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

Earpiece with sensors to measure/monitor multiple physiological variables

An apparatus and method for positioning sensors relative to one another and anatomic features in a non-invasive device for measuring and monitoring multiple physiological variables from a single site uses an earpiece incorporating a shielded pulse oximetry sensor (POS) having a miniaturized set of LEDs and photosensors configured for pulse oximetry measurements in the reflectance mode and located in the earpiece so as to position the POS against a rear wall of an ear canal. The earpiece also includes a thermopile of no larger than 7 mm. in diameter located on the earpiece to so as to position the thermopile past a second turn of an external auditory meatus so as to view the tympanic membrane. The thermopile includes a reference temperature sensor attached to its base for ambient temperature compensation

Human location estimation using thermopile array sensor

Utilization of Thermopile sensor at an early stage of human detection is challenging as there are many things produce thermal heat other than human such as electrical appliances and animals. Therefrom, an algorithm for early presence detection has been developed through the study of human body temperature behaviour with respect to the room temperature. The change in non-contact detected temperature of human varied according to body parts. In an indoor room, upper parts of human body change up to 3 o C whereas lower part ranging from 0.58 o C to 1.71 o C. The average changes in temperature of human is used as a conditional set-point value in the program algorithm to detect human presence. The current position of human and its respective angle is gained when human is detected to be presented at certain pixels of sensor array. Human position estimated successfully as the developed sensory system is tested to Stand Fan

Neural Networks for Indoor Person Tracking With Infrared Sensors

Indoor localization has many pervasive applications, like energy management, health monitoring, and security. Tagless localization detects directly the human body, like passive infrared sensing, and is the most amenable to different users and use cases. We evaluate the localization and tracking performance, as well as resource and processing requirements of various neural network (NN) types using directly the data from a low resolution 16-pixel thermopile sensor array in a 3 m x 3 m room. Out of the multilayer perceptron, autoregressive, 1D-CNN, and LSTM NN architectures that we test, the latter require more resources but can accurately locate and capture best the person movement dynamics, while the 1D-CNN provides the best compromise between localization accuracy (9.6 cm RMSE) and movement tracking smoothness with the least resources, and seem more suited for embedded applications

Monitoring Indoor People Presence in Buildings Using Low-Cost Infrared Sensor Array in Doorways

We propose a device for monitoring the number of people who are physically present inside indoor environments. The device performs local processing of infrared array sensor data detecting people’s direction, which allows monitoring users’ occupancy in any space of the building and also respects people privacy. The device implements a novel real-time pattern recognition algorithm for processing data sensed by a low-cost infrared (IR) array sensor. The computed information is transferred through a Z-Wave network. On-field evaluation of the algorithm has been conducted by placing the device on top of doorways in offices and laboratory rooms. To evaluate the performance of the algorithm in varying ambient temperatures, two groups of stress tests have been designed and performed. These tests established the detection limits linked to the difference between the average ambient temperature and perturbation. For an in-depth analysis of the accuracy of the algorithm, synthetic data have been generated considering temperature ranges typical of a residential environment, different human walking speeds (normal, brisk, running), and distance between the person and the sensor (1.5 m, 5 m, 7.5 m). The algorithm performed with high accuracy for routine human passage detection through a doorway, considering indoor ambient conditions of 21–30 °C

Low-Noise Energy-Efficient Sensor Interface Circuits

Today, the Internet of Things (IoT) refers to a concept of connecting any devices on network where environmental data around us is collected by sensors and shared across platforms. The IoT devices often have small form factors and limited battery capacity; they call for low-power, low-noise sensor interface circuits to achieve high resolution and long battery life. This dissertation focuses on CMOS sensor interface circuit techniques for a MEMS capacitive pressure sensor, thermopile array, and capacitive microphone. Ambient pressure is measured in the form of capacitance. This work propose two capacitance-to-digital converters (CDC): a dual-slope CDC employs an energy efficient charge subtraction and dual comparator scheme; an incremental zoom-in CDC largely reduces oversampling ratio by using 9b zoom-in SAR, significantly improving conversion energy. An infrared gesture recognition system-on-chip is then proposed. A hand emits infrared radiation, and it forms an image on a thermopile array. The signal is amplified by a low-noise instrumentation chopper amplifier, filtered by a low-power 30Hz LPF to remove out-band noise including the chopper frequency and its harmonics, and digitized by an ADC. Finally, a motion history image based DSP analyzes the waveform to detect specific hand gestures. Lastly, a microphone preamplifier represents one key challenge in enabling voice interfaces, which are expected to play a dominant role in future IoT devices. A newly proposed switched-bias preamplifier uses switched-MOSFET to reduce 1/f noise inherently.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137061/1/chaseoh_1.pd