Search

Accurate assessment of a child’s health is critical for appropriate allocation of medical resources and timely delivery of healthcare in both primary care (GP consultations) and secondary care (ED consultations). Serious illnesses such as meningitis and pneumonia account for 20% of deaths in childhood and require early recognition and treatment in order to maximize the chances of survival of affected children. Due to time constraints, poorly defined normal ranges, difficulty in achieving accurate readings and the difficulties faced by clinicians in interpreting combinations of vital signs, vital signs are rarely measured in primary care and their utility is limited in emergency departments. This thesis aims to develop a monitoring and data fusion system, to be used in both primary care and emergency department settings during the initial assessment of children suspected of having a serious infection. The proposed system relies on the photoplethysmogram (PPG) which is routinely recorded in different clinical settings with a pulse oximeter using a small finger probe. The most difficult vital sign to measure accurately is respiratory rate which has been found to be predictive of serious infection. An automated method is developed to estimate the respiratory rate from the PPG waveform using both the amplitude modulation caused by changes in thoracic pressure during the respiratory cycle and the phenomenon of respiratory sinus arrhythmia, the heart rate variability associated with respiration. The performance of such automated methods deteriorates when monitoring children as a result of frequent motion artefact. A method is developed that automatically identifies high-quality PPG segments mitigating the effects of motion on the estimation of respiratory rate. In the final part of the thesis, the four vital signs (heart rate, temperature, oxygen saturation and respiratory rate) are combined using a probabilistic framework to provide a novelty score for ranking various diagnostic groups, and predicting the severity of infection in two independent data sets from two different clinical settings.

This work explores the use of a commodity ultra-wideband (UWB) radar based device to detect breathing rate for health monitoring applications. Health monitoring devices observe physiological signals to detect medical conditions. We focus on capturing the small mechanical movements caused by breathing. This is traditionally done via a strain gauge worn around the chest or stomach, but these systems limit user movement. Contactless systems provide a unique design that allows free user movement by eliminating all direct contact with the user. Additionally, these systems have the potential to support full health monitoring in a Smart Built Environment (SBE). In this work, a comparative evaluation is performed on a commodity UWB radar based device, the Walabot, to determine the accuracy and possible health monitoring applications. Based on results from a systematic review, six research challenges were identified: (1) high cost, functional limitations based on the user's (2) location, (3) orientation, and (4) movement, (5) dependency on system hardware placement, and (6) vulnerabilities in signal processing methods. A comparative evaluation was designed to test the Walabot against a medical grade wearable system in the context of these research challenges. The data was processed using two breathing rate derivation techniques: Fast Fourier Transformation (FFT) and Peak Detection. Results suggest great potential for the Walabot coupled with the FFT technique. However, the system requires further testing to address all of the research challenges. Overall, this work provides important steps toward using the Walabot in health monitoring applications.

Several researchers have considered the relationship between total inward leakage (TIL) and other parameters such as sealing condition, particle concentration, type of particulate material breathing parameters, etc., however, there is dearth of information on the effects of varying particle concentration on the penetration efficiency of filtering facepiece respirators (FFRs), hence, this study aims to study the effect of varying NaCl particle concentration, sealing condition and breathing rate on the TIL as a result of the filtering efficiency of the filters. To achieve this, we followed the sampling procedure used by He et al., 2014. Four different sealing conditions (SCs) namely; fully sealed, fully opened, chin sealed, and cheek & chin sealed was used. NaCl aerosol particle was used at varying concentration levels of 100,000 (100K), 200,000 (200K) and 400,000 (400K). Also, we used mean inspiratory flow rates of 15LPM, 30LPM, 55LPM and 85LPM which we refer to as breathing rate throughout this study. Visual presentation of result was done with Python 3.8 and data analysis was carried out using R version 3.6.0 (2019-04-26). The statistical model statement follows the equation TILSC = βo + β1B + β2C + β3BC + £. where B is the breathing rate at the 4 MIF, 15, 30, 55, and 85; C is concentration and BC is the interaction between Breathing flow rate and the concentration. TILsc is the total inward leakage at the different sealing conditions The result shows that the variables have effect on the TIL for both fully opened and cheek and chin sealed condition with or without interaction between the variables at 0.05 confidence level. For fully sealed and chin sealed condition, the variables have no significant effect on the mean TIL response when interaction between variables was considered and when interaction between variables was not considered. In conclusion, there is statistically significance difference for mean TIL response with the change in sealing conditions, varying concentrations of NaCl particle and breathing rates. This study showed that in studying the effect of varying NaCl particle concentration, sealing conditions and breathing rate on the TIL, it is only under both fully opened and cheek and chin sealed conditions that the mean TIL response is significantly affected by the factors in consideration. Hence, workers should not expose their nose region at any time to prevent exposure.

Personalized health-care is trending and individuals tend to wear sensors in order to record their own health data. As a part of this trend, any redundancy in the data captured by wearable sensors must be exploited to reduce the number of devices one may wear. In this thesis, we work with a device which senses breathing and pulse through pressure tube and pulse oximetry, respectively. Extracting the dependency between these two measurements, we approximately predict the breathing rate by first reconstructing the breathing signal using the data coming from the finger-tip sensor, and then detecting the peaks in the reconstructed signal. For breathing signal reconstruction, two different techniques are used: (1) applying low- and high-pass filters on the pulse signal (2) training a neural network on a prepared dataset. Our experiments show that neural networks have a better performance comparing to filters in reconstructing the breathing signal, and consequently, predicting the breathing rate.