Adquisición y procesamiento de señales electromiográficas para el control de un vehículo virtual en tiempo real

This work presents the registration and classification of the electromyographic (EMG) signals of the lower extremities, specifically of the gross muscle, in order to control a virtual vehicle designed in Blender. The system has 4 channels, with a graphic interface, which allows the control of a virtual vehicle. For the processing of the signals, different mathematical tools were used such as: Fourier analysis and wavelet analysis. These techniques were used in order to compress data, obtain characteristic patterns in each set of signals and perform digital filtering. The control of the car consists of 4 commands such as: accelerate, stop, right turn and left turn, which are the basic instructions for the real operation of a car. The results showed that it is possible to use biological signals to perform virtual controls (video game). Likewise, it was verified that the parameterization found for each group of EMG signals was satisfactory, since the percentage of errors of the 4 variables studied was 0.04% for a total of 400 executions. This error percentage corroborates that the system has great potential for possible future applications.Este trabajo, se presenta el registro y clasificación de las señales electromiográficas (EMG) de las extremidades inferiores, específicamente del musculo basto, con el fin de controlar un vehículo virtual diseñado en Blender. El sistema tiene de 4 canales, con una interfaz gráfica, que permite el control de un vehículo virtual. Para el procesamiento de las señales, se utilizaron diferentes herramientas matemáticas tales como: análisis de Fourier y análisis wavelet. Estas técnicas se usaron con el objetivo de comprimir datos, obtener patrones característicos en cada conjunto de señales y realizar un filtrado digital. El control del automóvil consta de 4 comandos como: acelerar, detenerse, giro derecha y giro izquierda, las cuales son las instrucciones básicas para el manejo real de un automóvil. Los resultados mostraron que es posible usar señales biológicas para realizar controles virtuales (video juego). Así mismo, se verificó que la parametrizar encontrada de cada grupo de señales EMG, fue satisfactoria, ya que el porcentaje de errores de las 4 variables estudiadas fue del 0.04% para un total de 400 ejecuciones. Este porcentaje de error corrobora que el sistema tiene gran potencialidad para posibles aplicaciones futuras

Structural Generative Descriptions for Temporal Data

In data mining problems the representation or description of data plays a fundamental role, since it defines the set of essential properties for the extraction and characterisation of patterns. However, for the case of temporal data, such as time series and data streams, one outstanding issue when developing mining algorithms is finding an appropriate data description or representation. In this thesis two novel domain-independent representation frameworks for temporal data suitable for off-line and online mining tasks are formulated. First, a domain-independent temporal data representation framework based on a novel data description strategy which combines structural and statistical pattern recognition approaches is developed. The key idea here is to move the structural pattern recognition problem to the probability domain. This framework is composed of three general tasks: a) decomposing input temporal patterns into subpatterns in time or any other transformed domain (for instance, wavelet domain); b) mapping these subpatterns into the probability domain to find attributes of elemental probability subpatterns called primitives; and c) mining input temporal patterns according to the attributes of their corresponding probability domain subpatterns. This framework is referred to as Structural Generative Descriptions (SGDs). Two off-line and two online algorithmic instantiations of the proposed SGDs framework are then formulated: i) For the off-line case, the first instantiation is based on the use of Discrete Wavelet Transform (DWT) and Wavelet Density Estimators (WDE), while the second algorithm includes DWT and Finite Gaussian Mixtures. ii) For the online case, the first instantiation relies on an online implementation of DWT and a recursive version of WDE (RWDE), whereas the second algorithm is based on a multi-resolution exponentially weighted moving average filter and RWDE. The empirical evaluation of proposed SGDs-based algorithms is performed in the context of time series classification, for off-line algorithms, and in the context of change detection and clustering, for online algorithms. For this purpose, synthetic and publicly available real-world data are used. Additionally, a novel framework for multidimensional data stream evolution diagnosis incorporating RWDE into the context of Velocity Density Estimation (VDE) is formulated. Changes in streaming data and changes in their correlation structure are characterised by means of local and global evolution coefficients as well as by means of recursive correlation coefficients. The proposed VDE framework is evaluated using temperature data from the UK and air pollution data from Hong Kong.Open Acces

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Dynamic reconfiguration of human brain networks during learning

Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes -- flexibility and selection -- must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we explore the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.Comment: Main Text: 19 pages, 4 figures Supplementary Materials: 34 pages, 4 figures, 3 table

