Device-free indoor localisation with non-wireless sensing techniques : a thesis by publications presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Electronics and Computer Engineering, Massey University, Albany, New Zealand

Global Navigation Satellite Systems provide accurate and reliable outdoor positioning to support a large number of applications across many sectors. Unfortunately, such systems do not operate reliably inside buildings due to the signal degradation caused by the absence of a clear line of sight with the satellites. The past two decades have therefore seen intensive research into the development of Indoor Positioning System (IPS). While considerable progress has been made in the indoor localisation discipline, there is still no widely adopted solution. The proliferation of Internet of Things (IoT) devices within the modern built environment provides an opportunity to localise human subjects by utilising such ubiquitous networked devices. This thesis presents the development, implementation and evaluation of several passive indoor positioning systems using ambient Visible Light Positioning (VLP), capacitive-flooring, and thermopile sensors (low-resolution thermal cameras). These systems position the human subject in a device-free manner (i.e., the subject is not required to be instrumented). The developed systems improve upon the state-of-the-art solutions by offering superior position accuracy whilst also using more robust and generalised test setups. The developed passive VLP system is one of the first reported solutions making use of ambient light to position a moving human subject. The capacitive-floor based system improves upon the accuracy of existing flooring solutions as well as demonstrates the potential for automated fall detection. The system also requires very little calibration, i.e., variations of the environment or subject have very little impact upon it. The thermopile positioning system is also shown to be robust to changes in the environment and subjects. Improvements are made over the current literature by testing across multiple environments and subjects whilst using a robust ground truth system. Finally, advanced machine learning methods were implemented and benchmarked against a thermopile dataset which has been made available for other researchers to use

Context-aware management of multi-device services in the home

MPhilMore and more functionally complex digital consumer devices are becoming embedded or scattered throughout the home, networked in a piecemeal fashion and supporting more ubiquitous device services. For example, activities such as watching a home video may require video to be streamed throughout the home and for multiple devices to be orchestrated and coordinated, involving multiple user interactions via multiple remote controls. The main aim of this project is to research and develop a service-oriented multidevice framework to support user activities in the home, easing the operation and management of multi-device services though reducing explicit user interaction. To do this, user contexts i.e., when and where a user activity takes place, and device orchestration using pre-defined rules, are being utilised. A service-oriented device framework has been designed in four phases. First, a simple framework is designed to utilise OSGi and UPnP functionality in order to orchestrate simple device operation involving device discovery and device interoperability. Second, the framework is enhanced by adding a dynamic user interface portal to access virtual orchestrated services generated through combining multiple devices. Third the framework supports context-based device interaction and context-based task initiation. Context-aware functionality combines information received from several sources such as from sensors that can sense the physical and user environment, from user-device interaction and from user contexts derived from calendars. Finally, the framework supports a smart home SOA lifecycle using pre-defined rules, a rule engine and workflows

Smart-object based reasoning system for indoor acoustic profiling of elderly inhabitants

Many countries are facing significant challenges in relation to providing adequate care for their elderly citizens. The roots of these issues are manifold, but include changing demographics, changing behaviours, and a shortage of resources. As has been witnessed in the health sector and many others in society, technology has much to offer in terms of supporting people’s needs. This paper explores the potential for ambient intelligence to address this challenge by creating a system that is able to passively monitor the home environment, detecting abnormal situations which may indicate that the inhabitant needs help. There are many ways that this might be achieved, but in this paper, we will describe our investigation into an approach involving unobtrusively ’listening’ to sound patterns within the home, which classifies these as either normal daily activities, or abnormal situations. The experimental system we built was composed of an innovative combination of acoustic sensing, artificial intelligence (AI), and the Internet-of-Things (IoT), which we argue in the paper that it provides a cost-effective approach to alerting care providers when an elderly person in their charge needs help. The majority of the innovation in our work concerns the AI in which we employ Machine Learning to classify the sound profiles, analyse the data for abnormal events, and to make decisions for raising alerts with carers. A Neural Network classifier was used to train and identify the sound profiles associated with normal daily routines within a given person’s home, signalling departures from the daily routines that were then used as templates to measure deviations from normality, which were used to make weighted decisions regarding calling for assistance. A practical experimental system was then designed and deployed to evaluate the methods advocated by this research. The methodology involved gathering pre-design and post-design data from both a professionally run residential home and a domestic home. The pre-design data gathered the views on the system design from 11 members of the residential home, using survey questionnaires and focus groups. These data were used to inform the design of the experimental system, which was then deployed in a domestic home setting to gather post-design experimental data. The experimental results revealed that the system was able to detect 84% of abnormal events, and advocated several refinements which would improve the performance of the system. Thus, the research concludes that the system represents an important advancement to the state-of-the-art and, when taken together with the refinements, represents a line of research which has the potential to deliver significant improvements to care provision for the elderly

