Portuguese sign language recognition via computer vision and depth sensor

Sign languages are used worldwide by a multitude of individuals. They are mostly used by the deaf communities and their teachers, or people associated with them by ties of friendship or family. Speakers are a minority of citizens, often segregated, and over the years not much attention has been given to this form of communication, even by the scientific community. In fact, in Computer Science there is some, but limited, research and development in this area. In the particular case of sign Portuguese Sign Language-PSL that fact is more evident and, to our knowledge there isn’t yet an efficient system to perform the automatic recognition of PSL signs. With the advent and wide spreading of devices such as depth sensors, there are new possibilities to address this problem. In this thesis, we have specified, developed, tested and preliminary evaluated, solutions that we think will bring valuable contributions to the problem of Automatic Gesture Recognition, applied to Sign Languages, such as the case of Portuguese Sign Language. In the context of this work, Computer Vision techniques were adapted to the case of Depth Sensors. A proper gesture taxonomy for this problem was proposed, and techniques for feature extraction, representation, storing and classification were presented. Two novel algorithms to solve the problem of real-time recognition of isolated static poses were specified, developed, tested and evaluated. Two other algorithms for isolated dynamic movements for gesture recognition (one of them novel), have been also specified, developed, tested and evaluated. Analyzed results compare well with the literature.As Línguas Gestuais são utilizadas em todo o Mundo por uma imensidão de indivíduos. Trata-se na sua grande maioria de surdos e/ou mudos, ou pessoas a eles associados por laços familiares de amizade ou professores de Língua Gestual. Tratando-se de uma minoria, muitas vezes segregada, não tem vindo a ser dada ao longo dos anos pela comunidade científica, a devida atenção a esta forma de comunicação. Na área das Ciências da Computação existem alguns, mas poucos trabalhos de investigação e desenvolvimento. No caso particular da Língua Gestual Portuguesa - LGP esse facto é ainda mais evidente não sendo nosso conhecimento a existência de um sistema eficaz e efetivo para fazer o reconhecimento automático de gestos da LGP. Com o aparecimento ou massificação de dispositivos, tais como sensores de profundidade, surgem novas possibilidades para abordar este problema. Nesta tese, foram especificadas, desenvolvidas, testadas e efectuada a avaliação preliminar de soluções que acreditamos que trarão valiosas contribuições para o problema do Reconhecimento Automático de Gestos, aplicado às Línguas Gestuais, como é o caso da Língua Gestual Portuguesa. Foram adaptadas técnicas de Visão por Computador ao caso dos Sensores de Profundidade. Foi proposta uma taxonomia adequada ao problema, e apresentadas técnicas para a extração, representação e armazenamento de características. Foram especificados, desenvolvidos, testados e avaliados dois algoritmos para resolver o problema do reconhecimento em tempo real de poses estáticas isoladas. Foram também especificados, desenvolvidos, testados e avaliados outros dois algoritmos para o Reconhecimento de Movimentos Dinâmicos Isolados de Gestos(um deles novo).Os resultados analisados são comparáveis à literatura.Las lenguas de Signos se utilizan en todo el Mundo por una multitud de personas. En su mayoría son personas sordas y/o mudas, o personas asociadas con ellos por vínculos de amistad o familiares y profesores de Lengua de Signos. Es una minoría de personas, a menudo segregadas, y no se ha dado en los últimos años por la comunidad científica, la atención debida a esta forma de comunicación. En el área de Ciencias de la Computación hay alguna pero poca investigación y desarrollo. En el caso particular de la Lengua de Signos Portuguesa - LSP, no es de nuestro conocimiento la existencia de un sistema eficiente y eficaz para el reconocimiento automático. Con la llegada en masa de dispositivos tales como Sensores de Profundidad, hay nuevas posibilidades para abordar el problema del Reconocimiento de Gestos. En esta tesis se han especificado, desarrollado, probado y hecha una evaluación preliminar de soluciones, aplicada a las Lenguas de Signos como el caso de la Lengua de Signos Portuguesa - LSP. Se han adaptado las técnicas de Visión por Ordenador para el caso de los Sensores de Profundidad. Se propone una taxonomía apropiada para el problema y se presentan técnicas para la extracción, representación y el almacenamiento de características. Se desarrollaran, probaran, compararan y analizan los resultados de dos nuevos algoritmos para resolver el problema del Reconocimiento Aislado y Estático de Posturas. Otros dos algoritmos (uno de ellos nuevo) fueran también desarrollados, probados, comparados y analizados los resultados, para el Reconocimiento de Movimientos Dinámicos Aislados de los Gestos

Automation of motor dexterity assessment

Motor dexterity assessment is regularly performed in rehabilitation wards to establish patient status and automatization for such routinary task is sought. A system for automatizing the assessment of motor dexterity based on the Fugl-Meyer scale and with loose restrictions on sensing technologies is presented. The system consists of two main elements: 1) A data representation that abstracts the low level information obtained from a variety of sensors, into a highly separable low dimensionality encoding employing t-distributed Stochastic Neighbourhood Embedding, and, 2) central to this communication, a multi-label classifier that boosts classification rates by exploiting the fact that the classes corresponding to the individual exercises are naturally organized as a network. Depending on the targeted therapeutic movement class labels i.e. exercises scores, are highly correlated-patients who perform well in one, tends to perform well in related exercises-; and critically no node can be used as proxy of others - an exercise does not encode the information of other exercises. Over data from a cohort of 20 patients, the novel classifier outperforms classical Naive Bayes, random forest and variants of support vector machines (ANOVA: p <; 0.001). The novel multi-label classification strategy fulfills an automatic system for motor dexterity assessment, with implications for lessening therapist's workloads, reducing healthcare costs and providing support for home-based virtual rehabilitation and telerehabilitation alternatives

