This thesis focuses on developing a high data rate wireless sensor network framework that could be integrated with hardware prototypes to monitor structural health of buildings. In order to better understand the wireless sensor network architecture and its consideration in structural health monitoring, a detailed literature review on wireless sensor networks has been carried out. Through research, it was found that there are numerous simulation software packages available for wireless sensor network simulation. One suitable software was selected for modelling the framework. Research showed that Matlab/Simulink was the most suitable environment, and as a result, a wireless sensor network framework was designed in Matlab/Simulink. Further, the thesis illustrates modeling of a simple accelerometer sensor, such as those used in wireless sensor networks in Matlab/Simulink using a mathematical description. Finally, the framework operation is demonstrated with 10 nodes, and data integrity is analyzed with cyclic redundancy check and transmission error rate calculations.

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.

Observa-se nos tempos atuais um crescente interesse e demanda por novas tecnologias de sensoriamento e interação. A monitoração, com o objetivo de reconhecimento de movimentos humanos, permite oferecer serviços personalizados em diferentes áreas, dentre elas a área de cuidados médicos. Essa monitoração pode ser realizada por meio de diferentes técnicas como o uso de câmeras de vídeo, instrumentação do ambiente onde o indivíduo habita, ou pelo uso de dispositivos pessoais acoplados ao corpo. Os dispositivos acoplados ao corpo apresentam vantagens como baixo custo, uso confortável, além de muitas vezes serem despercebidos pelo usuário, diminuindo a sensação de invasão de privacidade durante a monitoração. Além disso, o dispositivo sensor pode ser facilmente acoplado ao corpo pelo próprio usuário, tornando o seu uso efetivo. Deste modo, este trabalho apresenta o desenvolvimento de um sistema que emprega técnicas de inteligência computacional e um acelerômetro facilmente acoplado ao punho do usuário para efetuar, de maneira confortável e não invasiva, o reconhecimento de movimentos básicos da rotina de uma pessoa. Aplicando máquinas de vetores de suporte para classificar os sinais e a razão discriminante de Fisher para efetuar a seleção das características mais significativas, o sistema apresentou uma taxa de sucesso em torno de 93% no reconhecimento de movimentos básicos efetuados por indivíduos monitorados. O sistema apresenta potencialidade para ser integrado a um hardware embarcado de baixo custo, responsável pelo gerenciamento da aquisição dos dados e pelo encaminhamento das informações a um sistema de monitoramento ou armazenamento. As informações providas por este sistema podem ser destinadas à promoção da saúde e bem estar do indivíduo, bem como utilizadas em diagnósticos ou monitoramento remoto de pacientes em um ambiente de vida assistida. / Nowadays it is observed a growing interest and demand for new sensing technologies and interaction. Monitoring with the objective of recognizing human movements, allows us to offer personalized services in different areas, among them healthcare. This monitoring can be performed through the use of different techniques such as the use of video cameras, living environment instrumentation, or the use of personal devices attached to the body, also known as wearable devices. These wearable devices have some advantages such as low cost, comfortable to use, and are often unnoticed by the user, reducing the feeling of privacy invasion during the monitoring. In addition, the sensing device can be easily attached to the body by the user itself, making its use effective. Thus, this work presents the development of a system that uses computational intelligence techniques and an accelerometer which is easily attached to the users wrist to perform, in a comfortable and non-invasive manner, the recognition of basic movements of a persons routine. By applying support vector machines to classify the signals and Fishers discriminant ratio to select the most significant features, the system has shown a success rate of 93% in the recognition of basic movements performed by monitored individuals. The system has the potential to be integrated into a low-cost embedded hardware, which is responsible for managing the data acquisition and routing the movement data to a remote monitoring system or storage. The information provided by the system can be designed to promote the health and wellness of the individual, as well used in diagnostics or remote patient monitoring in an ambient assisted living (AAL).

This thesis proposes a motion sensing system for wheelchairs with the main objective of determining tips, falls and risky situations. The system relies on measurements from an Inertial Measurement Unit, (IMU), consisting of a 3-axis accelerometer and a 2-axis gyroscope as the source of information. The IMU was embedded in a portable device, the "Motion Logger", which collects motion data in a Secure Digital memory card after running a real time preprocessing algorithm. The algorithm was designed to reduce energy consumption and memory usage. Actual signal analysis and attitude estimation is carried out offline. The motion sensing system was developed for determining wheelchair-related falls as part of a major research effort carried out at the research center of the James A Haley VA Hospital Subject Safety Center, Tampa, Florida. The focus of the study concentrated on achieving a thorough understanding of the demographics, nature, consequences and the creation of prediction models for fall events. The main goal of the embedded system was to successfully estimate the motion variables relevant to the occurrence of falls, tips and similar risky situations. Currently, off-line smoothing techniques based on Kalman filter concepts allow for optimal estimation of angles in the longitudinal direction, roll, and in the lateral direction, pitch. Results from both predefined experiments with known outcomes and data collected from actual wheelchair users during pilot and final deployment stages are presented and discussed.

