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A challenging goal for both the game industry and the research community of computer graphics is the generation of 3D virtual avatars that automatically perform realistic human motions with high speed at low monetary cost. So far, full body motion estimation of human complexity remains an important open problem. We propose a realistic motion estimation framework to control the animation of 3D avatars. Instead of relying on a motion capture device as the control signal, we use low-cost and ubiquitously available 3D accelerometer sensors. The framework is developed in a data-driven fashion, which includes two phases: model learning from an existing high quality motion database, and motion synthesis from the control signal. In the phase of model learning, we built a high quality motion model of less complexity that learned from a large motion capture database. Then, by taking the 3D accelerometer sensor signal as input, we were able to synthesize high-quality motion from the motion model we learned. In this thesis, we present two different techniques for model learning and motion synthesis, respectively. Linear and nonlinear reduction techniques for data dimensionality are applied to search for the proper low dimensional representation of motion data. Two motion synthesis methods, interpolation and optimization, are compared using the 3D acceleration signals with high noise. We evaluate the result visually compared to the real video and quantitatively compared to the ground truth motion. The system performs well, which makes it available to a wide range of interactive applications, such as character control in 3D virtual environments and occupational training. / Master of Science

This work demonstrates an application of Vibration-Based Occupant Inference (VBOI) in frailty analysis. The rise of both Internet-of-Things (IoT) and VBOI provide new techniques to perform gait analysis via footstep-induced vibration which can be analyzed for early detection of human frailty. Thus, this work provides an application of VBOI to passively track gait parameters (e.g., gait speed) using floor-mounted accelerometers as opposed to using a manual chronometer as it is commonly performed by healthcare professionals. The first part of this thesis describes the techniques used for footstep detection by measuring the power of the footstep-generated vibration waves. The extraction of temporal gait parameters from consecutive footsteps can then be used to estimate temporal features such as cadence and stride time variation. VBOI provides many algorithms to accurately detect when a human-induced vibration event happened, however, spatial information is also needed for many gait parameters used in frailty diagnosis. Detecting where an event happened is a complicated problem because footsteps waves travel and decay in different ways according to the medium (floor system), the number of people walking, and even the walking speed. Therefore, the second part of this work will utilize an energy-based approach of footstep localization in which it is assumed that footstep waves decay exponentially as they travel across the medium. The results from this approach are then used to calculate spatial and tempo-spatial parameters. The main goal of this study is to understand the applicability of VBOI algorithms in gait analysis for frailty detection in a healthcare setting. / Master of Science / Human frailty is responsible for one of the highest healthcare costs and the death of many people every year. Although anyone suffering from frailty has a higher chance of death, it is particularly dangerous for the elderly population and for those suffering from other comorbidities. Diagnosing frailty is hard because it usually happens slowly over time. However, it has been shown that changes in some walking parameters (such as gait speed) can be an early indication of frailty. Many technologies have been created in order to track gait parameters, many of which either require expensive equipment (e.g., force plates) or the use of wearable devices, which can introduce privacy concerns. It has been proposed in the literature that Vibration-Based Occupant Inference (VBOI) techniques could be used in healthcare applications. Such algorithms measure footstep-induced vibration waves in order to detect and track footsteps. This system can provide several advantages in frailty analysis because of its affordability, ease of use, and little impact on patients' privacy. Therefore, the aim of this study is to understand the applicability of VBOI algorithms in gait analysis for frailty detection to be used in a healthcare setting. This thesis will proceed as follows: 1- The demonstration of an energy-based footstep detection and localization algorithm in VBOI. 2 - The application of such algorithms for gait parameters extraction with simulated frail walkers. 3 - Finally, an analysis of the proposed VBOI techniques for deployment in a real hospital setting.

