MEMS Accelerometers

Micro-electro-mechanical system (MEMS) devices are widely used for inertia, pressure, and ultrasound sensing applications. Research on integrated MEMS technology has undergone extensive development driven by the requirements of a compact footprint, low cost, and increased functionality. Accelerometers are among the most widely used sensors implemented in MEMS technology. MEMS accelerometers are showing a growing presence in almost all industries ranging from automotive to medical. A traditional MEMS accelerometer employs a proof mass suspended to springs, which displaces in response to an external acceleration. A single proof mass can be used for one- or multi-axis sensing. A variety of transduction mechanisms have been used to detect the displacement. They include capacitive, piezoelectric, thermal, tunneling, and optical mechanisms. Capacitive accelerometers are widely used due to their DC measurement interface, thermal stability, reliability, and low cost. However, they are sensitive to electromagnetic field interferences and have poor performance for high-end applications (e.g., precise attitude control for the satellite). Over the past three decades, steady progress has been made in the area of optical accelerometers for high-performance and high-sensitivity applications but several challenges are still to be tackled by researchers and engineers to fully realize opto-mechanical accelerometers, such as chip-scale integration, scaling, low bandwidth, etc

Neural Networks for Indoor Human Activity Reconstructions

Low cost, ubiquitous, tagless, and privacy aware indoor monitoring is essential to many existing or future applications, such as assisted living of elderly persons. We explore how well different types of neural networks in basic configurations can extract location and movement information from noisy experimental data (with both high-pitch and slow drift noise) obtained from capacitive sensors operating in loading mode at ranges much longer that the diagonal of their plates. Through design space exploration, we optimize and analyze the location and trajectory tracking inference performance of multilayer perceptron (MLP), autoregressive feedforward, 1D Convolutional (1D-CNN), and Long-Short Term Memory (LSTM) neural networks on experimental data collected using four capacitive sensors with 16 cm x 16 cm plates deployed on the boundaries of a 3 m x 3 m open space in our laboratory. We obtain the minimum error using a 1D-CNN [0.251 m distance Root Mean Square Error (RMSE) and 0.307 m Average Distance Error (ADE)] and the smoothest trajectory inference using an LSTM, albeit with higher localization errors (0.281 m RMSE and 0.326 m ADE). 1D Convolutional and window-based neural networks have best inference accuracy and smoother trajectory reconstruction. LSTMs seem to infer best the person movement dynamics

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

Detection of human movement by near field imaging : development of a novel method and applications

The proportion of senior citizens is increasing, which requires more resources in the care services. The effectiveness of these services is proposed to be increased by remote monitoring of senior citizens living at home or in nursing homes. The monitoring can be performed with various types of sensors, but the solution presented here incorporates most of the functionalities found in related work in one comprehensive system. The system that was developed uses electric field sensing to detect human presence and movement. Falls and the vital functions of a fallen person can also be extracted from the signals. The sensor arrangement consists of a matrix of thin planar electrodes under the floor surface, which makes the system completely undetectable and discreet. It is not disturbed by shading or darkness and does not require a lot of computing power. Computer vision does not enjoy these advantages. Furthermore, no devices need to be worn and no batteries need to be charged, as with systems based on transponders worn by the subject. If identification is required, the system developed in this work does not rule out the use of transponders. The impedances of the electrodes are measured using a tuned transformer and a phase-sensitive detector. A signal-to-noise ratio of 37 dB has been achieved with this structure. The mean positioning error when observing people who are walking is 21 cm. Multiple people can be discriminated with a 90% certainty if the distance between them is 78 cm. The sensitivity and specificity in fall detection have been found to be 91% and 91%, respectively. The cardiac activity and respiration are clearly visible when a person lies prone or supine on the floor. A capacitive radio frequency identification (RFID) tag in a shoe was developed for person identification. The system developed here has been installed in a large nursing home. The nurses have indicated their satisfaction in a comprehensive questionnaire, which was conducted by a representative of the nurses. Positive feedback has also been obtained from a senior person living alone and from his family members

Machine Learning Techniques for Device-Free Indoor Person Tracking

L'abstract è presente nell'allegato / the abstract is in the attachmen

Measuring moisture content of biofilter media using capacitance

A biofilter operates by passing contaminated air through a moist medium like woodchips, saw dust, soil or artificial material. Maintaining moisture content within this medium is critical for biofilter operation. To date, most biofilters use a timer system and sprinklers to apply water to the medium which may lead to under-watering or over-watering. The moisture content of the filter medium in small and in research biofilters has been controlled gravimetrically using load cells. However, for full scale application of biofilters, this technique would be overly expensive and impractical. Some research is being undertaken to develop capacitor plates to monitor moisture levels within the biofilter medium. This technique shows promise and needs further development. This project will reviews the literature on biofilters and biofilter control methods, and also on capacitance-based and other possible moisture measurement techniques. Based on this research, designs are developed for the measurement of the moisture content of the selected biofilter media such as wood chips and soil. It is concluded that the completion of this project has demonstrated the advantages of using the capacitive sensor as the basis for the design and proved the feasibility of the capacitive sensing system in determining the moisture content of the biofilter media