Spacecraft navigation is a critical component of any space mission. Space navigation uses on-board sensors and other techniques to determine the spacecraft’s current position and velocity, with permissible accuracy. It also provides requisite information to navigate to a desired position, while following the desired trajectory. Developments in technology have resulted in new techniques of space navigation. However, inertial navigation systems have consistently been the bedrock for space navigation. Recently, the successful space mission GOCE used on-board gravity gradiometer for mapping Earth’s gravitational field. This has motivated the development of new techniques like cold atom accelerometers, to create ultra-sensitive gravity gradiometers, specifically suited for space applications, including autonomous orbital navigation. This research aims to highlight the existing developments in the field of gravity gradiometry and its potential space navigation applications. The study aims to use the Linear Covariance Theory to determine specific sensor requirements to enable autonomous space navigation for different flight regimes.

Gesture Recognition provides an efficient human-computer interaction for interactive and intelligent computing. In this work, we address the problem of gesture recognition using the theory of random projection and by formulating the recognition problem as an $\ell_1$-minimization problem. The gesture recognition uses a single 3-axis accelerometer for data acquisition and comprises two main stages: a training stage and a testing stage. For training, the system employs dynamic time warping as well as affinity propagation to create exemplars for each gesture while for testing, the system projects all candidate traces and also the unknown trace onto the same lower dimensional subspace for recognition. A dictionary of 18 gestures is defined and a database of over 3,700 traces is created from 7 subjects on which the system is tested and evaluated. Simulation results reveal a superior performance, in terms of accuracy and computational complexity, compared to other systems in the literature.

Gesture Recognition provides an efficient human-computer interaction for interactive and intelligent computing. In this work, we address the problem of gesture recognition using the theory of random projection and by formulating the recognition problem as an $\ell_1$-minimization problem. The gesture recognition uses a single 3-axis accelerometer for data acquisition and comprises two main stages: a training stage and a testing stage. For training, the system employs dynamic time warping as well as affinity propagation to create exemplars for each gesture while for testing, the system projects all candidate traces and also the unknown trace onto the same lower dimensional subspace for recognition. A dictionary of 18 gestures is defined and a database of over 3,700 traces is created from 7 subjects on which the system is tested and evaluated. Simulation results reveal a superior performance, in terms of accuracy and computational complexity, compared to other systems in the literature.

Technological advances in physical activity measurement have increased the development and utilization of accelerometers and pedometers for assessing physical activity in controlled and free-living conditions. Individual differences in leg length, stride length and stride frequency may affect the reliability and validity of accelerometers in estimating energy expenditure. To address this theory, this thesis investigated the influence of leg length, stride length and stride frequency on accelerometer counts and energy expenditure using four accelerometers (AMP, Actical, MTI, and RT3) and one pedometer (Yamax). Eighty-six participants, age 8 to 40 (17.6 ± 8.0) years performed three ten-minute bouts of treadmill activity at self-selected speeds (4 to 12 km/h). Energy expenditure (kcal/min) was measured through expired gas analysis and used as the criterion standard to compare physical activity data from activity monitors. A 3 (models) x 2 (duplicates of each model) x 3 (speeds) x 7 (minutes) repeated measures ANOVA was used to assess intra-device, inter-device, and inter-model reliability. Coefficients of variation were calculated to compare within-device variation and between-device variation in accelerometer counts. Differences between measured and predicted energy expenditure were assessed across five height categories to determine the influence of leg length on the validity of accelerometer/pedometer data. Regression equations for each model were developed using mean activity counts/steps generated for each speed, adjusting for various predictor variables (i.e., age, weight, leg length). These were compared to model-specific equations to determine whether the addition of certain variables might explain more variance in energy expenditure. Leg length and stride frequency directly influenced variability in accelerometer data and thus predicted energy expenditure. At high speeds and stride frequencies counts began to level off in the Actical, however this did not occur in the other devices. Intra-device and inter-device variation in accelerometer counts was less than 10% and was lowest at very high speeds for the Actical, MTI, and RT3 (p<0.05). When compared to measured values, energy expenditure was consistently underestimated by the AMP, Actical, and Yamax models and consistently overestimated by the RT3 across speed. The MTI underestimated and overestimated energy expenditure depending on speed. Energy expenditure was both underestimated and overestimated to the greatest extent during the treadmill run for the tallest participants (p<0.05). Accelerometer counts or pedometer steps, when entered into regression equations with age, weight and leg length, explained from 85 to 94 % of the variance in measured energy expenditure, supporting the inclusion of these variables within manufacturer-based equations. These results suggest that individual differences in leg length and stride frequency affect the reliability and validity of accelerometer data and therefore must be controlled for when using accelerometry to predict energy expenditure.

