Master of Science / Department of Clinical Sciences / Robert L. Larson / Bradley J. White / We performed research that investigated the ability of three dimensional accelerometers to classify cattle behavior and also describe the circadian patterns within that behavior. The first of three studies (validation study) tested a decision tree classification system and its ability to describe behaviors of lying, standing, and walking. Classification accuracies for lying, standing, and walking behaviors were 99.2%, 98.0%, and 67.8% respectively, with walking behavior having significantly lower accuracy (P<0.01). This study also tested the accuracy of classifying the above behaviors using different device reporting intervals, or epochs. Reporting intervals of 3, 5, and 10 seconds (s) were evaluated in their ability to describe cattle behaviors of lying, standing, and walking. Classification accuracies for the 3s, 5s, and 10s reporting interval were 98.1%, 97.7%, and 85.4% respectively, with no difference in classification accuracy of the 3s and 5s epochs (P=0.73) while the 10s epoch exhibited significantly lower overall accuracy (P<0.01). This validated accelerometer monitoring system was then implemented in two studies (Winter 2007 and Spring 2008) where the devices were used to describe behavior patterns of beef calves in a drylot production setting. Lying behavior of the cattle was analyzed and found to be significantly associated (P<0.001) with hour of the day. Calves in these studies spent most (> 55%) of the nighttime hours (2000 to 0400) involved in lying behavior and spent the least percentage of time lying (<30%) during periods of time where feed was presented at the bunk (0700 and 1700). Mean lying time was also associated with trial day (P<0.01) and most trial days (67.5%) calves spending between 45% and 55% of time lying. Variation of lying behavior was found between individuals (range 29% to 66%); however, consistency in lying behavior was found within individual calves across study periods. The accelerometer monitoring system studies presented here provide evidence these devices have utility in recording behaviors (lying, standing, and walking) of individual beef calves raised in typical production settings.

International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / The GPS/INS equipment is used at the Air Force Flight Test Center (AFFTC) to collect time space position information (TSPI) during testing. The GPS-based test instrumentation is lagging behind available commercial technologies. Advancing technologies for test use requires investigation of affordable commercial equipment. To enable technology insertion for state of the art testing, there is a need for more robust, flexible, reliable, modular, affordable low cost TSPI systems capable of operating in all flight environments. Modular (plug-and-play) hardware and software, quick and easy to re-configure, are required for supporting various test platforms from fighter aircraft to cargo size aircraft. Flight testing dynamics are such that, GPS-only systems tend to lose data during critical maneuvers. To minimize this data loss, inertial measurement systems coupled with GPS sensors are used in most sophisticated range instrumentation packages. However, these packages have required fairly expensive inertial units, are usually very large and not very flexible in terms of quick and easy reconfiguration to meet the unique needs of AFFTC’s test customers. WADDAN SYSTEMS has begun to address this problem with a modular design concept, which incorporates their high-performance navigation quality inertial measurement unit, but with costs comparative to that of lower-end performance inertial units. This paper describes WADDAN’s concept and the components that make up MAGI; and addresses some of the preliminary testing and near-term proposed activities. In general, the system will provide GPS, inertial and discrete MIL-STD 1553, RS-232/422 and video data from the participant. The MAGI will be structured around the Compact personal computer interface (PCI) backplane bus with on-board recording and processing and will include real-time command and control through a UHF data link.

This study concerned the development and validation of a hardware and software biomeasurement system, which was designed to be used by physiotherapists, general practitioners and other healthcare professionals. The purpose of the system is to detect and assess gait deviation in the form of reduced post-operative range of movement (ROM) of the replacement hip joint in total hip arthroplasty (THA) patients. In so doing, the following original work is presented: Production of a wearable, microcontroller-equipped system which was able to wirelessly relay accelerometer sensor data of the subject’s key hip-position parameters to a host computer, which logs the data for later analysis. Development of an artificial neural network is also reported, which was produced to process the sensor data and output assessment of the subject’s hip ROM in the flexion/extension and abduction/adduction rotations (forward and backward swing and outward and inward movement of the hip respectively). The review of literature in the area of biomeasurement devices is also presented. A major data collection was carried out using twenty-one THA patients, where the device output was compared to the output of a Vicon motion analysis system which is considered the ‘gold standard’ in clinical gait analysis. The Vicon system was used to show that the device developed did not itself affect the patient’s hip, knee or ankle gait cycle parameters when in use, and produced measurement of hip flexion/extension and abduction/adduction closely approximating those of the Vicon system. In patients who had gait deviations manifesting in reduced ROM of these hip parameters, it was demonstrated that the device was able to detect and assess the severity of these excursions accurately. The results of the study substantiate that the system developed could be used as an aid for healthcare professionals in the following ways: · To objectively assess gait deviation in the form of reduced flexion/extension and abduction/adduction in the human hip, after replacement, · Monitoring of patient hip ROM post-operatively · Assist in the planning of gait rehabilitation strategies related to these hip parameters.