Self-injurious behavior (SIB) is one of the most dangerous behavioral responses among individuals with autism spectrum disorder (ASD), often leading to injury and hospitalization. There is an ongoing need to measure the triggers of SIB to inform management and prevention. These triggers are determined traditionally through clinical observations of the child with SIB, often involving a functional assessment (FA), which is methodologically documenting responses to stimuli (e.g., environmental or social) and recording episodes of SIB. While FA has been a "gold standard" for many years, it is costly, tedious, and often artificial (e.g., in controlled environments). If performed in a naturalistic environment, such as the school or home, caregivers are responsible for tracking behaviors. FA in naturalistic environments relies on caregiver and patient compliance, such as responding to prompts or recalling past events. Recent technological developments paired with classification methods may help decrease the required tracking efforts and support management plans. However, the needs of caregivers and individuals with ASD and SIB should be considered before integrating technology into daily routines, particularly to encourage technology acceptance and adoption. To address this, the perspectives of SIB management and technology were first collected to support future technology design considerations (Chapter 2). Accelerometers were then selected as a specific technology, based on caregiver preferences and reported preferences of individuals with ASD, and were used to collect movement data for classification (Chapter 3). Machine learning algorithms with featureless data were explored, resulting in individual-level models that demonstrated high accuracy (up to 99%) in detecting and classifying SIB. Group-level classifiers could provide more generalizable models for efficient SIB monitoring, though the highly variable nature of both ASD and SIB can preclude accurate detection. A multi-level regression model (MLR) was implemented to consider such individual variability (Chapter 4). Both linear and nonlinear measures of motor variability were assessed as potential predictors in the model. Diverse classification methods were used (as in Chapter 3), and MLR outperformed other group level classifiers (accuracy ~75%). Findings from this research provide groundwork for a future smart SIB monitoring system. There are clear implications for such monitoring methods in prevention and treatment, though additional research is required to expand the developed models. Such models can contribute to the goal of alerting caregivers and children before SIB occurs, and teaching children to perform another behavior when alerted. / Doctor of Philosophy / Autism spectrum disorder (ASD) is a prevalent developmental disorder that adversely affects communication, social skills, and behavioral responses. Roughly half of individuals diagnosed with ASD show self-injurious behavior (SIB), including self-hitting or head banging), which can lead to injury and hospitalization. Clinicians or trained caregivers traditionally observe and record events before/after SIB to determine possible causes (“triggers”) of this behavior. Clinicians can then develop management plans to redirect, replace, or extinguish SIB at the first sign of a known trigger. Tracking SIB in this way, though, requires substantial experience, time, and effort from caregivers. Observations may suffer from subjectivity and inconsistency if tracked across caregivers, or may not generalize to different contexts if SIB is only tracked in the home or school. Recent technological innovations, though, could objectively and continuously monitor SIB to address the described limitations of traditional tracking methods. Yet, “smart” SIB tracking will not be adopted into management plans unless first accepted by potential users. Before a monitoring system is developed, caregiver needs related to SIB, management, and technology should be evaluated. Thus, as an initial step towards developing an accepted SIB monitoring system, caregiver perspectives of SIB management and technology were collected here to support future technology design considerations (Chapter 2). Sensors capable of collecting the acceleration of movement (accelerometers) were then selected as a specific technology, based on the reported preferences of caregivers and individuals with ASD, and were used to capture SIB movements from individuals with ASD (Chapter 3). These movements were automatically classified as “SIB” or “non-SIB” events using machine learning algorithms. When separately applying these methods to each individual, up to 99% accuracy in detecting and classifying SIB was achieved. Classifiers that predict SIB for diverse individuals could provide more generalizable and efficient methods for SIB monitoring. ASD and SIB presentations, however, range across individuals, which impose challenges for SIB detection. A multi-level regression model (MLR) was implemented to consider individual differences, such as those that may occur from diagnosis or behavior (Chapter 4). Model inputs included measures capturing changes of movement over time, and these were found to enhance SIB identification. Diverse classification models were also developed (as in Chapter 3), though MLR outperformed these (yielding accuracy of ~75%). Findings from this research provide groundwork for a smart SIB monitoring system. There are clear implications for monitoring methods in prevention, though additional research is required to expand the developed models. Such models can contribute to the goal of alerting caregivers and children before SIB occurs, and teaching children to perform another behavior when alerted.

