A meta-learning algorithm for respiratory flow prediction from FBG-based wearables in unrestrained conditions

Methods for the metrological characterization of wearable devices for the measurement of physiological signals: state of the art and future challenges

“…Many physiological parameters can be measured (directly or indirectly, through estimation processes that sometimes are guided by AI algorithms), such as heart rate (HR) and its variability (HRV) [18] , [19] , [20] , [21] , energy expenditure [22 , 23] , blood pressure [24 , 25] , breathing activity [26 , 27] , thermal comfort [28 , 29] , etc. Such parameters are very relevant since they can depict the overall health status of a subject and also support the early detection of pathological symptoms, hence limiting also the risk of contagion [30] .…”