This paper reports the evaluation results of the mechanical structures of MEMS (micro electro mechanical systems) sensor implemented in the integrated MEMS inertial sensor for a wide sensing range from below 0.1 G to 20 G (1 G = 9.8 m/s^2). To investigate the mechanical tolerance, a maximum target acceleration of 20G was applied to the sub-1G sensor which had the heaviest proof mass of all that sensors had. The structure stability of Ti/Au multi-layered structures was also examined by using Ti/Au micro cantilevers. The results showed that the stoppers effectively functioned to prevent the proof mass and the springs from self-destruction, and that the stability of Ti/Au structures increased with an increase in width. Those results suggest that the proposed stopper and spring structures could be promising to realize MEMS sensors.

In this paper we will briefly revisit the optical vibration measurement system (OVMS) at the Large Binocular Telescope (LBT) and how these values are used for disturbance compensation and particularly for the LBT Interferometer (LBTI) and the LBT Interferometric Camera for Near-Infrared and Visible Adaptive Interferometry for Astronomy (LINC-NIRVANA). We present the now centralized software architecture, called OVMS+, on which our approach is based and illustrate several challenges faced during the implementation phase. Finally, we will present measurement results from LBTI proving the effectiveness of the approach and the ability to compensate for a large fraction of the telescope induced vibrations.

In modern society, computers are primarily interacted with via keyboards, touch screens, voice recognition, video analysis, and many others. For certain applications, these methods may be the most efficient interface. However, there are applications that we can conceive where a more natural interface could be convenient and connect humans and computers in a more intuitive and natural way. These applications are gesture recognition systems and range from the interpretation of sign language by a computer to virtual reality control. This Thesis proposes a gesture recognition system that primarily uses accelerometers to capture gestures from a tactical communications application. A segmentation algorithm is developed based on the accelerometer energy to segment these gestures from an input sequence. Using signal processing and machine learning techniques, the segments are reduced to mathematical features and classified with support vector machines. Experimental results show that the system achieves an overall gesture recognition accuracy of 98.9%. Additional methods, such as non-gesture recognition/suppression, are also proposed and tested.

Cette thèse a été effectuée dans un contexte industriel de forte concurrence en lien avec les capteurs miniatures en silicium, destinés au gigantesque marché dit "consumer", dont l'application phare est le "Smartphone", pour laquelle les fonctionnalités accrues engendrent un besoin en matière de multi-capteurs inertiels dits 10-axes (accéléromètre 3-axes, magnétomètre 3-axes, gyromètre 3-axes et capteur de pression). Tout comme les circuits intégrés, les contraintes de coût de tels capteurs se traduisent par une exigence en termes de densité d'intégration. La technologie M&NEMS (Micro- & Nano- Electro Mechanical Systems) a été développée pour répondre à cette attente. Elle repose sur l'intégration de jauges de contraintes de dimensions nanométriques (~250 nm) avec des structures électromécaniques micrométriques, ce qui prodigue une compacité hors-pair des capteurs, ouvrant la voie à la co-intégration de multi-capteurs sur la même puce de silicium. Toutefois, la nature différente des grandeurs physiques à mesurer impose des contraintes supplémentaires, parfois opposées, ce qui rend leur co-intégration difficile. Partant de ce constat, nous avons exploré et développé, des solutions devant permettre le fonctionnement sous une même pression environnante, d'accéléromètres et de gyromètres à force de Coriolis. Cette problématique de co-intégration, s'étend au-delà du couple accéléromètre-gyromètre. Des questions inhérentes au capteur de pression ainsi qu'aux 3 axes de mesure d'un accéléromètre, sont également traitées dans cette thèse / This thesis was carried out in an industrial context of strong competition in connection with miniature silicon sensors for the huge so-called “consumer” market, where the “Smartphone” is the killer application; its increasing functionality creates a need for the so-called ‘10-axis' inertial multi-sensors (3-axis accelerometer, 3-axis magnetometer, 3-axis gyro sensor and pressure). Similarly to integrated circuits, cost constraints on such sensors translate into a requirement in terms of integration density. The M & NEMS (Micro- & Nano- Electro-Mechanical-Systems) technology has been developed to meet this expectation. It is based on the integration of nanoscale (~ 250 nm) strain gauges together with micrometric electromechanical structures, which ensure unrivaled compactness, paving the way for the co-integration of multiple inertial sensors on the same silicon chip. However, the different nature of the physical quantities to be measured imposes additional constraints, sometimes conflicting, which leads to a difficult co-integration. Based on this observation, we have explored and developed solutions to allow operation under the same ambient pressure, of accelerometers together with Coriolis force based gyroscopes. This issue of co-integration extends beyond the accelerometer-gyroscope couple. Issues inherent to the pressure sensor and to the 3-axis accelerometer measurements, are also addressed in this thesis