Simultaneously achieving high-sensitivity and a large resonance frequency of micromachined accelerometers is difficult because of the inherent trade-off between the two. In this thesis, we present a mechanical displacement-amplifying technique that is amenable to micromachining to enhance sensitivity without compromising on the resonance frequency and cross-axis sensitivity. Depending on the requirements of sensitivity alone or sensitivity and resonance frequency, Displacement-amplifying Compliant Mechanisms (DaCMs) are designed using the selection map-based technique, which indicates the limits of what is possible for given specifications on size and microfabrication. In order to prove the benefits of a DaCM, we modified the designs of two very sensitive capacitive micromachined accelerometers from the literature by incorporating DaCMs and showed that, within the same footprint on the chip, the displacement sensitivity could be enhanced by more than 60% while the resonance frequency was also improved by more than 30%. As the focus of the thesis is to explore the integration of DaCMs into accelerometers, the analytical, computational, and practical aspects are discussed in detail. Both single and dual axis in-plane accelerometers are considered. The fabrication processes used are Silicon-on-Insulator Multi-user MEMS Processes (SOIMUMPs) and a customized Silicon-on-Insulator (SOI) based process. The fabricated accelerometers are packaged and brought to the product form. They were tested at the die level as well as in the packaged form. Under dynamic conditions, the measured amplification factor of the fabricated single-axis in-plane accelerometer was observed to be 11. The overall dimension of the accelerometer was 4.25 mm × 1.25 mm. The first in-plane natural frequency of the fabricated accelerometer was found to be 6.25 kHz. The voltage sensitivity of the packaged accelerometer with the DaCM measured 26.7 mV/g at 40 Hz with differential capacitance sensitivity of 3926 ppm/g around the base capacitance of 0.75 pF. The fabricated dual-axis accelerometer has a special configuration of twelve folded-beam suspension blocks that de-couple any displacements along the two in-plane orthogonal axes. The decoupling feature is retained even after adding the DaCMs along both the axes. The total device size was 8.6 mm × 8.6 mm. The device was also fabricated and packaged inside a ceramic flat-pin package using hybrid die-to-die wire-bonding. Die-level dynamic characterization showed that the average geometric advantage achieved using the DaCMs is 6.2 along both the in-plane axes. The measured axial voltage sensitivity of about 580 mV/g for both the axes was achieved with a cross-axial sensitivity of less than 2% and a natural frequency of 920 Hz. The static capacitance sensitivity was found to be 0.296 × 106 ppm/g with a base capacitance of 0.977 pF. Also presented in this work is a wide-band dual-axis accelerometer without an amplifying mechanism. Its first two in-plane modal frequencies measured 14.2 kHz. The measured sensitivity of the packaged accelerometer along both the axes of the device was found to be 62 mV/g at 200 Hz. Aiming at towards cost-effective accelerometers for small-volume markets, we also developed a single-axis and two dual-axis meso-scale spring-steel in-plane accelerometers equipped with Allegro A1395 linear Hall-effect sensors for sensing the displacement of the proof-mass. The single-axis in-plane meso-scale accelerometer also contains a DaCM. It is observed through simulation that the single-axis design with a DaCM is 39% more sensitive and has 41% more bandwidth compared to a single-axis design without a DaCM. The measured sensitivity of the fabricated single-axis spring-steel accelerometer with a DaCM was found to be 71.4 mV/g with a minimum resolvable acceleration of 14 milli-g. The unique features of the first generation of dual-axis accelerometers are that a rechargeable Li-ion battery adds to the proof-mass. It also contains a de-coupling mechanism that can decompose any planar acceleration into its axial components. The second generation of dual-axis accelerometers is more compact in size. All the mechanical elements of the accelerometers are made of EN J42/AISI 1080 spring steel foil machined using Wire-cut Electro-Discharge- Machining. The measured sensitivity of the first generation of dual-axis meso-scale accelerometers is 78 and 108 mV/g along the X and Y axes whereas the second generation device exhibits a sensitivity of 40 mV/g for both the axes. The thesis concludes that the sensitivity of a displacement-based sensor can be improved using a suitably designed DaCM without compromising the resonance frequency and hence the bandwidth. Furthermore, the work describing the development of meso-scale accelerometers also establishes spring steel as a viable material for meso-scale applications.

