A Generic Framework for Hidden Markov Models on Biomedical Data

Background: Biomedical data are usually collections of longitudinal data assessed at certain points in time. Clinical observations assess the presences and severity of symptoms, which are the basis for description and modeling of disease progression. Deciphering potential underlying unknowns solely from the distinct observation would substantially improve the understanding of pathological cascades. Hidden Markov Models (HMMs) have been successfully applied to the processing of possibly noisy continuous signals. The aim was to improve the application HMMs to multivariate time-series of categorically distributed data. Here, we used HHMs to study prediction of the loss of free walking ability as one major clinical deterioration in the most common autosomal dominantly inherited ataxia disorder worldwide. We used HHMs to investigate the prediction of loss of the ability to walk freely, representing a major clinical deterioration in the most common autosomal-dominant inherited ataxia disorder worldwide. Results: We present a prediction pipeline which processes data paired with a configuration file, enabling to construct, validate and query a fully parameterized HMM-based model. In particular, we provide a theoretical and practical framework for multivariate time-series inference based on HMMs that includes constructing multiple HMMs, each to predict a particular observable variable. Our analysis is done on random data, but also on biomedical data based on Spinocerebellar ataxia type 3 disease. Conclusions: HHMs are a promising approach to study biomedical data that naturally are represented as multivariate time-series. Our implementation of a HHMs framework is publicly available and can easily be adapted for further applications

On adaptive decision rules and decision parameter adaptation for automatic speech recognition

Recent advances in automatic speech recognition are accomplished by designing a plug-in maximum a posteriori decision rule such that the forms of the acoustic and language model distributions are specified and the parameters of the assumed distributions are estimated from a collection of speech and language training corpora. Maximum-likelihood point estimation is by far the most prevailing training method. However, due to the problems of unknown speech distributions, sparse training data, high spectral and temporal variabilities in speech, and possible mismatch between training and testing conditions, a dynamic training strategy is needed. To cope with the changing speakers and speaking conditions in real operational conditions for high-performance speech recognition, such paradigms incorporate a small amount of speaker and environment specific adaptation data into the training process. Bayesian adaptive learning is an optimal way to combine prior knowledge in an existing collection of general models with a new set of condition-specific adaptation data. In this paper, the mathematical framework for Bayesian adaptation of acoustic and language model parameters is first described. Maximum a posteriori point estimation is then developed for hidden Markov models and a number of useful parameters densities commonly used in automatic speech recognition and natural language processing.published_or_final_versio

Using Hidden Markov Models to Segment and Classify Wrist Motions Related to Eating Activities

Advances in body sensing and mobile health technology have created new opportunities for empowering people to take a more active role in managing their health. Measurements of dietary intake are commonly used for the study and treatment of obesity. However, the most widely used tools rely upon self-report and require considerable manual effort, leading to underreporting of consumption, non-compliance, and discontinued use over the long term. We are investigating the use of wrist-worn accelerometers and gyroscopes to automatically recognize eating gestures. In order to improve recognition accuracy, we studied the sequential ependency of actions during eating. In chapter 2 we first undertook the task of finding a set of wrist motion gestures which were small and descriptive enough to model the actions performed by an eater during consumption of a meal. We found a set of four actions: rest, utensiling, bite, and drink; any alternative gestures is referred as the other gesture. The stability of the definitions for gestures was evaluated using an inter-rater reliability test. Later, in chapter 3, 25 meals were hand labeled and used to study the existence of sequential dependence of the gestures. To study this, three types of classifiers were built: 1) a K-nearest neighbor classifier which uses no sequential context, 2) a hidden Markov model (HMM) which captures the sequential context of sub-gesture motions, and 3) HMMs that model inter-gesture sequential dependencies. We built first-order to sixth-order HMMs to evaluate the usefulness of increasing amounts of sequential dependence to aid recognition. The first two were our baseline algorithms. We found that the adding knowledge of the sequential dependence of gestures achieved an accuracy of 96.5%, which is an improvement of 20.7% and 12.2% over the KNN and sub-gesture HMM. Lastly, in chapter 4, we automatically segmented a continuous wrist motion signal and assessed its classification performance for each of the three classifiers. Again, the knowledge of sequential dependence enhances the recognition of gestures in unsegmented data, achieving 90% accuracy and improving 30.1% and 18.9% over the KNN and the sub-gesture HMM

