Fever is one of the main symptoms of various illnesses during infancy and it requires continuous supervision especially above 38 degree Celsius as fatal complications may develop. With the emerging IoT and e-Health technologies, a variety of different consumer products are being developed to facilitate the fever monitoring for parents. In this study, our aim is to develop a prototype that will continuously track the body temperature of an infant using an RGB and a thermal camera, and provide different notification capabilities for the users. The product is expected to work effectively from 1-2 meters of distance with approximately 0.4 degree Celsius accuracy. The real-time video display is included to the system with one second of latency for video, audio and alarm features. The user interface also provides room temperature and humidity data, along with measured statistics of baby body temperature. The system is also able to alarm the user upon the detection of loud noise in the environment, which can indicate interruptions in baby's sleep. The prototype is expected to provide an affordable, all-in-one solution for the parents as a baby monitoring device.
Multi-shell dMRI metrics quantify information on micro-properties of neural tissue and can be used as markers for neurological diseases. The protocols used to acquire dMRI data often have prolonged acquisition times. In this work, we propose different undersampling strategies that reduce the acquisition time to half, and evaluate how the performances of multi-shell dMRI metrics change under these strategies. The results show that, while the best performing strategy changes for each metric, 3-shell gradient schemes with small variance of b-vector density on consecutive shells demonstrate improved performance. Additionally, more complex dMRI metrics exhibit relatively increased sensitivity to undersampling.
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