DESIGN OF A DROWNING RESCUE ALERT SYSTEM

Dating back in time, drowning has been a significant ground for death worldwide; it accounts for the third cause of unplanned death globally, with about 1.2 million cases yearly. Characteristically it affects swimmers, accident victims, children and recreational seeking individuals. Although there have been various provisions put in place from drowning in some countries, it still accounts for the primary cause of unplanned death. Eradication rather than cure has been able to minimize the number of individuals who drown generally, except in developing nations, who lack adequate educational facilities and enforcement of safety measures on the dangers of drowning, thereby making the burden of drowning to escalate. The proposed drowning rescue system aims to curb deaths from drowning by observing the rise and fall of the heart rate and blood pressure of a swimmer or non-swimmer in water and if endangered, sends signals from the wearable device attached to the wrist of the victim who maybe undergoing a neardrowning experience to the receiver or rescuer who could be a lifeguard, parent or neighbour, in order to enable the rescuer render immediate help

VoIPLoc : passive VoIP call provenance via acoustic side-channels

We propose VoIPLoc, a novel location fingerprinting technique and apply it to the VoIP call provenance problem. It exploits echo-location information embedded within VoIP audio to support fine-grained location inference. We found consistent statistical features induced by the echo-reflection characteristics of the location into recorded speech. These features are discernible within traces received at the VoIP destination, enabling location inference. We evaluated VoIPLoc by developing a dataset of audio traces received through VoIP channels over the Tor network. We show that recording locations can be fingerprinted and detected remotely with a low false-positive rate, even when a majority of the audio samples are unlabelled. Finally, we note that the technique is fully passive and thus undetectable, unlike prior art. VoIPLoc is robust to the impact of environmental noise and background sounds, as well as the impact of compressive codecs and network jitter. The technique is also highly scalable and offers several degrees of freedom terms of the fingerprintable space

Bayes’ Network and Smart Sensors – Occupancy Detection

This dissertation presents a Bayesian analysis for determining residential occupancy using inexpensive commercially available passive infrared (PIR) motion detectors, compared against two other detectors that were used to establish ground-truth. One of the ground-truth detectors was a GPS signal from a smartphone, the second was a Bluetooth key fob. Data were gathered from four residential locations, and then analyzed to determine occupancy. The occupancy data collected from the PIR sensors were compared against ground-truth to verify the results of the PIR sensor events that were collected every minute for a week. The Bayesian training data that was used to determine the prior probability used a four-week time period collected once a minute. Having established the correspondence between ground-truth and the PIR sensor events, the PIR data were then used to build Bayesian network conditional tables. Once the conditional tables were constructed, the Bayesian network results could be compiled and then compared against the ground-truth data. One analysis compared the ground-truth data against the performance of individual PIR sensors and showed that there was a low correlation between the PIR motion and occupancy. Further analyses compared the ground-truth data against the performance of various groupings of PIR sensors within each residence and showed that there was a little less correlation than the individual PIR sensors method. When Bayesian modeling was applied using historical PIR sensor data, results demonstrated an improvement in occupancy detection over the individual and grouped PIR sensor methods that were evaluated. The historical sensor data (using PIR sensor signal pulses) was successfully applied to the network, with an average of .025 ϕ correlation improvement. The historical presence data (using ground-truth data) were then applied to the same network. This step improved the ϕ correlation between the PIR sensors and ground-truth by an average of .40 over the four locations. These findings show that applying Bayesian modeling improves the accuracy of occupancy detection required for safety and efficiency, which will permit occupants to live in their homes longer. Advisor: Avery Schwe

