Search for dissertations about: "HRV Signals"
Showing result 1 - 5 of 6 swedish dissertations containing the words HRV Signals.
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1. Modelling and Inference using Locally Stationary Processes : Biomedical applications
Abstract : This thesis considers statistical methods for non-stationary signals, specifically stochastic modelling, inference on the model parameters and optimal spectral estimation. The models are based on Silverman’s definition of Locally Stationary Processes (LSPs). READ MORE
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2. User Modeling for Adaptive Virtual Reality Experiences : Personalization from Behavioral and Physiological Time Series
Abstract : Research in human-computer interaction (HCI) has focused on designing technological systems that serve a beneficial purpose, offer intuitive interfaces, and adapt to a person's expectations, goals, and abilities. Nearly all digital services available in our daily lives have personalization capabilities, mainly due to the ubiquity of mobile devices and the progress that has been made in machine learning (ML) algorithms. READ MORE
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3. Intelligent Driver Mental State Monitoring System Using Physiological Sensor Signals
Abstract : Driving a vehicle involves a series of events, which are related to and evolve with the mental state (such as sleepiness, mental load, and stress) of the driv- er. These states are also identified as causal factors of critical situations that can lead to road accidents and vehicle crashes. READ MORE
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4. A Personalised Case-Based Stress Diagnosis System Using Physiological Sensor Signals
Abstract : Stress is an increasing problem in our present world. It is recognised that increased exposure to stress may cause serious health problems if undiagnosed and untreated. In stress medicine, clinicians’ measure blood pressure, Electrocardiogram (ECG), finger temperature and respiration rate etc. READ MORE
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5. Statistical inference and time-frequency estimation for non-stationary signal classification
Abstract : This thesis focuses on statistical methods for non-stationary signals. The methods considered or developed address problems of stochastic modeling, inference, spectral analysis, time-frequency analysis, and deep learning for classification. READ MORE