Technological advances in physical activity measurement have increased the development and utilization of accelerometers and pedometers for assessing physical activity in controlled and free-living conditions. Individual differences in leg length, stride length and stride frequency may affect the reliability and validity of accelerometers in estimating energy expenditure. To address this theory, this thesis investigated the influence of leg length, stride length and stride frequency on accelerometer counts and energy expenditure using four accelerometers (AMP, Actical, MTI, and RT3) and one pedometer (Yamax). Eighty-six participants, age 8 to 40 (17.6 ± 8.0) years performed three ten-minute bouts of treadmill activity at self-selected speeds (4 to 12 km/h). Energy expenditure (kcal/min) was measured through expired gas analysis and used as the criterion standard to compare physical activity data from activity monitors. A 3 (models) x 2 (duplicates of each model) x 3 (speeds) x 7 (minutes) repeated measures ANOVA was used to assess intra-device, inter-device, and inter-model reliability. Coefficients of variation were calculated to compare within-device variation and between-device variation in accelerometer counts. Differences between measured and predicted energy expenditure were assessed across five height categories to determine the influence of leg length on the validity of accelerometer/pedometer data. Regression equations for each model were developed using mean activity counts/steps generated for each speed, adjusting for various predictor variables (i.e., age, weight, leg length). These were compared to model-specific equations to determine whether the addition of certain variables might explain more variance in energy expenditure. Leg length and stride frequency directly influenced variability in accelerometer data and thus predicted energy expenditure. At high speeds and stride frequencies counts began to level off in the Actical, however this did not occur in the other devices. Intra-device and inter-device variation in accelerometer counts was less than 10% and was lowest at very high speeds for the Actical, MTI, and RT3 (p<0.05). When compared to measured values, energy expenditure was consistently underestimated by the AMP, Actical, and Yamax models and consistently overestimated by the RT3 across speed. The MTI underestimated and overestimated energy expenditure depending on speed. Energy expenditure was both underestimated and overestimated to the greatest extent during the treadmill run for the tallest participants (p<0.05). Accelerometer counts or pedometer steps, when entered into regression equations with age, weight and leg length, explained from 85 to 94 % of the variance in measured energy expenditure, supporting the inclusion of these variables within manufacturer-based equations. These results suggest that individual differences in leg length and stride frequency affect the reliability and validity of accelerometer data and therefore must be controlled for when using accelerometry to predict energy expenditure.

Abstract: It is believed that Flag Rugby may produce physical activity (PA) in middle school students that is more vigorous than other sports. PURPOSE: To examine the effects of different sports on physical activity in middle school students. METHODS: 101 (55 M; 46 F, ages 11-14, grades 6-8 ) middle schoolers were randomly selected to participate in three different sports on three separate days during their regular scheduled PE class. The participants engaged in flag football on day one, basketball on day two and flag rugby on day three of the research study. These days were not consecutive. All physiological-related variables were collected using the Stayhealthy RT3TM accelerometer (Monrovia, USA). Enjoyment and competence were measured using the Intrinsic Motivation Inventory (IMI) (McAuley et. al., 1989; Ryan, 1982). The research design utilized a repeated measure analysis of variance (RMANOVA) followed by dependent t-tests. RESULTS: Flag football mean MET values were 4.93 + 1.35(SD). Basketball mean MET values were 5.51 + 2.02. Flag rugby mean MET values were 6.02 + 1.52. These results indicate a significant difference between flag football vs. basketball (p = 0.023) and flag rugby vs. flag football (p < 0.000), but no significant difference between basketball vs. flag rugby (p = 0.109). The results from the enjoyment and competence paired samples t-test found a significant difference between play of flag rugby (6.24 + 1.59, enjoyment; 6.00 + 1.46, competence) and flag football (5.38 + 1.69, enjoyment; 5.26 + 1.56) at (p < 0.000) for both scales. There was a significant difference between flag rugby and basketball (5.21 + 1.80 enjoyment; 5.21 + 1.68) at (p < 0.000) enjoyment and (p< 0.001) competence. However, there was no significant difference between basketball and flag football (p = 0.481) enjoyment and (p = 0.827) competence. DISCUSSION: There is, in fact, a significant difference in physical activity intensities and durations between flag rugby and that of flag football and/or basketball (p < 0.001) F, 7.66. Results from this study suggest that there is not a significant difference in between flag rugby and basketball but there is a significant difference in enjoyment and competence between flag rugby and flag football.