Approved for public release; distribution in unlimited. / MEMS technology is rapidly taking an important role in today's and future military systems. MEMS are able to lower the device size from millimeter to micrometer and maintain and sometimes surpass the performance of conventional devices. This thesis encompasses the knowledge acquired throughout the MEMS courses to design a two-axis capacitive accelerometer. The required acceleration and operating temperature range were Š50g in each axis and -40ʻC to +80 ʻC, respectively. The accelerometer was also needed to survive within a dynamic shocking environment with accelerations of up to 225g. The parameters of the accelerometer to achieve above specifications were calculated using lumped element approximation and the results were used for initial layout of it. A finite element analysis code (ANSYS) was used to perform simulations of the accelerometer under various operating conditions and to determine the optimum configuration. The simulated results were found to be within about 5% of the calculations indicating the validity of lumped element approach. The response of the designed accelerometer was 7 mV/g and with sensitivity of 1.3g at 3dB. It was also found that the accelerometer was stable in the desired range of operation including under the shock. Two axes sensing can be achieved using two identical accelerometers having their sensing axes perpendicular to each other. / Major, Taiwan Army

This dissertation discusses the design, simulation and characterization of process-compatible accelerometers and gyroscopes for the implementation of multi-degree-of-freedom (multi-DOF) systems. All components presented herein were designed to operate under the same vacuum-sealed environment to facilitate batch fabrication and wafer-level packaging (WLP), enabling the development of small form-factor single-die inertial measurement units (IMUs). The high-aspect-ratio poly and single-crystal silicon (HARPSS) process flow was used to co-fabricate the devices that compose the system, enabling the implementation ultra-narrow capacitive gaps (< 300 nm) in thick device-layer substrates (40 um). The presented gyroscopes were implemented as high-frequency BAW disk resonators operating in a mode-matched condition. A new technique to reduced dependencies on environmental stimuli such as temperature, vibration and shock was introduced. Novel decoupling springs were utilized to effectively isolate the gyros from their substrate, minimizing the effect that external sources of error have on offset and scale-factor. The substrate-decoupled (SD) BAW gyros were interfaced with a customized IC to achieve supreme random-vibration immunity (0.012 (deg/s)/g) and excellent rejection to shock (0.075 (deg/s)/g). With a scale factor of 800 uV/(deg/s), the complete SD-BAW gyro system attains a large full-scale range (2500 deg/s) with excellent linearity. The measured angle-random walk (ARW) of 0.36 deg/rthr and bias-instability of 10.5 deg/hr are dominated by the thermal and flicker noise of the IC, respectively. Additional measurements using external electronics show bias-instability values as low as 3.5 deg/hr. To implement the final monolithic multi-DOF IMU, accelerometers were carefully designed to operate in the same vacuum environment required for the gyroscopes. Narrow capacitive gaps were used to adjust the accelerometer squeeze-film damping (SFD) levels, preventing an under-damped response. Robust simulation techniques were developed using finite-element analysis (FEA) tools to extract accurate values of SFD, which were then match with measured results. Ultra-small single proof-mass tri-axial accelerometers with Brownian-noise as low as 30 ug/rtHz were interfaced with front-end electronics exhibiting scale-factor values in the order of 5 to 10 mV/g and cross-axis sensitivities of less than 3% before any electronic compensation.

