A Study on the Influence of Sensors in Frequency and Time Domains on Context Recognition.

Adaptive AI for context and activity recognition remains a relatively unexplored field due to difficulty in collecting sufficient information to develop supervised models. Additionally, building a dataset for human context activities "in the wild" demands time and human resources, which explains the lack of public datasets available. Some of the available datasets for activity recognition were collected using wearable sensors, since they are less invasive than images and precisely capture a user's movements in time series.

[Comparative study between fast and slow induction of propofol given by target-controlled infusion: expected propofol concentration at the effect site. Randomized controlled trial].

Background And Objective: studies have shown that rate of propofol infusion may influence the predicted propofol concentration at the effect site (Es). The aim of this study was to evaluate the Es predicted by the Marsh pharmacokinetic model (ke0 0.26min(-1)) in loss of consciousness during fast or slow induction.