Sparse System Identification of Leptin Dynamics in Women With Obesity

The prevalence of obesity is increasing around the world at an alarming rate. The interplay of the hormone leptin with the hypothalamus-pituitary-adrenal axis plays an important role in regulating energy balance, thereby contributing to obesity. This study presents a mathematical model, which describes hormonal behavior leading to an energy abnormal equilibrium that contributes to obesity. To this end, we analyze the behavior of two neuroendocrine hormones, leptin and cortisol, in a cohort of women with obesity, with simplified minimal state-space modeling. Using a system theoretic approach, coordinate descent method, and sparse recovery, we deconvolved the serum leptin-cortisol levels. Accordingly, we estimate the secretion patterns, timings, amplitudes, number of underlying pulses, infusion, and clearance rates of hormones in eighteen premenopausal women with obesity. Our results show that minimal state-space model was able to successfully capture the leptin and cortisol sparse dynamics with the multiple correlation coefficients greater than 0.83 and 0.87, respectively. Furthermore, the Granger causality test demonstrated a negative prospective predictive relationship between leptin and cortisol, 14 of 18 women. These results indicate that increases in cortisol are prospectively associated with reductions in leptin and vice versa, suggesting a bidirectional negative inhibitory relationship. As dysregulation of leptin may result in an abnormality in satiety and thereby associated to obesity, the investigation of leptin-cortisol sparse dynamics may offer a better diagnostic methodology to improve better treatments plans for individuals with obesity

Closed-Loop Regulation of Internal Brain States using Wearable Brain Machine Interface Architectures with Real-World Experimental Implementation

The brain is a control system with a strong impact on all human functions. Inspired by the recent advances in wearable technologies, we design wearable-machine interface (WMI) architectures for controlling brain responses. The WMI architecture encompasses collecting physiological data using wearable devices, inferring neural stimuli underlying pulsatile signals, estimating an unobserved state based on the underlying stimuli, designing the control, and closing the loop. In this thesis, we design WMI architectures for regulating human’s cognitive stress state and controlling energy levels in patients with hypercortisolism. Hypercortisolism, which corresponds to the excessive levels of cortisol hormone, is associated with tiredness and fatigue during the day and disturbed sleep at night. Automating the use of medications that are effective by either elevating or lowering the energy levels might help patients with hypercortisolism to experience more balanced energy cycles required for their daily activities and better sleep patterns at night. Keeping cognitive stress at a healthy range can improve the overall quality of life by helping the subjects to decrease their high levels of arousal to relax them and elevate their low levels of arousal to increase the engagement. Skin conductance data provides us with valuable information regarding one's cognitive stress-related state. We propose to use this physiological data collected via wearable devices to infer individuals' arousal state. In the first part of this research, we simulate multi-day cortisol profile data for multiple subjects both in healthy conditions and with Cushing's disease. Then, we present a state-space model to relate an internal hidden cognitive energy state to subject's cortisol secretion patterns. Particularly, we consider circadian upper and lower bound envelopes on cortisol levels, and timings of hypothalamic pulsatile activity underlying cortisol secretions as continuous and binary observations, respectively. By estimating the hidden energy state and incorporating the simulated hypothetical medication dynamics, we design a knowledge-based control system and close the loop. In the second part of this research, we design a simulation environment to control a cognitive stress-related state in a closed-loop manner. Hence, using the state-space approach, we relate internal cognitive stress state to the changes in skin conductance. Then, we estimate the hidden stress state and close the loop by designing a fuzzy controller. Next, we propose supervised control architectures to enhance the closed-loop performance in cognitive stress regulation. To further enhance the closed-loop design, we consider adaptive and robust control systems to handle model uncertainty and additional disturbance input. Finally, we design and perform multiple human-subject experiments to further explore safe actuation to regulate internal hidden brain states in real-world. In these novel experiments, we employ wearable technologies and publish data sets that could be further investigated to model the dynamics of proposed safe actuation. These studies are the first steps toward the goal of treating similar mental and hormone-related disorders in real-world situations. Analyzing the human subjects’ responses to the effective safe actuation would further enhance the efficiency of proposed approaches and lead us to a practical automated personalized closed-loop architecture

Detection of Stator Winding Fault in Induction Motor Using Fuzzy Logic with Optimal Rules

Induction motors are critical components in many industrial processes. Therefore, swift, precise and reliable monitoring and fault detection systems are required to prevent any further damages. The online monitoring of induction motors has been becoming increasingly important. The main difficulty in this task is the lack of an accurate analytical model to describe a faulty motor. A fuzzy logic approach may help to diagnose traction motor faults. This paper presents a simple method for the detection of stator winding faults (which make up 38% of induction motor failures) based on monitoring the line/terminal current amplitudes. In this method, fuzzy logic is used to make decisions about the stator motor condition. In fact, fuzzy logic is reminiscent of human thinking processes and natural language enabling decisions to be made based on vague information. The motor condition is described using linguistic variables. Fuzzy subsets and the corresponding membership functions describe stator current amplitudes. A knowledge base, comprising rule and data bases, is built to support the fuzzy inference. Simulation results are presented to verify the accuracy of motor’s fault detection and knowledge extraction feasibility. The preliminary results show that the proposed fuzzy approach can be used for accurate stator fault diagnosis

Regulation of brain cognitive states through auditory, gustatory, and olfactory stimulation with wearable monitoring

Abstract Inspired by advances in wearable technologies, we design and perform human-subject experiments. We aim to investigate the effects of applying safe actuation (i.e., auditory, gustatory, and olfactory) for the purpose of regulating cognitive arousal and enhancing the performance states. In two proposed experiments, subjects are asked to perform a working memory experiment called n-back tasks. Next, we incorporate listening to different types of music, drinking coffee, and smelling perfume as safe actuators. We employ signal processing methods to seamlessly infer participants’ brain cognitive states. The results demonstrate the effectiveness of the proposed safe actuation in regulating the arousal state and enhancing performance levels. Employing only wearable devices for human monitoring and using safe actuation intervention are the key components of the proposed experiments. Our dataset fills the existing gap of the lack of publicly available datasets for the self-management of internal brain states using wearable devices and safe everyday actuators. This dataset enables further machine learning and system identification investigations to facilitate future smart work environments. This would lead us to the ultimate idea of developing practical automated personalized closed-loop architectures for managing internal brain states and enhancing the quality of life

Regulation of brain cognitive states through auditory, gustatory, and olfactory stimulation with wearable monitoring

Inspired by advances in wearable technologies, we design and perform human-subject experiments. We aim to investigate the effects of applying safe actuation (i.e., auditory, gustatory, and olfactory) for the purpose of regulating cognitive arousal and enhancing the performance states. In two proposed experiments, subjects are asked to perform a working memory experiment called n-back tasks. Next, we incorporate listening to different types of music, drinking coffee, and smelling perfume as safe actuators. We employ signal processing methods to seamlessly infer participants' brain cognitive states. The results demonstrate the effectiveness of the proposed safe actuation in regulating the arousal state and enhancing performance levels. Employing only wearable devices for human monitoring and using safe actuation intervention are the key components of the proposed experiments. Our dataset fills the existing gap of the lack of publicly available datasets for the self-management of internal brain states using wearable devices and safe everyday actuators. This dataset enables further machine learning and system identification investigations to facilitate future smart work environments. This would lead us to the ultimate idea of developing practical automated personalized closed-loop architectures for managing internal brain states and enhancing the quality of life