A motion-based approach for audio-visual automatic speech recognition

The research work presented in this thesis introduces novel approaches for both visual region of interest extraction and visual feature extraction for use in audio-visual automatic speech recognition. In particular, the speaker‘s movement that occurs during speech is used to isolate the mouth region in video sequences and motionbased features obtained from this region are used to provide new visual features for audio-visual automatic speech recognition. The mouth region extraction approach proposed in this work is shown to give superior performance compared with existing colour-based lip segmentation methods. The new features are obtained from three separate representations of motion in the region of interest, namely the difference in luminance between successive images, block matching based motion vectors and optical flow. The new visual features are found to improve visual-only and audiovisual speech recognition performance when compared with the commonly-used appearance feature-based methods. In addition, a novel approach is proposed for visual feature extraction from either the discrete cosine transform or discrete wavelet transform representations of the mouth region of the speaker. In this work, the image transform is explored from a new viewpoint of data discrimination; in contrast to the more conventional data preservation viewpoint. The main findings of this work are that audio-visual automatic speech recognition systems using the new features extracted from the frequency bands selected according to their discriminatory abilities generally outperform those using features designed for data preservation. To establish the noise robustness of the new features proposed in this work, their performance has been studied in presence of a range of different types of noise and at various signal-to-noise ratios. In these experiments, the audio-visual automatic speech recognition systems based on the new approaches were found to give superior performance both to audio-visual systems using appearance based features and to audio-only speech recognition systems

Towards gestural understanding for intelligent robots

Fritsch JN. Towards gestural understanding for intelligent robots. Bielefeld: Universität Bielefeld; 2012.A strong driving force of scientific progress in the technical sciences is the quest for systems that assist humans in their daily life and make their life easier and more enjoyable. Nowadays smartphones are probably the most typical instances of such systems. Another class of systems that is getting increasing attention are intelligent robots. Instead of offering a smartphone touch screen to select actions, these systems are intended to offer a more natural human-machine interface to their users. Out of the large range of actions performed by humans, gestures performed with the hands play a very important role especially when humans interact with their direct surrounding like, e.g., pointing to an object or manipulating it. Consequently, a robot has to understand such gestures to offer an intuitive interface. Gestural understanding is, therefore, a key capability on the way to intelligent robots. This book deals with vision-based approaches for gestural understanding. Over the past two decades, this has been an intensive field of research which has resulted in a variety of algorithms to analyze human hand motions. Following a categorization of different gesture types and a review of other sensing techniques, the design of vision systems that achieve hand gesture understanding for intelligent robots is analyzed. For each of the individual algorithmic steps – hand detection, hand tracking, and trajectory-based gesture recognition – a separate Chapter introduces common techniques and algorithms and provides example methods. The resulting recognition algorithms are considering gestures in isolation and are often not sufficient for interacting with a robot who can only understand such gestures when incorporating the context like, e.g., what object was pointed at or manipulated. Going beyond a purely trajectory-based gesture recognition by incorporating context is an important prerequisite to achieve gesture understanding and is addressed explicitly in a separate Chapter of this book. Two types of context, user-provided context and situational context, are reviewed and existing approaches to incorporate context for gestural understanding are reviewed. Example approaches for both context types provide a deeper algorithmic insight into this field of research. An overview of recent robots capable of gesture recognition and understanding summarizes the currently realized human-robot interaction quality. The approaches for gesture understanding covered in this book are manually designed while humans learn to recognize gestures automatically during growing up. Promising research targeted at analyzing developmental learning in children in order to mimic this capability in technical systems is highlighted in the last Chapter completing this book as this research direction may be highly influential for creating future gesture understanding systems