Qualification of a Collaborative Human-robot Welding Cell

AbstractThis work is focused on evaluating performance of a collaborative robot welding cell developed in our previous research. Such a cell is based on an interactive cooperation between a human supervisor and a welding robot. This approach to organizing a workstation allows to employ robots even in the case of prototypes or small productions. Research on collaborative robots usually focuses on safety issues and on the programming techniques. Present work deals with a complementary problem crucial to industrial applications: the qualification of the welding cell performance in terms of accuracy, repeatability and dependability.In this application, the human worker is responsible for handling of the parts to be assembled and for teaching the robot. The robot is in charge of actual welding. Teaching is executed by demonstration: the teacher shows the welding trajectories with a pointer observed by a motion capture system. The program is generated automatically and executed by the robot. Robots and humans share the same workspace in different times therefore human risk exposure is minimal.Industrial applications of this or similar technology require that the process reliability and capability be assessed. We describe and analyze error accumulation along the entire data flow from the measurement tool, through the reference system transformations, to the actual representation and execution of the robot program

Implementation of a neural network-based electromyographic control system for a printed robotic hand

3D printing has revolutionized the manufacturing process reducing costs and time, but only when combined with robotics and electronics, this structures could develop their full potential. In order to improve the available printable hand designs, a control system based on electromyographic (EMG) signals has been implemented, so that different movement patterns can be recognized and replicated in the bionic hand in real time. This control system has been developed in Matlab/ Simulink comprising EMG signal acquisition, feature extraction, dimensionality reduction and pattern recognition through a trained neural-network. Pattern recognition depends on the features used, their dimensions and the time spent in signal processing. Finding balance between this execution time and the input features of the neural network is a crucial step for an optimal classification.Ingeniería Biomédic

Recognition of elementary arm movements using orientation of a tri-axial accelerometer located near the wrist

In this paper we present a method for recognising three fundamental movements of the human arm (reach and retrieve, lift cup to mouth, rotation of the arm) by determining the orientation of a tri-axial accelerometer located near the wrist. Our objective is to detect the occurrence of such movements performed with the impaired arm of a stroke patient during normal daily activities as a means to assess their rehabilitation. The method relies on accurately mapping transitions of predefined, standard orientations of the accelerometer to corresponding elementary arm movements. To evaluate the technique, kinematic data was collected from four healthy subjects and four stroke patients as they performed a number of activities involved in a representative activity of daily living, 'making-a-cup-of-tea'. Our experimental results show that the proposed method can independently recognise all three of the elementary upper limb movements investigated with accuracies in the range 91–99% for healthy subjects and 70–85% for stroke patients

South African sign language recognition using feature vectors and Hidden Markov Models

Masters of ScienceThis thesis presents a system for performing whole gesture recognition for South African Sign Language. The system uses feature vectors combined with Hidden Markov models. In order to constuct a feature vector, dynamic segmentation must occur to extract the signer's hand movements. Techniques and methods for normalising variations that occur when recording a signer performing a gesture, are investigated. The system has a classification rate of 69%.South Afric

Fast upper body pose estimation for human-robot interaction

This work describes an upper body pose tracker that finds a 3D pose estimate using video sequences obtained from a monocular camera, with applications in human-robot interaction in mind. A novel mixture of Ornstein-Uhlenbeck processes model, trained in a reduced dimensional subspace and designed for analytical tractability, is introduced. This model acts as a collection of mean-reverting random walks that pull towards more commonly observed poses. Pose tracking using this model can be Rao-Blackwellised, allowing for computational efficiency while still incorporating bio-mechanical properties of the upper body. The model is used within a recursive Bayesian framework to provide reliable estimates of upper body pose when only a subset of body joints can be detected. Model training data can be extended through a retargeting process, and better pose coverage obtained through the use of Poisson disk sampling in the model training stage. Results on a number of test datasets show that the proposed approach provides pose estimation accuracy comparable with the state of the art in real time (30 fps) and can be extended to the multiple user case. As a motivating example, this work also introduces a pantomimic gesture recognition interface. Traditional approaches to gesture recognition for robot control make use of predefined codebooks of gestures, which are mapped directly to the robot behaviours they are intended to elicit. These gesture codewords are typically recognised using algorithms trained on multiple recordings of people performing the predefined gestures. Obtaining these recordings can be expensive and time consuming, and the codebook of gestures may not be particularly intuitive. This thesis presents arguments that pantomimic gestures, which mimic the intended robot behaviours directly, are potentially more intuitive, and proposes a transfer learning approach to recognition, where human hand gestures are mapped to recordings of robot behaviour by extracting temporal and spatial features that are inherently present in both pantomimed actions and robot behaviours. A Bayesian bias compensation scheme is introduced to compensate for potential classification bias in features. Results from a quadrotor behaviour selection problem show that good classification accuracy can be obtained when human hand gestures are recognised using behaviour recordings, and that classification using these behaviour recordings is more robust than using human hand recordings when users are allowed complete freedom over their choice of input gestures