In order to have a complete understanding of the relationship between physical activity (PA) and health, it is essential to be able to accurately assess PA in free-living situations. The purpose of this study was to use three accelerometers located on the dominant ankle, hip, and wrist and a heart rate (HR) monitor to estimate energy expenditure (EE) compared to measured EE while completing activities of daily living (ADL). The regression equation developed showed a significant relationship (P < 0.05) with EE. Together, HR and the hip-worn accelerometer explained 73.4% of the variance in EE with HR accounting for 59.3% (P < 0.05). Type of activity, the wrist-, and ankle-worn accelerometer each explained an additional 5%, 3% and 3% of the variance in EE, respectively (P < 0.05). The regression equation developed is a good predictor of EE during selected ADL, and allows researchers to more accurately quantify free-living PA. / School of Physical Education, Sport, and Exercise Science

Well-being incorporates psychological, social and moral development, the capacity to enter into and sustain satisfying relationships and limit distress and maladaptive behaviour. The benefits of physical activity and dangers of a sedentary lifestyle to health outcomes, e.g. cardio-vascular disease, obesity, diabetes and psychological well-being are well documented children. Due to methodological and measurement problems, research addressing the relationship between psychological well-being and physical activity are inconsistent and weak. This series of studies aimed to overcome the problems of previous research and investigated the relationship between children’s psychological well-being and habitual physical activity. Previous research has highlighted an association between children’s physical activity and individual psychological well-being constructs. However, Masse et al. (1998) argued that psychological well-being should contain the measurement of both positive (e.g. self-esteem) and negative (e.g. depression) psychological states. Subsequently, Parfitt and Eston (2005) identified an association between children’s total physical activity and psychological well-being (anxiety, depression, self-esteem). Limitations of previous studies include the type of physical activity measures used and the measurement of only global self-esteem, which itself is multidimensional. The purpose of this research was to extend and expand on the limitations of the Parfitt and Eston’s (2005) study. Psychological measures included both global, domain and sub-domain measurements of self-esteem, with accelerometry providing estimates of total daily physical activity and time spent in sedentary through to vigorous intensity activity. Study One assessed relationships between psychological well-being and physical activity using the same psychological constructs as Parfitt and Eston (2005), but with physical activity intensity included. Results highlighted an association between self-esteem and time spent in very light activity. Study Two assessed the current data’s applicability with the proposed models and theories related to self-esteem. It was concluded that the current data provided an adequate fit with the proposed models and theories of self-esteem. Study Three introduced the domain and sub-domain constructs of self-esteem and a more valid measure of physical activity. Results highlighted associations at the global, domain and sub-domain levels with predominately time spent in very light and vigorous intensity activity. Very light intensity activity was associated with negative effects while vigorous intensity activity was associated with positive effects. It was hypothesised that if these cross-sectional relationships also existed longitudinally, then an intervention study changing the time children spend in very light and vigorous intensity activity may be beneficial to children’s psychological health. With the relationship between children’s psychological well-being and physical activity clearly highlighted, Study Four assessed the direction of this relationship and aimed to inform a potential intervention study. Longitudinal data were collected over a 12-month period and multi-level modelling was used to analyse the direction of the relationship. Results highlighted a potential indirect effect of time accumulated in very light and vigorous intensity activity on psychological well-being, which reinforced the previous cross-sectional studies. Furthermore, a reciprocal effect was identified between physical activity and the physical self-worth domain. It was concluded that interventions decreasing time spent in very light and increasing vigorous intensity activity may be beneficial to children’s psychological well-being. Study Five aimed to have an impact on children’s psychological well-being, by reducing the time children spent in very light intensity activity, through an increase in daily step counts. Although the intervention decreased the time children spent in sedentary behaviour, there was no influence on psychological well-being. However, there were several methodological limitations that affected the intervention, limiting the conclusions that can be drawn from this study, including a small sample size providing available data for analysis. The results of this thesis show a relationship between children’s psychological well-being and habitual physical activity intensity exists. Further research manipulating the time children accumulate in very light (reduction) and vigorous (increase) intensity activity, can potentially impact positively on the psychological well-being of a normal population of children.