Advanced Occupancy Measurement Using Sensor Fusion

With roughly about half of the energy used in buildings attributed to Heating, Ventilation, and Air conditioning (HVAC) systems, there is clearly great potential for energy saving through improved building operations. Accurate knowledge of localised and real-time occupancy numbers can have compelling control applications for HVAC systems. However, existing technologies applied for building occupancy measurements are limited, such that a precise and reliable occupant count is difficult to obtain. For example, passive infrared (PIR) sensors commonly used for occupancy sensing in lighting control applications cannot differentiate between occupants grouped together, video sensing is often limited by privacy concerns, atmospheric gas sensors (such as CO2 sensors) may be affected by the presence of electromagnetic (EMI) interference, and may not show clear links between occupancy and sensor values. Past studies have indicated the need for a heterogeneous multi-sensory fusion approach for occupancy detection to address the short-comings of existing occupancy detection systems. The aim of this research is to develop an advanced instrumentation strategy to monitor occupancy levels in non-domestic buildings, whilst facilitating the lowering of energy use and also maintaining an acceptable indoor climate. Accordingly, a novel multi-sensor based approach for occupancy detection in open-plan office spaces is proposed. The approach combined information from various low-cost and non-intrusive indoor environmental sensors, with the aim to merge advantages of various sensors, whilst minimising their weaknesses. The proposed approach offered the potential for explicit information indicating occupancy levels to be captured. The proposed occupancy monitoring strategy has two main components; hardware system implementation and data processing. The hardware system implementation included a custom made sound sensor and refinement of CO2 sensors for EMI mitigation. Two test beds were designed and implemented for supporting the research studies, including proof-of-concept, and experimental studies. Data processing was carried out in several stages with the ultimate goal being to detect occupancy levels. Firstly, interested features were extracted from all sensory data collected, and then a symmetrical uncertainty analysis was applied to determine the predictive strength of individual sensor features. Thirdly, a candidate features subset was determined using a genetic based search. Finally, a back-propagation neural network model was adopted to fuse candidate multi-sensory features for estimation of occupancy levels. Several test cases were implemented to demonstrate and evaluate the effectiveness and feasibility of the proposed occupancy detection approach. Results have shown the potential of the proposed heterogeneous multi-sensor fusion based approach as an advanced strategy for the development of reliable occupancy detection systems in open-plan office buildings, which can be capable of facilitating improved control of building services. In summary, the proposed approach has the potential to: (1) Detect occupancy levels with an accuracy reaching 84.59% during occupied instances (2) capable of maintaining average occupancy detection accuracy of 61.01%, in the event of sensor failure or drop-off (such as CO2 sensors drop-off), (3) capable of utilising just sound and motion sensors for occupancy levels monitoring in a naturally ventilated space, (4) capable of facilitating potential daily energy savings reaching 53%, if implemented for occupancy-driven ventilation control

