This paper deals with the design of multi-axis force (also known as force/torque) sensors, as considered within the framework of optimal design theory. The principal goal of this paper is to identify a mathematical objective function, whose minimization corresponds to the optimization of sensor accuracy. The methodology employed is derived from linear algebra and analysis of numerical stability. The problem of optimizing the number of basic transducers employed in a multi-component sensor is also addressed. Finally, applications of the proposed method to the design of a simple sensor as well as to the optimization of a novel, 6-axis miniaturized sensor are discussed.

Indiana University-Purdue University Indianapolis (IUPUI) / Fall in recent years have become a potential threat to elder generation. It occurs because of side effects of medication, lack of physical activities, limited vision, and poor mobility. Looking at the problems faced by people and cost of treatment after falling, it is of high importance to develop a system that will help in detecting the fall before it occurs. Over the year's, this has influenced researchers to pursue the development to automatic fall detection system. However, much of existing work achieved a hardware system to detect pre and post fall patterns, the existing systems deficient in achieving low power consumption, user-friendly hardware implementation and high precision. Growth in medical devices can be seen in recent years. Today's medical devices aim to increase the life expectancy and comfort of human being. The systems are designed to be made reliable by improving the performance, optimizing the size and minimizing the energy consumption. For wearable technologies, power consumption is an important factor to be considered during system design. High power consumption decreases the battery life, which leads to poor comfortability. The purpose of this research is to develop a system with low power consumption to detect human falls before they happen. This research points towards the development of dependable and low power embedded system device with easy to wear capabilities and optimal sensor structure. In this work, we have developed a device using motion sensor to sense the subjects linear and angular velocity, communication sensor to send the fall related information to caretaker, and signal sensors to communicate and update user about device information. The designed system is triggered on interrupts from motion sensor. As soon as the system is triggered by an interrupt signal, users balanced and unbalanced states gets monitored. Once the unbalanced state is designated, the system signifies it as fall by setting a fall flag. The fall decision parameters; pitch, roll, complementary pitch, complementary roll, Signal Vector Magnitude (SVM), and Signal Magnitude Area (SMA) are layered to classify subject's different body posture. This helps the system to differentiate between activity of daily living (ADL) and fall. When the fall flag is set, the device sends important information like GPS location and fall type to caretaker. Early fall detection gives milliseconds of time to initiates the preventive measures. The system was designed, developed, and constructed. Near 100% sensitivity, 96% accuracy, and 95% specificity for fall detection were measured. The system can detect Front, Back, Side and Stair fall with consumption of 100_A (650_A with BLE consumption) in deep sleep mode, 6.5mA in active mode with no fall, and 14.5mA, of which 8.5 mA is consumed via the BLE when fall is declared in active mode. The power consumption was reduced because the integrated wireless communication devices consumed power only when the fall is triggered, giving the device a potential to communicate wirelessly.

In this research a state-of-the-art micro-thermal conductivity detector is developed based on MEMS technology. Its efficient design include a miniaturized 100×2 µm bridge from doped polysilicon, suspended 10 µm away from the single crystalline silicon substrate through a thermally grown silicon dioxide sacrificial layer. The microbridge is covered by 200 nm silicon nitride layer to provide more life time. Analytical models were developed that describe the relationship between the sensor response and ambient gas material properties. To obtain local temperature distribution and accurate predictions of the sensor response, a computational three dimensional simulation based on real geometry and minimal simplifications was prepared. It was able to handle steady-state and transient state, include multiple physics such as flow, heat transfer, electrical current and thermal stresses. Two new methods of measurement for micro TCD were developed; a time resolved method based on transient response of the detector to a step current pulse was introduced that correlates time constant of the response to the concentration of gas mixture. The other method is based on AC excitation of the micro detector; the amplitude and phase of the third harmonic of the resulting output signal is related to gas composition. Finally, the developed micro-sensor was packaged and tested in a GC system and was compared against conventional and complex FID for the detection of a mixture of VOCs. Moreover compact electronics and telemetry modules were developed that allow for highly portable applications including microGC utilization in the field.

