Heat-related illnesses and deaths are significant public health problems. Extreme heat is the No.1 deadliest form of weather on average in 1990-2019 in the US according to the National Weather Service. Measurements and forecasts made at regional weather stations are a common data source of Heatwave Early Warning Systems. However, regional weather stations provide inaccurate estimates of the heat index that people experience in different microclimates. Introducing a direct measurement of heat index experienced by individuals via wearable sensors will allow more accurate exposure assessment and identification of factors associated with dangerous exposures to extreme heat. The goal of this dissertation is to characterize the individually experienced heat index exposure via wearable sensors in an urban and a rural location in summer in a southern part of the United States. In the first study, 51 outdoor workers in Birmingham, Alabama wore a small thermometer attached to their shoe. Their occupational Wet Bulb Globe Temperatures (WBGT), a comprehensive heat exposure index, was estimated from either temperature from the shoe thermometers or nearby weather stations. In the second and third studies, 88 urban participants and 89 rural participants completed a seven-day intervention where they performed normal activity on Days 1-2 and spent an additional 30 minutes outdoors daily on Days 3-7. Participants wore a small thermometer attached to the shoe and a pedometer at their waist to track steps. Neighborhood hygrometers/thermometers were deployed close to participants' homes to measure neighborhood level heat indexes. In the fourth study, we conducted a phone survey including 101 participants in the same urban and rural locations to examine how their heat-health behaviors changed due to COVID-19 and high profiles of police brutality cases in Summer 2020 compared to previous summers. The results demonstrated that (1) a wearable thermometer on the shoe was a feasible way to measure individually experienced temperatures; (2) among outdoor workers, WBGT from shoe thermometer temperatures estimated more hours in dangerous exposure categories and recommended more protective work-rest schedules compared to WBGT from weather station temperatures; (3) neighborhood level heat indexes improved the prediction of individually experienced heat indexes compared to weather station data alone; (4) rural participants experienced higher heat index exposures than urban participants, after accounting for ambient conditions; (5) spending a small amount of additional time outdoors was a feasible and effective intervention where participants walked more steps and had lower individually experienced heat indexes during the intervention days compared to baseline days; (6) a significantly lower percent of participants reported they would use public cooling centers in Summer 2020 compared to previous summers. Taken together, the results of these studies identified methods for more accurate heat exposure assessment and its application in monitoring heat-safety while promoting physical activity via time spent outdoors in the summer. Future work could incorporate physiological response monitoring linked to simultaneous individually experienced heat exposure to further characterize exposure-response relationships across different populations. Additionally, a longer intervention and more advanced wearable devices such Fitbit, Apple Watches could be used to monitor sustainability of the intervention and intervention benefits beyond short term increases in physical activity, respectively. / Doctor of Philosophy / Extreme high temperatures/humidity can bring dangerous adverse effects in people. Extreme heat is on average the deadliest form of weather in 1990-2019 in the US estimated by National Weather Service. Heatwave Early Warning Systems are introduced to closely monitor extreme heat events, estimate the magnitude of health consequences due to extreme heat, send warning messages to vulnerable populations, and trigger response plans to reduce the dangerous health effects of heat. Heatwave Early Warning Systems generally rely on the measurement and forecasts from regional weather stations. However, the temperature/humidity measurements made at weather stations can be different from the temperature/humidity people experience. People can live far away from weather stations and they move through indoor and outdoor locations, where weather station measurements will not represent temperatures experienced, particularly in climate-controlled indoor settings. Therefore, we recruited participants in an urban and a rural location and had each participant wear a small thermometer clipped to their shoe to directly measure the temperature they experienced as they went about their normal activities. In the first study, 51 outdoor workers wore this small thermometer on their shoe at work. We calculated a comprehensive heat exposure index from either the shoe thermometer temperatures or nearby weather station temperatures. In the second and third studies, 88 urban participants and 89 rural participants completed a seven-day intervention where they performed normal activities on Days 1-2 and spent an additional 30 minutes outdoors daily on Days 3-7. Participants wore the small thermometer clipped to the shoe and a pedometer at their waist to track how many steps they walked. We placed temperature/humidity sensors close to participants' homes to take measurements at a neighborhood level. In the fourth study, we conducted a phone survey including 101 participants in the same urban and rural locations to examine whether they had different cooling methods due to the COVID-19 pandemic and high profiles of police brutality cases in Summer 2020 compared to previous summers. The results demonstrated that (1) a small thermometer clipped on the shoe was a feasible way to measure temperatures at the individual level; (3) among outdoor workers, the comprehensive heat exposure index using temperatures from the shoe thermometers estimated more hours when outdoor workers were at a risk of dangerous exposure to extreme heat, and it recommended more rest time for workers to cool off compared to using weather station temperatures alone; (3) neighborhood level temperature/humidity was more representative of the temperatures recorded from thermometers on the shoe compared to nearby weather stations; (4) rural participants experienced higher temperature/humidity than urban participants, even when their nearby weather station temperature measurements were the same; (5) spending a small amount of additional time outdoors is a feasible and effective intervention where participants walked more steps and experienced lower temperature/humidity during the intervention days compared to baseline days; (6) a smaller number of participants reported they would use public cooling centers/spaces (e.g., air-conditioned library, air-conditioned churches, waterparks) to cool down due to fear of contracting COVID-19 and safety concerns brought by the high profiles of police brutality cases in Summer 2020 compared to previous summers. Taken together, the results of these studies showed that the wearable thermometers clipped on the shoe could provide more accurate assessment of temperatures experienced by participants compared to weather stations. This method could be used in future outdoor time interventions to monitor and ensure participants safely spend time outdoors while minimizing the risk of heat-related illness. In future work, more advanced sensors (e.g., Fitbit, Apple Watch) can be worn by participants to measure physiological responses across different temperatures experienced. Additionally, a longer intervention time can be used to test if participants would continue to spend additional time outdoors.

