Computing and Information Systems - Theses

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    Accurate and efficient human activity recognition
    Cheng, Weihao ( 2018)
    Human Activity Recognition (HAR) is a promising technology which enables artificial intelligence systems to identify user's physical activities such as walking, running, and cycling. Recently, the demand for HAR is continuously increasing in pace with the rapid development of ubiquitous computing techniques. Major applications of HAR including fitness tracking, safety monitoring, and contextual recommendation have been widely applied in people's daily lives. For example, a music App on smartphones can use HAR to detect the current activity of the user and recommend activity-related songs. State-of-the-art HAR methods are based on the machine learning technique, where a classification model is trained on a dataset to infer a number of predefined activities. The data for HAR is usually in the form of time series, which can be collected by sensors such as accelerometers, microphones, and cameras. In this thesis, we mainly focus on HAR using the data from inertial sensors, such as accelerations from accelerometers. A large number of existing studies on HAR aim to obtain high recognition accuracy. However, efficiency is also an important aspect of HAR. In this thesis, we attempt to improve HAR methods for both accuracy and efficiency. Toward this goal, we first devise accurate HAR methods, and then improve the efficiency of HAR while maintaining the accuracy. More specifically, we tackle three problems. The first problem is to accurately recognize the current activity during activity transitions. Existing HAR methods train classification models based on tailored time series containing single activity. However, in practical scenarios, a piece of time series data could capture multiple interleaving activities causing activity transitions. Thus, recognition of the current activity, i.e., the most recent one, is a critical problem to investigate. The second problem is to accurately predict complex activities from ongoing observations. Many time-critical applications, such as safety monitoring, require early recognition of complex activities which are performed over a long period of time. However, without being fully observed, complex activities are hard to be recognized due to their complicated patterns. Therefore, predicting complex activities from ongoing observations is an important task to study. The third problem is to improve energy-efficiency of HAR on mobile devices while maintaining high accuracy. Many applications of HAR are based on mobile devices. However, due to the limited battery capacity, real-time HAR requires minimization of energy cost to extend the operating spans of the devices. Generally, the cost can be cut down by reducing algorithmic computations and sensing frequencies. Yet it is worth to find a maximal cost reduction while preserving a high recognition accuracy. In this thesis, we present a set of algorithms to address the proposed problems. The key contributions of the thesis can be summarized as follows: 1. We propose a method to accurately recognize the current activity in the presence of multiple activities with transitions. The method partitions a time series matching the occurring activities, where the maximum classification error of the activities is minimized. 2. We propose a method to accurately predict complex activities over time from ongoing multivariate time series. The method utilizes an action sequence model and a complex activity model, which make predictions alternately based on each other as the observed data increases. 3. We propose a method to minimize the computational cost of HAR while maintaining high recognition accuracy. The method uses a Markov Decision Process (MDP) to select an optimal subset of feature representations for ensemble classification that minimizes redundant computations. 4. We propose a method to minimize a combined measurement of sensing cost and classification error of HAR. The method uses MDP to select appropriate sensing rate to sample the incoming data points, where the sparsity of the outcome time series is ensured to preserve the recognition accuracy.
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    Indoor localization supported by landmark graph and locomotion activity recognition
    Gu, Fuqiang ( 2018)
    Indoor localization is important for a variety of applications such as location-based services, mobile social networks, and emergency response. Although a number of indoor localization systems have been proposed in recent years, they have different limitations in terms of accuracy, cost, coverage, complexity, and applicability. In order to achieve a higher accuracy with relatively low cost, hybrid methods combining multiple positioning techniques have been used. However, hybrid methods usually require an infrastructure of beacons or transmitters, which may not be available in many environments or it may be available at a high cost. Spatial knowledge is available in many scenarios, and can be used to assist localization without additional cost. Landmarks are one of the spatial constraints useful for indoor localization. Indoor localization systems that use landmarks have been proposed in the literature, but they are usually applied for tracking robots by using laser scanners or/and cameras. The systems using these devices are economically or/and computationally expensive, and hence are not suitable for indoor pedestrian localization. Although landmarks based on the built-in smartphone sensors are also used in some indoor localization systems, the performance of these systems relies highly on the completeness of landmarks. A mismatch of landmarks may cause a large localization error and even lead to the failure of localization. The advent of sensor-equipped smart devices has enabled a variety of activity recognition and inference, including locomotion (e.g., walking, running, standing). The sensors built in the smart devices can capture the intensity and duration of activity, and even are able to sense the activity context. Such information can be used to enhance the localization accuracy or reduce the energy consumption and deployment cost while maintaining the accuracy. For example, the knowledge of locomotion activities can be used to optimize the step length estimation of people, which will contribute to the improvement of localization accuracy. However, it is challenging to precisely recognize activities related to indoor localization with smartphones. The hypothesis of this research is that accurate and reliable indoor localization can be achieved by fusing smartphone sensor data with locomotion activities and a landmark graph. This hypothesis is tested using the novel algorithms proposed and developed in this research. The proposed framework consists of four main phases, namely recognizing locomotion activities related to indoor localization from sensor data, improving the accuracy of step counting and step length estimation for pedestrian dead reckoning method, developing a landmark graph-based indoor localization method, and implementing quick WiFi fingerprint collection. The main contributions of this research are as follows. First, a novel method is proposed for locomotion activity recognition by automatically learning useful features from sensor data using a deep learning model. Second, robust and accurate algorithms are proposed for step counting and step length estimation to improve the performance of pedestrian dead reckoning, which will be fused with spatial information. Third, the concept of sensory landmarks and the landmark graph is proposed, and a landmark graph-based method is developed for indoor localization. Fourth, a practical, fast, and reliable fingerprint collection method is designed, which uses the landmark graph-based localization method for automatically estimating the location of reference points used to associate the collected fingerprints.