Markov modelling on human activity recognition

Human Activity Recognition (HAR) is a research topic with a relevant interest in the machine learning community. Understanding the activities that a person is performing and the context where they perform them has a huge importance in multiple applications, including medical research, security or patient monitoring. The improvement of the smart-phones and inertial sensors technologies has lead to the implementation of activity recognition systems based on these devices, either by themselves or combining their information with other sensors. Since humans perform their daily activities sequentially in a specific order, there exist some temporal information in the physical activities that characterize the different human behaviour patterns. However, the most popular approach in HAR is to assume that the data is conditionally independent, segmenting the data in different windows and extracting the most relevant features from each segment. In this thesis we employ the temporal information explicitly, where the raw data provided by the wearable sensors is fed to the training models. Thus, we study how to perform a Markov modelling implementation of a long-term monitoring HAR system with wearable sensors, and we address the existing open problems arising while processing and training the data, combining different sensors and performing the long-term monitoring with battery powered devices. Employing directly the signals from the sensors to perform the recognition can lead to problems due to misplacements of the sensors on the body. We propose an orientation correction algorithm based on quaternions to process the signals and find a common frame reference for all of them independently on the position of the sensors or their orientation. This algorithm allows for a better activity recognition when feed to the classification algorithm when compared with similar approaches, and the quaternion transformations allow for a faster implementation. One of the most popular algorithms to model time series data are Hidden Markov Models (HMMs) and the training of the parameters of the model is performed using the Baum-Welch algorithm. However, this algorithm converges to local maxima and the multiple initializations needed to avoid them makes it computationally expensive for large datasets. We propose employing the theory of spectral learning to develop a discriminative HMM that avoids the problems of the Baum-Welch algorithm, outperforming it in both complexity and computational cost. When we implement a HAR system with several sensors, we need to consider how to perform the combination of the information provided by them. Data fusion can be performed either at signal level or at classification level. When performed at classification level, the usual approach is to combine the decisions of multiple classifiers on the body to obtain the performed activities. However, in the simple case with two classifiers, which can be a practical implementation of a HAR system, the combination reduces to selecting the most discriminative sensor, and no performance improvement is obtained against the single sensor implementation. In this thesis, we propose to employ the soft-outputs of the classifiers in the combination and we develop a method that considers the Markovian structure of the ground truth to capture the dynamics of the activities. We will show that this method improves the recognition of the activities with respect to other combination methods and with respect to the signal fusion case. Finally, in long-term monitoring HAR systems with wearable sensors we need to address the energy efficiency problem that is inherent to battery powered devices. The most common approach to improve the energy efficiency of such devices is to reduce the amount of data acquired by the wearable sensors. In that sense, we introduce a general framework for the energy efficiency of a system with multiple sensors under several energy restrictions. We propose a sensing strategy to optimize the temporal data acquisition based on computing the uncertainty of the activities given the data and adapt the acquisition actively. Furthermore, we develop a sensor selection algorithm based on Bayesian Experimental Design to obtain the best configuration of sensors that performs the activity recognition accurately, allowing for a further improvement on the energy efficiency by limiting the number of sensors employed in the acquisition.El reconocimiento de actividades humanas (HAR) es un tema de investigación con una gran relevancia para la comunidad de aprendizaje máquina. Comprender las actividades que una persona está realizando y el contexto en el que las realiza es de gran importancia en multitud de aplicaciones, entre las que se incluyen