It's hard out there for a publisher. To me, good talkers are beautiful because good talk is what I love. For health monitoring which typically include healthy individuals who want to ensure the maintenance of good health, prediction and anomaly detection are used in the literature [ 22 ]. To design a real-time health monitoring system, such methods like NN, GMM, and frequency analysis are not efficient for the sake of their computational complexities. It is interesting to correlate how my addiction got worse and worse as technology got better and better. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements.
How can anyone know what's going on if you don't tell them? Due to the nature of vital signs, some of them are essentially counted as continuous such as ECG, heart rate and respiration rate [ 31 , 42 ] or discrete like blood glucose level [ 28 ]. Here, three major data gathering approaches have been identified such as experimental wearable sensor data, clinical or online databases of sensor data, and simulated sensor data. However, this is not always the case. Of course, everyone is different. I originally started with the website CAM4. The retrieved abnormal patterns in physiological data are significantly valuable in the medical domain.
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