Cardiac output (CO), a vital hemodynamic parameter that reflects the blood volume pumped by the heart per minute, is crucial for determining tissue oxygen delivery and the heart's ability to meet the body's demands. Researchers have developed various methods to measure cardiac output, including thermodilution using pulmonary artery catheters (PAC), also called Swan-Ganz catheters, the gold standard for cardiac output measurements. Such an approach involves an invasive procedure associated with complications, and it requires specialized equipment and expertise, limiting its use to critically ill patients undergoing operations in intensive care units (ICUs). An alternative, less invasive way to estimate CO is by analyzing arterial blood pressure (ABP) waveforms. However, the relationship between cardiac output and blood pressure is unknown. This study uses machine learning and feature engineering techniques to discover the relationship between CO and ABP. We applied the sparse identification non-linear dynamics (SINDy) algorithm to discover features that significantly contribute to the relationship between CO and ABP. Additionally, we investigated the optimum number of cardiac cycles required for feature extraction to achieve the best performance providing insights into the temporal dynamics of CO estimation. The proposed approach achieved clinically acceptable performance regarding radial limits of agreement and bias. Further, the proposed approach was validated on an external dataset and achieved comparable performance. Finally, the learned model was interpreted as a differential equation describing the blood flow where CO acts as an external force to the system. All materials used in this study, including code, model, raw data, processed data, and extracted features, are available on GitHub to facilitate further development.

The amount of people dealing with dementia is rising globally. The amount of caretakers is, however, not. Therefore, technological aids are needed to support people dealing with dementia and relieve the stress on their caretakers. Current solutions provide tracking of people with dementia. Also, different robots exist that provide people with companionship. However, no solution exists that combines tracking and companionship capabilities. Therefore, the Smart Teddy is introduced. The Smart Teddy can track different indicators that indicate the progress of dementia and simultaneously provide the user with companionship through interaction. The goal of this thesis is to design a data acquisition system that acquires meaningful data that can be used for the development of algorithms that will autonomously determine the progress of dementia. To achieve this, a system with a Teddy and a Base station has been designed. The Teddy has a sound-, a carbon monoxide-, a smoke- and a movement sensor. Also, a real-time module is implemented to be able to assign the current time to the measurement data. Lastly, a GPS and GSM module is implemented to be able to track seniors in case they wander. In the Base station, a mmWave sensor is implemented that tracks the position, velocity, and direction of the persons present in the room. Also, a processor is implemented that gathers and stores the data from the mmWave sensor and the data from the Teddy which is sent via a LoRa connection. In addition, the designed system can store the collected data for more than one week. The collected data can be used by an expert in dementia to extract meaningful information about dementia progress, after that, an expert in digital signal processing is needed to develop algorithms that estimate the quality of life of a senior suffering from dementia.