Internet of Things is expanding to the areas such as healthcare, home management, industrial, agriculture, and becoming pervasive in our life, resulting in improved efficiency, accuracy and economic benefits. Smart sensors with embedded interfacing integrated circuits (ICs) are important enablers, hence, variety of smart sensors are required. However, each type of sensor requires specific interfacing chips, which divides the huge market of sensors’ interface chips into lots of niche markets, resulting in high develop cost and long time-to-market period for each type. Software defined sensor is regarded as a promising solution, which is expected to use a flexible interface platform to cover different sensors, deliver specificity through software programming, and integrate easily into the Internet of Things. In this work, research is carried out on the design and implementations of ultra low power analog interface circuits toward software defined sensors for healthcare services based on Internet of Things.    This thesis first explores architectures and circuit techniques for energy-efficient and flexible analog to digital conversion. A time-spreading digital calibration, to calibrate the errors due to finite gain and capacitor mismatch in multi-bit/stage pipelined converters, is developed with short convergence time. The effectiveness of the proposed technique is demonstrated with intensive simulations. Two novel circuit level techniques, which can be combined with digital calibration techniques to further improve the energy efficiency of the converters, are also presented. One is the Common-Mode-Sensing-and-Input-Interchanging (CSII) operational-transconductance-amplifier (OTA) sharing technique to enable eliminating potential memory effects. The other is a workload-balanced multiplying digital-to-analog converter (MDAC) architecture to improve the settling efficiency of a high linear multi-bit stage. Two prototype converters have been designed and fabricated in 0.13 μm CMOS technology. The first one is a 14 bit 50 MS/s digital calibrated pipelined analog to digital converter that employs the workload-balanced MDAC architecture and time-spreading digital calibration technique to achieve improved power-linearity tradeoff. The second one is a 1.2 V 12 bit 5~45 MS/s speed and power-scalable ADC incorporating the CSII OTA-sharing technique, sample-and-hold-amplifier-free topology and adjustable current bias of the building blocks to minimize the power consumption. The detailed measurement results of both converters are reported and deliver the experimental verification of the proposed techniques.     Secondly, this research investigates ultra-low-power analog front-end circuits providing programmability and being suitable for different types of sensors. A pulse-width- -modulation-based architecture with a folded reference is proposed and proven in a 0.18 μm technology to achieve high sensitivity and enlarged dynamic range when sensing the weak current signals. A 8-channel bio-electric sensing front-end, fabricated in a 0.35 μm CMOS technology is also presented that achieves an input impedance of 1 GΩ, input referred noise of 0.97 Vrms and common mode rejection ratio of 114 dB. With the programmable gain and cut-off frequency, the front-end can be configured to monitor for long-term a variety of bio-electric signals, such as electrooculogram (EOG), electromyogram (EMG), electroencephalogram (EEG) and electrocardiogram (ECG) signals. The proposed front-end is integrated with dry electrodes, a microprocessor and wireless link to build a battery powered E-patch for long-term and continuous monitoring. In-vivo test results with dry electrodes in the field trials of sitting, standing, walking and running slowly, show that the quality of ECG signal sensed by the E-patch satisfies the requirements for preventive cardiac care.    Finally, a wireless multimodal bio-electric sensor system is presented. Enabled by a customized flexible mixed-signal system on chip (SoC), this bio-electric sensor system is able to be configured for ECG/EMG/EEG recording, bio-impedance sensing, weak current stimulation, and other promising functions with biofeedback. The customized SoC, fabricated in a 0.18 μm CMOS technology, integrates a tunable analog front-end, a 10 bit ADC, a 14 bit sigma-delta digital to current converter, a 12 bit digital to voltage converter, a digital accelerator for wavelet transformation and data compression, and a serial communication protocol. Measurement results indicate that the SoC could support the versatile bio-electric sensor to operate in various applications according to specific requirements. / <p>QC 20151221</p>

No description available.

Orientador: Alexandre César Rodrigues da Silva / Resumo: O reconhecimento de atividades humanas abrange diversas técnicas de classificação que permitem identificar padrões específicos do comportamento humano no momento da ocorrência. A identificação é realizada analisando dados gerados por diversos sensores corporais, entre os quais destaca-se o acelerômetro, pois responde tanto à frequência como à intensidade dos movimentos. A identificação de atividades é uma área bastante explorada. Porém, existem desafios que necessitam ser superados, podendo-se mencionar a necessidade de sistemas leves, de fácil uso e aceitação por parte dos usuários e que cumpram com requerimentos de consumo de energia e de processamento de grandes quantidades de dados. Neste trabalho apresenta-se o desenvolvimento do Sistema de Reconhecimento de atividades Humanas e Avaliação do Equilíbrio Postural, denominado SIRAH. O sistema está baseado no uso de um acelerômetro localizado na cintura do usuário. As duas fases do reconhecimento de atividades são apresentadas, fase Offline e fase Online. A fase Offline trata do treinamento de uma rede neural artificial do tipo perceptron de três camadas. No treinamento foram avaliados três estudos de caso com conjuntos de atributos diferentes, visando medir o desempenho do classificador na diferenciação de 3 posturas e 4 atividades. No primeiro caso o treinamento foi realizado com 15 atributos, gerados no domínio do tempo, com os que a rede neural artificial alcançou uma precisão de 94,40%. No segundo caso foram gerados 34 ... (Resumo completo, clicar acesso eletrônico abaixo) / Doutor