Adaptive tracking of people and vehicles using mobile platforms

Tracking algorithms have important applications in detection of humans and vehicles for border security and other areas. For large-scale deployment of such algorithms, it is critical to provide methods for their cost- and energy-efficient realization. To this end, commodity mobile devices have significant potential for use as prototyping and testing platforms due to their low cost, widespread availability, and integration of advanced communications, sensing, and processing features. Prototypes developed on mobile platforms can be tested, fine-tuned, and demonstrated in the field and then provide reference implementations for application-specific disposable sensor node implementations that are targeted for deployment. In this paper, we develop a novel, adaptive tracking system that is optimized for energy-efficient, real-time operation on off-the-shelf mobile platforms. Our tracking system applies principles of dynamic data-driven application systems (DDDAS) to periodically monitor system operating characteristics and apply these measurements to dynamically adapt the specific classifier configurations that the system employs. Our resulting adaptive approach enables powerful optimization of trade-offs among energy consumption, real-time performance, and tracking accuracy based on time-varying changes in operational characteristics. Through experiments employing an Android-based tablet platform, we demonstrate the efficiency of our proposed tracking system design for multimode detection of human and vehicle targets.publishedVersionPeer reviewe

Control of a Bipedal Robot

In this project, we developed and modified the Anthropomorphic Robotic Movement System (ARMS) to allow for the simulation and implementation of low-energy walking, standing, and control. The robot underwent an in-depth analysis of its mechanical design, including a retrofit of the cabling system, upgrades to the series elastics, and an implementation of a mechanical gravity compensation system using springs and variable radius pulleys. The electrical system was completely overhauled with new microcontrollers and sensors for better control of the robot. A new control schema with gravity compensation was implemented which allowed the robot to stand. The combination of the mechanical, electrical, and control changes were evaluated via standing and leaning tests, and walking simulations

Climbing and Walking Robots

Nowadays robotics is one of the most dynamic fields of scientific researches. The shift of robotics researches from manufacturing to services applications is clear. During the last decades interest in studying climbing and walking robots has been increased. This increasing interest has been in many areas that most important ones of them are: mechanics, electronics, medical engineering, cybernetics, controls, and computers. Today’s climbing and walking robots are a combination of manipulative, perceptive, communicative, and cognitive abilities and they are capable of performing many tasks in industrial and non- industrial environments. Surveillance, planetary exploration, emergence rescue operations, reconnaissance, petrochemical applications, construction, entertainment, personal services, intervention in severe environments, transportation, medical and etc are some applications from a very diverse application fields of climbing and walking robots. By great progress in this area of robotics it is anticipated that next generation climbing and walking robots will enhance lives and will change the way the human works, thinks and makes decisions. This book presents the state of the art achievments, recent developments, applications and future challenges of climbing and walking robots. These are presented in 24 chapters by authors throughtot the world The book serves as a reference especially for the researchers who are interested in mobile robots. It also is useful for industrial engineers and graduate students in advanced study

Tracking Biped Motion in Pervasive Environment

Textiles are ubiquitous to humans since ages. Transistors made of silicon have made a deep impact in modern industry. A new field of research called wearable electronics integrates both these worlds to provide intelligent new services. Based on modern technologies of textile manufacturing, a carpet is embedded with a network of computing devices. One of their applications is to sense, when someone walks over them. This carpet was used to track the path a person took on his walk. When a person steps on the carpet, embedded sensors in the carpet get activated. These activations are stored at a monitoring PC as a log file. This data is processed and carefully viewed by data mining algorithms to identify hidden patterns that reveal the trails of the subject on his motion over the carpet. Different methods for validating the data mining algorithms are presented. These methods are perfected to produce an ideal reference in a format that can be directly compared with the estimated results of the algorithms. The evaluation results show a better performance for the new approach compared to the state-of-the-art technologies. Veracious testing, discussions, suggestions and their impact after implementation, are discussed in detail. The concepts used in the data mining algorithms are structurally sound and maintainable. Suggestions are given for further work on this system as whole. The footsteps of the person walking on the carpet are identified. The trajectory of walk is traced. The carpet can be used in a variety of domains. Rich examples on usage, assisted with augmented literature conclude this work