Intelligent ultrasound hand gesture recognition system

With the booming development of technology, hand gesture recognition has become a hotspot in Human-Computer Interaction (HCI) systems. Ultrasound hand gesture recognition is an innovative method that has attracted ample interest due to its strong real-time performance, low cost, large field of view, and illumination independence. Well-investigated HCI applications include external digital pens, game controllers on smart mobile devices, and web browser control on laptops. This thesis probes gesture recognition systems on multiple platforms to study the behavior of system performance with various gesture features. Focused on this topic, the contributions of this thesis can be summarized from the perspectives of smartphone acoustic field and hand model simulation, real-time gesture recognition on smart devices with speed categorization algorithm, fast reaction gesture recognition based on temporal neural networks, and angle of arrival-based gesture recognition system. Firstly, a novel pressure-acoustic simulation model is developed to examine its potential for use in acoustic gesture recognition. The simulation model is creating a new system for acoustic verification, which uses simulations mimicking real-world sound elements to replicate a sound pressure environment as authentically as possible. This system is fine-tuned through sensitivity tests within the simulation and validate with real-world measurements. Following this, the study constructs novel simulations for acoustic applications, informed by the verified acoustic field distribution, to assess their effectiveness in specific devices. Furthermore, a simulation focused on understanding the effects of the placement of sound devices and hand-reflected sound waves is properly designed. Moreover, a feasibility test on phase control modification is conducted, revealing the practical applications and boundaries of this model. Mobility and system accuracy are two significant factors that determine gesture recognition performance. As smartphones have high-quality acoustic devices for developing gesture recognition, to achieve a portable gesture recognition system with high accuracy, novel algorithms were developed to distinguish gestures using smartphone built-in speakers and microphones. The proposed system adopts Short-Time-Fourier-Transform (STFT) and machine learning to capture hand movement and determine gestures by the pretrained neural network. To differentiate gesture speeds, a specific neural network was designed and set as part of the classification algorithm. The final accuracy rate achieves 96% among nine gestures and three speed levels. The proposed algorithms were evaluated comparatively through algorithm comparison, and the accuracy outperformed state-of-the-art systems. Furthermore, a fast reaction gesture recognition based on temporal neural networks was designed. Traditional ultrasound gesture recognition adopts convolutional neural networks that have flaws in terms of response time and discontinuous operation. Besides, overlap intervals in network processing cause cross-frame failures that greatly reduce system performance. To mitigate these problems, a novel fast reaction gesture recognition system that slices signals in short time intervals was designed. The proposed system adopted a novel convolutional recurrent neural network (CRNN) that calculates gesture features in a short time and combines features over time. The results showed the reaction time significantly reduced from 1s to 0.2s, and accuracy improved to 100% for six gestures. Lastly, an acoustic sensor array was built to investigate the angle information of performed gestures. The direction of a gesture is a significant feature for gesture classification, which enables the same gesture in different directions to represent different actions. Previous studies mainly focused on types of gestures and analyzing approaches (e.g., Doppler Effect and channel impulse response, etc.), while the direction of gestures was not extensively studied. An acoustic gesture recognition system based on both speed information and gesture direction was developed. The system achieved 94.9% accuracy among ten different gestures from two directions. The proposed system was evaluated comparatively through numerical neural network structures, and the results confirmed that incorporating additional angle information improved the system's performance. In summary, the work presented in this thesis validates the feasibility of recognizing hand gestures using remote ultrasonic sensing across multiple platforms. The acoustic simulation explores the smartphone acoustic field distribution and response results in the context of hand gesture recognition applications. The smartphone gesture recognition system demonstrates the accuracy of recognition through ultrasound signals and conducts an analysis of classification speed. The fast reaction system proposes a more optimized solution to address the cross-frame issue using temporal neural networks, reducing the response latency to 0.2s. The speed and angle-based system provides an additional feature for gesture recognition. The established work will accelerate the development of intelligent hand gesture recognition, enrich the available gesture features, and contribute to further research in various gestures and application scenarios

Liquid Crystal Shuttered Passive Infrared Sensors for True Presence Detection

PIR sensors, known as motion detectors, are widely used for moving occupancy detection. Made of pyroelectric materials, such as LiTaO₃, generating pyroelectric current when the received infrared radiation changes, PIR sensors only respond to the motion of occupants. This results in frequent false negative detections when stationary occupancy detection is also desired, such as occupancy-based building lighting control. To enable stationary occupancy detection, in this dissertation, we develop optical shutters to actively modulate the radiation received by the PIR sensors in the long-wave infrared (LWIR) region (8-12 µm) where human skin radiates the most. The optical shutter is made of polymer dispersed liquid crystal (PDLC) sandwiched by two germanium substrates. Each germanium substrate has an anti-reflected film on one side (the nonconductive side) to reduce the reflection. The PDLC infrared shutter, a PIR sensor, and a driving circuit forms a synchronized low-energy electronically chopped PIR (SLEEPIR) sensor module. To better improve its performance, we devised SLEEPIR sensor nodes, and formed a SLEEPIR sensor network system with advanced machine learning algorithms. The main contributions of this dissertation include: (i) modeling the SLEEPIR output as a function of the effective modulation, the response time of the PDLC shutter, and the time constants of the PIR sensor; (ii) quantifying the impact of the driving voltage, the mass ratio, the cell gap, and the cooling rate on the effective modulation and the response time of the PDLC shutter to obtain the optimal driving voltage and fabrication conditions that maximize SLEEPIR module’s output; and (iii) experimental validation of the SLEEPIR sensor nodes for presence detection in the lab and uncontrolled environment settings