Human skill capturing and modelling using wearable devices

Industrial robots are delivering more and more manipulation services in manufacturing. However, when the task is complex, it is difficult to programme a robot to fulfil all the requirements because even a relatively simple task such as a peg-in-hole insertion contains many uncertainties, e.g. clearance, initial grasping position and insertion path. Humans, on the other hand, can deal with these variations using their vision and haptic feedback. Although humans can adapt to uncertainties easily, most of the time, the skilled based performances that relate to their tacit knowledge cannot be easily articulated. Even though the automation solution may not fully imitate human motion since some of them are not necessary, it would be useful if the skill based performance from a human could be firstly interpreted and modelled, which will then allow it to be transferred to the robot. This thesis aims to reduce robot programming efforts significantly by developing a methodology to capture, model and transfer the manual manufacturing skills from a human demonstrator to the robot. Recently, Learning from Demonstration (LfD) is gaining interest as a framework to transfer skills from human teacher to robot using probability encoding approaches to model observations and state transition uncertainties. In close or actual contact manipulation tasks, it is difficult to reliabley record the state-action examples without interfering with the human senses and activities. Therefore, wearable sensors are investigated as a promising device to record the state-action examples without restricting the human experts during the skilled execution of their tasks. Firstly to track human motions accurately and reliably in a defined 3-dimensional workspace, a hybrid system of Vicon and IMUs is proposed to compensate for the known limitations of the individual system. The data fusion method was able to overcome occlusion and frame flipping problems in the two camera Vicon setup and the drifting problem associated with the IMUs. The results indicated that occlusion and frame flipping problems associated with Vicon can be mitigated by using the IMU measurements. Furthermore, the proposed method improves the Mean Square Error (MSE) tracking accuracy range from 0.8˚ to 6.4˚ compared with the IMU only method. Secondly, to record haptic feedback from a teacher without physically obstructing their interactions with the workpiece, wearable surface electromyography (sEMG) armbands were used as an indirect method to indicate contact feedback during manual manipulations. A muscle-force model using a Time Delayed Neural Network (TDNN) was built to map the sEMG signals to the known contact force. The results indicated that the model was capable of estimating the force from the sEMG armbands in the applications of interest, namely in peg-in-hole and beater winding tasks, with MSE of 2.75N and 0.18N respectively. Finally, given the force estimation and the motion trajectories, a Hidden Markov Model (HMM) based approach was utilised as a state recognition method to encode and generalise the spatial and temporal information of the skilled executions. This method would allow a more representative control policy to be derived. A modified Gaussian Mixture Regression (GMR) method was then applied to enable motions reproduction by using the learned state-action policy. To simplify the validation procedure, instead of using the robot, additional demonstrations from the teacher were used to verify the reproduction performance of the policy, by assuming human teacher and robot learner are physical identical systems. The results confirmed the generalisation capability of the HMM model across a number of demonstrations from different subjects; and the reproduced motions from GMR were acceptable in these additional tests. The proposed methodology provides a framework for producing a state-action model from skilled demonstrations that can be translated into robot kinematics and joint states for the robot to execute. The implication to industry is reduced efforts and time in programming the robots for applications where human skilled performances are required to cope robustly with various uncertainties during tasks execution

Sparse Eigenmotions derived from daily life kinematics implemented on a dextrous robotic hand

Our hands are considered one of the most complex to control actuated systems, thus, emulating the manipulative skills of real hands is still an open challenge even in anthropomorphic robotic hand. While the action of the 4 long fingers and simple grasp motions through opposable thumbs have been successfully implemented in robotic designs, complex in-hand manipulation of objects was difficult to achieve. We take an approach grounded in data-driven extraction of control primitives from natural human behaviour to develop novel ways to understand the dexterity of hands. We collected hand kinematics datasets from natural, unconstrained human behaviour of daily life in 8 healthy in a studio flat environment. We then applied our Sparse Motion Decomposition approach to extract spatio-temporally localised modes of hand motion that are both time-scale and amplitude-scale invariant. These Sparse EigenMotions (SEMs)[1] form a sparse symbolic code that encodes continuous hand motions. We mechanically implemented the common SEMs on our novel dexterous robotic hand [2] in open-loop control. We report that without processing any feedback during grasp control, several of the SEMs resulted in stable grasps of different daily life objects. The finding that SEMs extracted from daily life implement stable grasps in open-loop control of dexterous hands, lends further support for our hypothesis the brain controls the hand using sparse control strategies

The Investigation of Motor Primitives During Human Reaching Movements and the Quantification of Post-Stroke Motor Impairment

Movement is a complex task, requiring precise and coordinated muscle contractions. The forces and torques produced during multi-segmental movement of the upper limbs in humans, must be controlled, in order for movement to be achieved successfully. Although a critical aspect of everyday life, there remain questions regarding the specific controller used by the central nervous system to govern movement. Furthermore, how this system is affected by neurological injuries such as stroke also remains in question. It was the goal of this thesis to examine the neurological control of movement in healthy individuals and apply these findings to the further investigation of chronically motor impaired stroke patients. Additionally, this work aimed at providing clinicians with a more reliable, easy to use, and inexpensive approach to quantify post-stroke motor impairment