The electrochemical enzymatic electrodes dominate the world market for blood glucose monitoring devices for controlling, as well as reducing the detrimental effects of diabetes. However, the enzymatic electrodes exhibit constraints restricting their reliance on the enzyme’s activity which can be influenced by the external, and the environmental factors such as temperature, pH, and humidity etc. However, the greater thickness of the enzyme layer hinders the performance of the glucose biosensors resulting in signal dampening or loss. In addition, the selectivity of the electrodes is affected by the interferents present in blood. Moreover, the invasive nature of the electrodes is a major problem considering the patient’s perspective. In contrast, recent research activities demonstrated that the electrochemical non-enzymatic electrodes possess huge potential for inexpensive and highly sensitive glucose monitoring devices, yet these electrodes are invasive in nature. Therefore, the purpose of this research was to fabricate electrochemical non-enzymatic non-invasive electrodes for sweat glucose monitoring devices. A very simple low-cost fabrication technique has been shown to make the facile, flexible, and inexpensive electrodes to detect sugar in sweat bio-analyte for a non-invasive glucose monitoring system using the native stable Cu oxides (CuNOx), Cu2O, layers grown on 35 µm thin Cu foils keeping under ambient conditions (25℃- and 760-mm Hg) for more than 2 years so that the oxide layers are full-grown, and fully stable. Moreover, the foils also annealed at various temperatures such as 160, 230, and 280℃ with new temperature profile for reducing the required time of growing stable oxides and producing oxides with larger crystallized structures with higher surface – to – volume ratio. The X-ray photoelectron spectroscopy (XPS) and high-resolution transmission electron microscopy (HRTEM) results supported that at 280℃ annealing temperature the surface, mostly, transformed into highly electrocatalytic CuO with larger grain sizes, crystallized structures, and the uniform layer of ~ 140 nm. The electrochemical characterization, and sensing performance of the electrodes have been done by cyclic voltammetry (CV), one of the excellent and well accepted electrochemical methods, with the 3 – electrode configuration of the potentiostat. The CuNOx sensors of having ~10 nm layer of stable Cu2O exhibited a sensitivity of 603.42 μA mM−1 cm−2, a linear range beyond the desired limit of 7.00 mM with excellent linearity (R2 = 0.9983) and a low limit of detection of 94.21 μM. In contrast, the new annealing profile has. the CuNOx sensors annealed at 280 ºC using new temperature profile provided twin calibration curves of linear ranges of 0.05 – 1.00 mM and 1.00 – 7.00 mM, that applicable for sweat and blood glucose sensing, respectively, and exhibited a sensitivity of 1795 μA mM−1 cm−2, a linear range up to the desired limit of 1.00 mM for sweat glucose sensing with excellent linearity (R2 = 0.9844), and a lower limit of detection of 135.39 μM. In addition, it has been shown that the peak electro-oxidation current of glucose sensing is linearly related with the squire root of the annealing temperature, √T. This can help to figure out the required applied annealing temperature for getting desired peak electro-oxidation current of glucose in a human health monitoring system. / Dissertation / Doctor of Philosophy (PhD)

As the nation’s infrastructure continues to age, there is a growing need for methods to safely inspect critical structures, often during operation. The failure of post-tensioned anchor rods in Tainter style flood gates presented an immediate need for new inspection capabilities for U.S. Army Corps of Engineers (USACE) managed flood control gates. In response to this need, the Sensor Integration Branch (SIB) of The U.S. Army Engineer Research and Develop Center (ERDC) developed the capability to non-destructively test (NDT) both greased and grouted cylindrical steel anchor rods using higher order guided wave ultrasonic testing. Understanding the results requires a knowledge of both guided waves and digital signal processing in order to identify the possibility of a defect. In order to both facilitate rapid defect identification and expanding ease-of-use of the equipment, the research in this thesis uses a combination of the discrete wavelet transform (DWT) and gradient boosting machine learning to build a model capable of identifying the dispersive defect responses in the rods.

As transportation infrastructure continues to age, new methods of non-contact monitoring should be evaluated and, if found suitable, employed for bridge monitoring and structural health assessment. This study highlights the use of infrasound monitoring, a geophysical technique utilizing acoustics below 20 Hz, as one possible solution for non-contact, non-line-of-sight infrastructure health monitoring. This dissertation focuses on the technique of infrasound for infrastructure monitoring (bridges are of primary interest) beginning with a literature review and an overview of current operational considerations for infrasound for infrastructure monitoring developed at the U.S. Army Engineer Research and Development Center. A meta-analysis of bridge vibrational characteristics was completed following identification of a gap in the knowledge base in this area. This completed meta-analysis compared vibrational characteristics across multiple bridge types and construction materials to determine applicability of infrasound for detection and monitoring of each bridge type. With these considerations in mind, an experimental series involving a steel, two-girder bridge in northern California was completed using infrasound to detect natural modes of the structure and validated by on-structure accelerometers. The non-contact nature of this structural assessment approach has potential to supplement traditional structural assessment techniques as affordable, remote, persistent monitoring of transportation infrastructure. Upon completion of the original experimental series, the data were used to investigate the possibility of wide area monitoring using infrasound, including possible limitations and boundaries. Overall implications for use of this technology are also discussed for a multiple infrastructure types.

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Activities of daily living (ADLs) are everyday routine tasks which provide insight into the physical and cognitive wellbeing of older adults. ADLs are commonly self-reported to clinicians, which can lead to overestimation and underestimation of a patients’ functional abilities. Remote health monitoring is an emerging field aimed at utilizing technology for monitoring ADLs remotely, improving clinical accuracy and enabling older adults to age safely within their homes. In this dissertation, we report a Smart Home platform and two indoor positioning systems (IPSs) – (i) a hybrid Bluetooth Low Energy (BLE) and radar motion sensor system and (ii) a hybrid radio-frequency identification (RFID) and infrared (IR) range-finding system for tracking occupant mobility, the primary predictor of falls among older adults. For the Smart Home platform, the design methodology and technological features were explained. As for the IPSs’, position accuracy of multiple occupants within multiple rooms of a residential apartment was evaluated. The systems were also evaluated for cost, implementation ease, and scalability, which, upon reviewing literature, were identified as key metrics for developing an IPS for enabling aging in place. Both IPSs enforced a decentralized localization architecture and performed well, achieving high localization accuracy for multiple occupants. / Thesis / Master of Applied Science (MASc) / By 2031, the number of people aged 65 and over is expected to nearly double. This population shift is concerning for healthcare providers as limited resources become increasingly constrained. Resultantly, older adults, the largest consumers of healthcare, face longer wait times and reduced quality of care. Remote health monitoring is an emerging field aimed at utilizing technology for monitoring older adults within their homes. In this thesis, we report a Smart Home platform and two indoor positioning systems (IPSs) for tracking resident mobility, the primary predictor of falls among older adults. For the Smart Home platform, the design methodology and technological features were explained. As for the IPSs’, position accuracy of multiple occupants within multiple rooms of a residential apartment was evaluated. Upon reviewing literature system cost, implementation ease, and scalability, were identified as key metrics for developing an IPS for enabling aging in place. Both IPSs performed well, achieving high localization accuracy for multiple occupants.