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

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

Wearables for independent living in older adults: Gait and falls

Solutions are needed to satisfy care demands of older adults to live independently. Wearable technology (wearables) is one approach that offers a viable means for ubiquitous, sustainable and scalable monitoring of the health of older adults in habitual free-living environments. Gait has been presented as a relevant (bio)marker in ageing and pathological studies, with objective assessment achievable by inertial-based wearables. Commercial wearables have struggled to provide accurate analytics and have been limited by non-clinically oriented gait outcomes. Moreover, some research-grade wearables also fail to provide transparent functionality due to limitations in proprietary software. Innovation within this field is often sporadic, with large heterogeneity of wearable types and algorithms for gait outcomes leading to a lack of pragmatic use. This review provides a summary of the recent literature on gait assessment through the use of wearables, focusing on the need for an algorithm fusion approach to measurement, culminating in the ability to better detect and classify falls. A brief presentation of wearables in one pathological group is presented, identifying appropriate work for researchers in other cohorts to utilise. Suggestions for how this domain needs to progress are also summarised

Motion Compatibility for Indoor Localization

Indoor localization -- a device's ability to determine its location within an extended indoor environment -- is a fundamental enabling capability for mobile context-aware applications. Many proposed applications assume localization information from GPS, or from WiFi access points. However, GPS fails indoors and in urban canyons, and current WiFi-based methods require an expensive, and manually intensive, mapping, calibration, and configuration process performed by skilled technicians to bring the system online for end users. We describe a method that estimates indoor location with respect to a prior map consisting of a set of 2D floorplans linked through horizontal and vertical adjacencies. Our main contribution is the notion of "path compatibility," in which the sequential output of a classifier of inertial data producing low-level motion estimates (standing still, walking straight, going upstairs, turning left etc.) is examined for agreement with the prior map. Path compatibility is encoded in an HMM-based matching model, from which the method recovers the user s location trajectory from the low-level motion estimates. To recognize user motions, we present a motion labeling algorithm, extracting fine-grained user motions from sensor data of handheld mobile devices. We propose "feature templates," which allows the motion classifier to learn the optimal window size for a specific combination of a motion and a sensor feature function. We show that, using only proprioceptive data of the quality typically available on a modern smartphone, our motion labeling algorithm classifies user motions with 94.5% accuracy, and our trajectory matching algorithm can recover the user's location to within 5 meters on average after one minute of movements from an unknown starting location. Prior information, such as a known starting floor, further decreases the time required to obtain precise location estimate

Context Awareness for Navigation Applications

This thesis examines the topic of context awareness for navigation applications and asks the question, “What are the benefits and constraints of introducing context awareness in navigation?” Context awareness can be defined as a computer’s ability to understand the situation or context in which it is operating. In particular, we are interested in how context awareness can be used to understand the navigation needs of people using mobile computers, such as smartphones, but context awareness can also benefit other types of navigation users, such as maritime navigators. There are countless other potential applications of context awareness, but this thesis focuses on applications related to navigation. For example, if a smartphone-based navigation system can understand when a user is walking, driving a car, or riding a train, then it can adapt its navigation algorithms to improve positioning performance. We argue that the primary set of tools available for generating context awareness is machine learning. Machine learning is, in fact, a collection of many different algorithms and techniques for developing “computer systems that automatically improve their performance through experience” [1]. This thesis examines systematically the ability of existing algorithms from machine learning to endow computing systems with context awareness. Specifically, we apply machine learning techniques to tackle three different tasks related to context awareness and having applications in the field of navigation: (1) to recognize the activity of a smartphone user in an indoor office environment, (2) to recognize the mode of motion that a smartphone user is undergoing outdoors, and (3) to determine the optimal path of a ship traveling through ice-covered waters. The diversity of these tasks was chosen intentionally to demonstrate the breadth of problems encompassed by the topic of context awareness. During the course of studying context awareness, we adopted two conceptual “frameworks,” which we find useful for the purpose of solidifying the abstract concepts of context and context awareness. The first such framework is based strongly on the writings of a rhetorician from Hellenistic Greece, Hermagoras of Temnos, who defined seven elements of “circumstance”. We adopt these seven elements to describe contextual information. The second framework, which we dub the “context pyramid” describes the processing of raw sensor data into contextual information in terms of six different levels. At the top of the pyramid is “rich context”, where the information is expressed in prose, and the goal for the computer is to mimic the way that a human would describe a situation. We are still a long way off from computers being able to match a human’s ability to understand and describe context, but this thesis improves the state-of-the-art in context awareness for navigation applications. For some particular tasks, machine learning has succeeded in outperforming humans, and in the future there are likely to be tasks in navigation where computers outperform humans. One example might be the route optimization task described above. This is an example of a task where many different types of information must be fused in non-obvious ways, and it may be that computer algorithms can find better routes through ice-covered waters than even well-trained human navigators. This thesis provides only preliminary evidence of this possibility, and future work is needed to further develop the techniques outlined here. The same can be said of the other two navigation-related tasks examined in this thesis

Examining sensor-based physical activity recognition and monitoring for healthcare using Internet of Things: A systematic review.

Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Monitoring (PARM) have been considered as a key paradigm for smart healthcare. Traditional methods for PARM focus on controlled environments with the aim of increasing the types of identifiable activity subjects complete and improving recognition accuracy and system robustness by means of novel body-worn sensors or advanced learning algorithms. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to open and connected uncontrolled environments by connecting heterogeneous cost-effective wearable devices and mobile apps. Little is currently known about whether traditional PARM technologies can tackle the new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand the use of IoT technologies in PARM studies, this paper will give a systematic review, critically examining PARM studies from a typical IoT layer-based perspective. It will firstly summarize the state-of-the-art in traditional PARM methodologies as used in the healthcare domain, including sensory, feature extraction and recognition techniques. The paper goes on to identify some new research trends and challenges of PARM studies in the IoT environments, and discusses some key enabling techniques for tackling them. Finally, this paper consider some of the successful case studies in the area and look at the possible future industrial applications of PARM in smart healthcare