investigación médica, seguridad o monitorización de pacientes. La mejora en los smart-phones y en las tecnologías de sensores inerciales han dado lugar a la implementación de sistemas de reconocimiento de actividades basado en dichos dispositivos, ya sea por si mismos o combinándolos con otro tipo de sensores. Ya que los seres humanos realizan sus actividades diarias de manera secuencial en un orden específico, existe una cierta información temporal en las actividades físicas que caracterizan los diferentes patrones de comportamiento, Sin embargo, los algoritmos más comunes asumen que los datos son condicionalmente independientes, segmentándolos en diferentes ventanas y extrayendo las características más relevantes de cada segmento. En esta tesis utilizamos la información temporal de manera explícita, usando los datos crudos de los sensores como entrada de los modelos de entrenamiento. Por ello, analizamos como implementar modelos Markovianos para el reconocimiento de actividades en monitorizaciones de larga duración con sensores wearable, y tratamos los problemas existentes al procesar y entrenar los datos, al combinar diferentes sensores y al realizar adquisiciones de larga duración con dispositivos alimentados por baterías. Emplear directamente las señales de los sensores para realizar el reconocimiento de actividades puede dar lugar a problemas debido a la incorrecta colocación de los sensores en el cuerpo. Proponemos un algoritmo de corrección de la orientación basado en quaterniones para procesar las señales y encontrar un marco de referencia común independiente de la posición de los sensores y su orientación. Este algoritmo permite obtener un mejor reconocimiento de actividades al emplearlo en conjunto con un algoritmo de clasificación, cuando se compara con modelos similares. Además, la transformación de la orientación basada en quaterniones da lugar a una implementación más rápida. Uno de los algoritmos más populares para modelar series temporales son los modelos ocultos de Markov, donde los parámetros del modelo se entrenan usando el algoritmo de Baum-Welch. Sin embargo, este algoritmo converge en general a máximos locales, y las múltiples inicializaciones que se necesitan en su implementación lo convierten en un algoritmo de gran carga computacional cuando se emplea con bases de datos de un volumen considerable. Proponemos emplear la teoría de aprendizaje espectral para desarrollar un HMM discriminativo que evita los problemas del algoritmo de Baum-Welch, superándolo tanto en complejidad como en coste computacional. Cuando se implementa un sistema de reconocimiento de actividades con múltiples sensores, necesitamos considerar cómo realizar la combinación de la información que proporcionan. La fusión de los datos, se puede realizar tanto a nivel de señal como a nivel de clasificación. Cuando se realiza a nivel de clasificación, lo normal es combinar las decisiones de múltiples clasificadores colocados en el cuerpo para obtener las actividades que se están realizando. Sin embargo, en un caso simple donde únicamente se emplean dos sensores, que podría ser una implantación habitual de un sistema de reconocimiento de actividades, la combinación se reduce a seleccionar el sensor más discriminativo, y no se obtiene mejora con respecto a emplear un único sensor. En esta tesis proponemos emplear salidas blandas de los clasificadores para la combinación, desarrollando un modelo que considera la estructura Markoviana de los datos reales para capturar la dinámica de las actividades. Mostraremos como este método mejora el reconocimiento de actividades con respecto a otros métodos de combinación de clasificadores y con respecto a la fusión de los datos a nivel de señal. Por último, abordamos el problema de la eficiencia energética de dispositivos alimentados por baterías en sistemas de reconocimiento de actividades de larga duración. La aproximación más habitual para mejorar la eficiencia energética consiste en reducir el volumen de datos que adquieren los sensores. En ese sentido, introducimos un marco general para tratar el problema de la eficiencia energética en un sistema con múltiples sensores bajo ciertas restricciones de energética. Proponemos una estrategia de adquisición activa para optimizar el sistema temporal de recogida de datos, basándonos en la incertidumbre de las actividades dados los datos que conocemos. Además, desarrollamos un algoritmo de selección de sensores basado diseño experimental Bayesiano y así obtener la mejor configuración para realizar el reconocimiento de actividades limitando el número de sensores empleados y al mismo tiempo reduciendo su consumo energético.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Luis Ignacio Santamaría Caballero.- Secretario: Pablo Martínez Olmos.- Vocal: Alberto Suárez Gonzále