Un important effort de recherche est nécessaire pour assurer la maîtrise du vol en espace restreint de drones miniatures. Cette recherche s’appuie sur une nouvelle génération de capteurs inertiels, les capteurs vibrants, qui offrent par leur principe et leur technologie de réalisation associée, des perspectives tout à fait intéressantes en terme de miniaturisation / performance / coût. Dans ce travail de thèse, nous avons notamment étudié la relation entre la performance et les dimensions de micro-accéléromètres vibrants en silicium. Pour cela, nous avons établi les limites de ce type de capteur en fonction de la taille du résonateur. En particulier, le facteur de qualité est apparu comme un paramètre déterminant et une étude expérimentale a été réalisée afin de valider les modèles théoriques. De nombreux résultats expérimentaux nous ont permis de dresser une estimation des performances attendues. Enfin, une première architecture d’accéléromètre vibrant en silicium est détaillée / A major research effort is necessary to ensure flight control for miniaturized UAV. This research is based on a new generation of inertial sensors, the vibrating sensors, which offer because of their principle and their associated technology achievement, interesting prospects in terms of miniaturization / performance / cost. The focus of this research was to study the relationship between performance and size of silicon vibrating accelerometers.We have established the boundaries of this type of sensor based on the size of the resonator. In particular, the quality factor has emerged as a determining factor and an experimental study was conducted to validate the theoretical models. Thus, many experimental results have enabled us to estimate the expected performances. Finally, a first architecture of silicon vibrating accelerometer is presented

Master of Science / Department of Electrical & Computer Engineering / Steve Warren / Activity recognition can be an important part of human health awareness. Many benefits can be generated from the recognition results, including knowledge of activity intensity as it relates to wellness over time. Various activity-recognition techniques have been presented in the literature, though most address simple activity-data collection and off-line analysis. More sophisticated real-time identification is less often addressed. Therefore, it is promising to consider the combination of current off-line, activity-detection methods with wearable, embedded tools in order to create a real-time wireless human activity recognition system with improved accuracy. Different from previous work on activity recognition, the goal of this effort is to focus on specific activities that an astronaut may encounter during a mission. Planetary navigation field test (PNFT) tasks are designed to meet this need. The approach used by the KSU team is to pre-record data on the ground in normal earth gravity and seek signal features that can be used to identify, and even predict, fatigue associated with these activities. The eventual goal is to then assess/predict the condition of an astronaut in a reduced-gravity environment using these predetermined rules. Several classic machine learning algorithms, including the k-Nearest Neighbor, Naïve Bayes, C4.5 Decision Tree, and Support Vector Machine approaches, were applied to these data to identify recognition algorithms suitable for real-time application. Graphical user interfaces (GUIs) were designed for both MATLAB and LabVIEW environments to facilitate recording and data analysis. Training data for the machine learning algorithms were recorded while subjects performed each activity, and then these identification approaches were applied to new data sets with an identification accuracy of around 86%. Early results indicate that a single three-axis accelerometer is sufficient to identify the occurrence of a given PNFT activity. A custom, embedded acceleration monitoring system employing ZigBee transmission is under development for future real-time activity recognition studies. A different GUI has been implemented for this system, which uses an on-line algorithm that will seek to identify activity at a refresh rate of 1 Hz.

This thesis is focused on the design and realisation of inertial navigation unit INS. The unit is capable to measure, store and send data to a PC in real-time for a later offline processing. The first part of the thesis introduces the reader with the basic principles of accelerometers, gyroscopes and MEMS sensors. An introduction to coordinate systems and measuring errors is also included. The second and third part of the thesis deals with the analysis of the solution and the implementation of the INS unit. The fourth part of the document is dedicated to the software for the INS unit. This thesis concludes with explanation how the gathered data are processed.