Capacitive Sensing and Communication for Ubiquitous Interaction and Environmental Perception

During the last decade, the functionalities of electronic devices within a living environment constantly increased. Besides the personal computer, now tablet PCs, smart household appliances, and smartwatches enriched the technology landscape. The trend towards an ever-growing number of computing systems has resulted in many highly heterogeneous human-machine interfaces. Users are forced to adapt to technology instead of having the technology adapt to them. Gathering context information about the user is a key factor for improving the interaction experience. Emerging wearable devices show the benefits of sophisticated sensors which make interaction more efficient, natural, and enjoyable. However, many technologies still lack of these desirable properties, motivating me to work towards new ways of sensing a user's actions and thus enriching the context. In my dissertation I follow a human-centric approach which ranges from sensing hand movements to recognizing whole-body interactions with objects. This goal can be approached with a vast variety of novel and existing sensing approaches. I focused on perceiving the environment with quasi-electrostatic fields by making use of capacitive coupling between devices and objects. Following this approach, it is possible to implement interfaces that are able to recognize gestures, body movements and manipulations of the environment at typical distances up to 50cm. These sensors usually have a limited resolution and can be sensitive to other conductive objects or electrical devices that affect electric fields. The technique allows for designing very energy-efficient and high-speed sensors that can be deployed unobtrusively underneath any kind of non-conductive surface. Compared to other sensing techniques, exploiting capacitive coupling also has a low impact on a user's perceived privacy. In this work, I also aim at enhancing the interaction experience with new perceptional capabilities based on capacitive coupling. I follow a bottom-up methodology and begin by presenting two low-level approaches for environmental perception. In order to perceive a user in detail, I present a rapid prototyping toolkit for capacitive proximity sensing. The prototyping toolkit shows significant advancements in terms of temporal and spatial resolution. Due to some limitations, namely the inability to determine the identity and fine-grained manipulations of objects, I contribute a generic method for communications based on capacitive coupling. The method allows for designing highly interactive systems that can exchange information through air and the human body. I furthermore show how human body parts can be recognized from capacitive proximity sensors. The method is able to extract multiple object parameters and track body parts in real-time. I conclude my thesis with contributions in the domain of context-aware devices and explicit gesture-recognition systems

Ubiquitous computing and natural interfaces for environmental information

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do Grau de Mestre em Engenharia do Ambiente, perfil Gestão e Sistemas AmbientaisThe next computing revolution‘s objective is to embed every street, building, room and object with computational power. Ubiquitous computing (ubicomp) will allow every object to receive and transmit information, sense its surroundings and act accordingly, be located from anywhere in the world, connect every person. Everyone will have the possibility to access information, despite their age, computer knowledge, literacy or physical impairment. It will impact the world in a profound way, empowering mankind, improving the environment, but will also create new challenges that our society, economy, health and global environment will have to overcome. Negative impacts have to be identified and dealt with in advance. Despite these concerns, environmental studies have been mostly absent from discussions on the new paradigm. This thesis seeks to examine ubiquitous computing, its technological emergence, raise awareness towards future impacts and explore the design of new interfaces and rich interaction modes. Environmental information is approached as an area which may greatly benefit from ubicomp as a way to gather, treat and disseminate it, simultaneously complying with the Aarhus convention. In an educational context, new media are poised to revolutionize the way we perceive, learn and interact with environmental information. cUbiq is presented as a natural interface to access that information

New Approach of Indoor and Outdoor Localization Systems

Accurate determination of the mobile position constitutes the basis of many new applications. This book provides a detailed account of wireless systems for positioning, signal processing, radio localization techniques (Time Difference Of Arrival), performances evaluation, and localization applications. The first section is dedicated to Satellite systems for positioning like GPS, GNSS. The second section addresses the localization applications using the wireless sensor networks. Some techniques are introduced for localization systems, especially for indoor positioning, such as Ultra Wide Band (UWB), WIFI. The last section is dedicated to Coupled GPS and other sensors. Some results of simulations, implementation and tests are given to help readers grasp the presented techniques. This is an ideal book for students, PhD students, academics and engineers in the field of Communication, localization & Signal Processing, especially in indoor and outdoor localization domains