Previous reports have demonstrated that children’s physical activity is typically intermittent in nature. Accelerometers are reliable and valid tools for quantifying the pattern of activity in children. However, in order to interpret accelerometer output it is necessary to apply appropriate accelerometer thresholds for classifying physical activity intensity. Currently multiple accelerometer thresholds are available in the literature and it is unclear which are the most appropriate or whether thresholds should be sample specific. Additionally, there is little information regarding how the pattern of activity in children varies across groups and how activity patterns relate to health. The overall aim of the thesis was to describe the pattern of habitual physical activity, using objectively-measured physical activity data, in relation to health outcomes in pre-adolescent boys. The first objective was to characterize the pattern of boys’ habitual physical activity, using objectively-measured physical activity data. The second objective was to investigate the relationship between habitual physical activity and specific aspects of the activity pattern and health outcomes in boys. The final objective was to investigate the effects of continuously- versus intermittently- accumulated physical activity on acute health outcomes in boys, using an intermittent activity protocol based on the measured pattern of habitual activity in boys. The first four studies used a sample of 54 boys, aged 8 to 10 years. The final study used a different sample of 10 boys, aged 9 to 11 years. Both samples were from the county of Devon, UK. The first study of this thesis established sample-specific accelerometer-intensity thresholds through calibration research with ActiGraph accelerometers (counts•2s-1) in boys. The second study in the thesis demonstrated that relationships between time accumulated at different activity intensities and health (fatness, peak oxygen consumption and resting blood pressure) in boys were similar irrespective of whether sample-specific or published thresholds were employed as long as the threshold was at least equivalent to a brisk walk (i.e. >4 METs). However, the prevalence of children reported as meeting activity guidelines did differ according to thresholds employed. Study three showed that, despite little difference between overweight and normal weight boys in overall activity, time spent sedentary and minutes of light, moderate and vigorous intensity activity accumulated, aspects of the activity pattern (frequency, intensity and duration of ≥4 s (short) and ≥5-min (long) bouts of ≥light, ≥moderate, ≥vigorous and ≥hard intensity activity) differed by weight status. Overweight boys accumulated fewer and shorter bouts of activity, particularly sustained bouts of activity which were of moderate intensity or greater. Study four examined the relationship between activity pattern and health in boys further, focusing on body fatness, aerobic fitness, blood pressure and microvascular function. Results demonstrated that summary measures of activity were negatively related to body fatness, and positively related with both aerobic fitness (i.e., total, moderate, vigorous and hard activity) and endothelial function (i.e., total and light activity). Time spent sedentary was negatively related to endothelial function. The frequency and duration of activity bouts of ≥moderate intensity and the intensity of all activity bouts (i.e., ≥light intensity) were most important for body fatness and aerobic fitness. The frequency of all bouts (short and long) of at least light intensity was most important for endothelial health. Finally, study five moved away from chronic measures of health and examined the acute physiological effects of the recommended daily amount of physical activity (60 minutes of physical activity of ≥moderate intensity) on postprandial lipaemia and microvascular function in boys the following day. Furthermore, the study aimed to assess whether the effects differed depending on whether the activity was accumulated continuously or in a manner more similar to the children’s typical activity patterns identified in studies 3 and 4. In contrast to findings from adolescents and adults, 60 minutes of >moderate intensity activity did not impact on postprandial lipaemia or microvascular function the following day in healthy, active boys, regardless of how it was accumulated. The results of the present thesis indicate that aspects of the activity pattern are significantly related to health in boys and differ according to type of day and weight status. Furthermore, the benefits of sporadically-accumulated activity are equally as strong as continuously-accumulated activity to body fatness, aerobic fitness and endothelial health in boys. Since children typically accumulate short, intermittent bouts of activity, the promotion of sporadic activity (i.e., in intervention research and current physical activity guidelines for children and youth) might increase enjoyment and adherence. The benefits of acute intermittent exercise (which simulates free-living activity) on postprandial lipaemia and microvascular function in inactive children with risk factors for cardiovascular disease should be investigated. Longitudinal investigations into the activity pattern of a much larger and more age-diverse sample of boys and girls are needed to determine whether any changes in aspects of the activity pattern might alter these and other health outcomes (i.e., cardiovascular risk factors).