance was worse in multiple- than in first-episode patients. First-episode patients demonstrated subtle deficits with a comparable pattern. CONCLUSIONS Subjects with multiple psychotic episodes have severe deficits in gesturing, while only mild impairments were found in first-episode patients independent of age, gender, education and negative symptoms. The results indicate that gesturing is impaired at the onset of disease and likely to further deteriorate during its course.

Advanced elocution : designed as a practical treatise for teachers and students in vocal training articulation, physical culture and gesture /

Mode of access: Internet.

The orator's manual : a practical and philosophical treatise on vocal culture, emphasis and gesture, together with selections for declamation and reading ... / by George L. Raymond.

Mode of access: Internet.

A practical treatise on gesture, chiefly abstracted from Austin's Chironomia ...

Mode of access: Internet.

A practical manual of elocution : embracing voice and gesture : designed for schools, academies and colleges, as well as for private learners /

Includes index.