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Excessive weight gain during pregnancy is a significant public health concern and has been the recent focus of novel, control systems-based interventions. Healthy Mom Zone (HMZ) is an intervention study that aims to develop and validate an individually tailored and intensively adaptive intervention to manage weight gain for overweight or obese pregnant women using control engineering approaches. Motivated by the needs of the HMZ, this dissertation presents how to use system identification and state estimation techniques to assist in dynamical systems modeling and further enhance the performance of the closed-loop control system for interventions.
Underreporting of energy intake (EI) has been found to be an important consideration that interferes with accurate weight control assessment and the effective use of energy balance (EB) models in an intervention setting. To better understand underreporting, a variety of estimation approaches are developed; these include back-calculating energy intake from a closed-form of the EB model, a Kalman-filter based algorithm for recursive estimation from randomly intermittent measurements in real time, and two semi-physical identification approaches that can parameterize the extent of systematic underreporting with global/local modeling techniques. Each approach is analyzed with intervention participant data and demonstrates potential of promoting the success of weight control.
In addition, substantial efforts have been devoted to develop participant-validated models and incorporate into the Hybrid Model Predictive Control (HMPC) framework for closed-loop interventions. System identification analyses from Phase I led to modifications of the measurement protocols for Phase II, from which longer and more informative data sets were collected. Participant-validated models obtained from Phase II data significantly increase predictive ability for individual behaviors and provide reliable open-loop dynamic information for HMPC implementation. The HMPC algorithm that assigns optimized dosages in response to participant real time intervention outcomes relies on a Mixed Logical Dynamical framework which can address the categorical nature of dosage components, and translates sequential decision rules and other clinical considerations into mixed-integer linear constraints. The performance of the HMPC decision algorithm was tested with participant-validated models, with the results indicating that HMPC is superior to "IF-THEN" decision rules.
Underreporting of energy intake (EI) has been found to be an important consideration that interferes with accurate weight control assessment and the effective use of energy balance (EB) models in an intervention setting. To better understand underreporting, a variety of estimation approaches are developed; these include back-calculating energy intake from a closed-form of the EB model, a Kalman-filter based algorithm for recursive estimation from randomly intermittent measurements in real time, and two semi-physical identification approaches that can parameterize the extent of systematic underreporting with global/local modeling techniques. Each approach is analyzed with intervention participant data and demonstrates potential of promoting the success of weight control.
In addition, substantial efforts have been devoted to develop participant-validated models and incorporate into the Hybrid Model Predictive Control (HMPC) framework for closed-loop interventions. System identification analyses from Phase I led to modifications of the measurement protocols for Phase II, from which longer and more informative data sets were collected. Participant-validated models obtained from Phase II data significantly increase predictive ability for individual behaviors and provide reliable open-loop dynamic information for HMPC implementation. The HMPC algorithm that assigns optimized dosages in response to participant real time intervention outcomes relies on a Mixed Logical Dynamical framework which can address the categorical nature of dosage components, and translates sequential decision rules and other clinical considerations into mixed-integer linear constraints. The performance of the HMPC decision algorithm was tested with participant-validated models, with the results indicating that HMPC is superior to "IF-THEN" decision rules.
ContributorsGuo, Penghong (Author) / Rivera, Daniel E. (Thesis advisor) / Peet, Matthew M. (Committee member) / Forzani, Erica (Committee member) / Deng, Shuguang (Committee member) / Pavlic, Theodore P. (Committee member) / Arizona State University (Publisher)
Created2018
Description
Workplace productivity is a result of many factors, and among them is the setup of the office and its resultant noise level. The conversations and interruptions that come along with converting an office to an open plan can foster innovation and creativity, or they can be distracting and harm the performance of employees. Through simulation, the impact of different types of office noise was studied along with other changing conditions such as number of people in the office. When productivity per person, defined in terms of mood and focus, was measured, it was found that the effect of noise was positive in some scenarios and negative in others. In simulations where employees were performing very similar tasks, noise (and its correlates, such as number of employees), was beneficial. On the other hand, when employees were engaged in a variety of different types of tasks, noise had a negative overall effect. This indicates that workplaces that group their employees by common job functions may be more productive than workplaces where the problems and products that employees are working on are varied throughout the workspace.
ContributorsHall, Mikaela Starrantino (Author) / Pavlic, Theodore P. (Thesis director) / Cooke, Nancy (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Social insect colonies adeptly make consensus decisions that emerge from distributed interactions among colony members. How consensus is accomplished when a split decision requires resolution is poorly understood. I studied colony reunification during emigrations of the crevice-dwelling ant Temnothorax rugatulus. Colonies can choose the most preferred of several alternative nest cavities, but the colony sometimes initially splits between sites and achieves consensus later via secondary emigrations. I explored the decision rules and the individual-level dynamics that govern reunification using artificially split colonies. When monogynous colonies were evenly divided between identical sites, the location of the queen played a decisive role, with 14 of the 16 colonies reuniting at the site that held the queen. This suggests a group-level strategy for minimizing risk to the queen by avoiding unnecessary moves. When the queen was placed in the less preferred of two sites, all 14 colonies that reunited did so at preferred nest, despite having to move the queen. These results show that colonies balance multiple factors when reaching consensus, and that preferences for physical features of environment can outweigh the queen's influence. I also found that tandem recruitment during reunification is overwhelmingly directed from the preferred nest to the other nest. Furthermore, the followers of these tandem runs had a very low probability (5.7%) of also subsequently conducting transports.
ContributorsDoering, Grant Navid (Author) / Pratt, Stephen (Thesis director) / Pavlic, Theodore P. (Committee member) / Sasaki, Takao (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12