A Mobile Robot System for Ambient Intelligence

Over the last years, Ambient Intelligence (AmI) has been pointed out as an alternative to current practices in home care. AmI supports the concept of Ambient Assisted Living, which aims to allow older people to remain independent at their own homes for longer. The integration of a mobile robot into a database-centric platform for Ambient Assisted Living is described in this thesis. The robot serves as a rst-aid agent to respond to emergencies, such as a fall, detected by the intelligent environment. To accomplish that the robot must 1) be able to receive tasks from intelligent environment; 2) execute the task; 3) report the progress and the result of the task back to the intelligent environment. The system of the robot is built on top of the Robot Operating System, while the existing intelligent environment on a PostgreSQL database. To receive tasks from the intelligent environment, the robot maintains an active connection with the database and subscribes to specic tasks. A task, for example, is to nd a person in the environment, which includes asking if the person is doing well. To nd a person a map-based approach and a face recognition are used. The robot can interact with people in the environment using text-to-speech and speech recognition. The active connection with the database enables the robot to report back about the execution of a task and to receive new or abort tasks. As a conclusion, together with an AAL system, mobile robots can support people living alone. The system has been implemented and successfully tested at Halmstad University on a Turtlebot 2. The code is available on Github

A Human-Machine Interface Based on Eye Tracking for Controlling and Monitoring a Smart Home Using the Internet of Things

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Privacy aware human action recognition: an exploration of temporal salience modelling and neuromorphic vision sensing

Solving the issue of privacy in the application of vision-based home monitoring has emerged as a significant demand. The state-of-the-art studies contain advanced privacy protection by filtering/covering the most sensitive content, which is the identity in this scenario. However, going beyond privacy remains a challenge for the machine to explore the obfuscated data, i.e., utility. Thanks for the usefulness of exploring the human visual system to solve the problem of visual data. Nowadays, a high level of visual abstraction can be obtained from the visual scene by constructing saliency maps that highlight the most useful content in the scene and attenuate others. One way of maintaining privacy with keeping useful information about the action is by discovering the most significant region and removing the redundancy. Another solution to address the privacy is motivated by the new visual sensor technology, i.e., neuromorphic vision sensor. In this thesis, we first introduce a novel method for vision-based privacy preservation. Particularly, we propose a new temporal salience-based anonymisation method to preserve privacy with maintaining the usefulness of the anonymity domain-based data. This anonymisation method has achieved a high level of privacy compared to the current work. The second contribution involves the development of a new descriptor for human action recognition (HAR) based on exploring the anonymity domain of the temporal salience method. The proposed descriptor tests the utility of the anonymised data without referring to RGB intensities of the original data. The extracted features using our proposed descriptor have shown an improvement with accuracies of the human actions, outperforming the existing methods. The proposed method has shown improvements by 3.04%, 3.14%, 0.83%, 3.67%, and 16.71% for DHA, KTH, UIUC1, UCF sports, and HMDB51 datasets, respectively, compared to state-of-the-art methods. The third contribution focuses on proposing a new method to deal with the new neuromorphic vision domain, which has come up to the application, since the issue of privacy has been already solved by the sensor itself. The output of this new domain is exploited by further exploring the local and global details of the log intensity changes. The empirical evaluation shows that exploring the neuromorphic domain provides useful details that have demonstrated increasing accuracy rates for E-KTH, E-UCF11 and E-HMDB5 by 0.54%, 19.42% and 25.61%, respectively

Architecting Smart Home Environments for Healthcare : A Database-Centric Approach

The development of system architectures and applications for smart homes and ambient assisted living has been the main activity of a number of academic and industrial research projects around the world. Existing system architectures for smart environments usually employ different architectural styles in a multi-layer logical architecture to support the integration and interoperation of heterogeneous hardware and software technologies, which are subsequently used to provide two major functionalities: monitoring and assistance. It is also usual among existing architectures that the database management system is the most common but the least exploited architectural component, existing in the periphery of the system and devoted exclusively for data storage and retrieval. However, database technology has advanced and matured considerably over the years, and, as a result, current database management systems can be and do more. This thesis considers the hypothesis of several features of modern database management systems being employed to address functional (e.g. well-being and security monitoring, automated control, data processing) and non-functional (e.g. interoperability, extensibility, data security and privacy) requirements of smart environments, i.e. the database management system serves as a platform for smart environments. The scope of this thesis is therefore to investigate the possibility of using different features supported by database management systems to create a database-centric system architecture for the development of smart home environments and ambient assisted living. The thesis also investigates the development of applications for health monitoring and assistance: 1) a serious game for fall prevention that assists people in practicing Tai Chi at home, and 2) a non-intrusive home-based method for sleep assessment. These features are explored in this thesis to address general functional aspects of smart environments, such as monitoring, processing, coordination and control of various types of events in a given environment. Extensibility and security features and cross-platform capabilities of database management systems are employed to accommodate non-functional, but still technical, properties of smart environments, including interoperability, extensibility, portability, scalability, security and privacy. Heterogeneous technologies are integrated into the system using programming language and platform independent software resource adapters. Interoperation among integrated technologies is mediated in an active database. The feasibility of the proposed database-centric system architecture was pragmatically investigated with the development of a "smart bedroom'' demonstrator and with the implementation of a number of short-term and long-term types of services to support active aging, aging in place and ambient assisted living. In the proposed architecture, active in-database processing maintains sensitive data within the database. This increases data security and independence from external software applications for data analysis. Changes in the system are managed during runtime, which improves flexibility and avoids system downtime. The proposed system architecture was evaluated taking into account different application scenarios and heterogeneous computing platforms. As a conclusion, modern database management systems support features that can be successfully employed in a database-centric system architecture to effectively and efficiently address functional and non-functional requirements of smart environments