Le suivi en temps réel des écoulements turbulents est une tâche difficile ayant des applications dans de nombreux domaines. Un exemple est la mesure des tourbillons de sillage au niveau des pistes d’aéroports afin d’optimiser la distance entre les avions en phase d’approche ou de décollage. Un autre exemple se rapporte au contrôle actif d’écoulements. De tels contrôles peuvent servir à réduire le bruit des avions... Cette thèse vise à développer des outils afin d’estimer en temps réel des champs de vitesse d’écoulements turbulents à partir d’un faible nombre de mesures ponctuelles. Après une étude bibliographique centrée sur une méthode de reconstruction populaire, l’estimation stochastique (SE), ses performances sont évaluées pour la prédiction de champs de vitesse issus d’écoulements de complexité croissante. La précision des estimations obtenues étant très faibles dans certains cas, une analyse précise de la méthode est effectuée. Celle-ci a montré l’effet filtrant de la SE sur le contenu spatial et temporel des champs de vitesse. De plus, le fort impact de la position des capteurs a été mis en avant. C’est pourquoi un algorithme d’optimisation de la position des capteurs est ensuite présenté. Bien que l’optimisation de la position des capteurs mène à une amélioration de la précision des prédictions obtenues par SE, elle reste néanmoins très faible pour certains cas tests. L’utilisation d’une technique issue du domaine de l’assimilation de données, le filtre de Kalman qui combine un modèle dynamique de l’écoulement avec les mesures, a donc été étudiée. Pour certains écoulements, le filtre de Kalman permet d’obtenir des prédictions plus précises que la SE. / Real time monitoring of turbulent flows is a challenging task that concerns a large range of applications. Evaluating wake vortices around the approach runway of an airport, in order to optimize the distance between lined-up aircraft, is an example. Another one touches to the broad subject of active flow control. In aerodynamic, control of detached flows is an essential issue. Such a control can serve to reduce noise produced by airplanes, or improve their aerodynamic performances. This work aims at developing tools to produce real time prediction of turbulent velocity fields from a small number of punctual sensors. After a literature review focused on a popular reconstruction method in fluid mechanics, the Stochastic Estimation (SE), the first step was to evaluate its overall prediction performances on several turbulent flows of gradual complexity. The accuracy of the SE being very limited in some cases, a deeper characterization of the method was performed. The filtering effect of the SE in terms of spatial and temporal content was particularly highlighted. This characterization pointed out the strong influence of the sensor locations on the estimation quality. Therefore, a sensor location optimization algorithm was proposed and extended to the choice of time delays when using Multi-Time-Delay SE. While using optimized locations for the sensors hold some accuracy improvements, they were still insufficient for some test cases. The opportunity to use a data assimilation method, the Kalman filter that combines a dynamic model of the flow with sensor information, was investigated. For some cases, the results were promising and the Kalman filter outperforms all SE methods.

<p> Volatile organic compounds (VOCs) are byproducts of metabolic processes that can be uniquely dysregulated by various medical conditions and are expressed in biological samples. Therefore, VOCs expressed in breath, urine and other sample types may be utilized for noninvasive, rapid, and accurate diagnostics in a point-of-care setting. Currently, the most common methods for VOC detection include gas chromatography-mass spectrometry (GC-MS) and electronic noses (E-noses) that integrate nanosensors. Both methods present important advantages and challenges that allow their implementation for different applications. While GC-MS can be used to directly identify VOCs in complex matrices, it is a non-portable and high-cost instrument. On the other hand, E-noses are portable and user-friendly VOC detectors, but they do not allow for direct VOC identification or quantification. Among different VOC rich sample types, breath offers the advantage of being a virtually limitless source of endogenous biomarkers that can be implemented for noninvasive VOC detection.</p> <p><br></p> <p>The presented thesis focuses on the optimization of the operating parameters (heater and sensor voltages) of a metal oxide (MOX) sensor and breath sampling techniques (sensor casing, breath fractionation, and exhalation volume) for their implementation in exhaled VOC analysis. In parallel, an in-house feature extraction algorithm was developed and implemented for the optimization of a MOX sensor composed of a tin oxide (SnO2) sensing layer. The optimized sensor parameters (heater voltage equal to 2 V and sensor voltage equal to 0.8 V) and breath sampling protocol (24 L of whole breath analyzed using the in-house sensor casing design) were tested with exhaled breath samples from distinct volunteers which could be successfully separated with 100% accuracy. The sensor response also showed a high degree of intrasubject reproducibility (RSD < 6%). Additionally, the sensor performance was further validated under ambient conditions, and sensor degradation was studied over the course of 3 months. Finally, sensor response to synthetic VOC profiles and individual VOC standards was explored. Optimized SnO2 sensors distinguished between VOC mixtures regardless of variations in relative humidity (RH) levels. Furthermore, the characteristic sensor response to VOC standards indicates that the sensors are most sensitive toward isopropanol by a factor of 1.15 in 45% RH and a factor of 3.58 in 85% RH relative to isoprene. </p> <p><br></p> <p>To translate the potential of MOX sensors to point-of-care biomedical applications, there first exists the need to establish a reference of sensor baseline signals corresponding to exhaled breath samples from healthy individuals. SnO2 sensors and breath sampling methods were implemented for the collection of individual samples from 109 relatively healthy volunteers. 10 of these volunteers provided 9 additional samples over the course of six months. In parallel, exhaled breath samples were also analyzed by GC-MS to comprehensively profile VOCs present in the samples. The results from these experiments not only aid in the identification of the healthy breath signal baseline but also allow the exploration of VOC reproducibility over time. High variation between samples from distinct volunteers was observed, but samples longitudinally collected across volunteers could not be distinguished, alluding to the existence of a universal range of sensor signals that could describe the composition of exhaled breath from healthy subjects. Finally, results were compared with relevant confounding variables to better understand how VOCs are impacted by an array of factors that are not directly correlated to disease diagnosis. Sensor signals were significantly elevated in breath samples from male volunteers compared to samples from female subjects (p-value = 0.044). Interestingly, isoprene signals resulting from the GC-MS analysis were also higher in male subjects relative to females. No other relationships were identified between sensor signals and the confounding variables of interest. </p> <p><br></p> <p>Future work would require a deeper understanding of sensor degradation and life cycle, along with sensor testing using a broader range of individual VOC standards and more complex VOC profiles. Additionally, further comparison between sensor signal and GC-MS signal of relevant VOC biomarkers present in breath would be beneficial. Nonetheless, the presented be leveraged in future investigations aiming to identify biomarkers for different medical conditions. Finally, the findings disclosed in the deposited thesis suggest the ability of a SnO2 nanosensor array to be implemented for breath analysis, providing a noninvasive, easy to use, and reliable diagnostic device in a point-of-care setting. </p>