and is constantly re learned and adapted from the acquired collected biometric 102 and environmental data 101 and 103 for example from current and previous times because the form of the predictive model 160 is of the kind known in the field of machine learning as a regression model any number of machine learning procedures can be used to learn that model from the acquired data provided that the data are organized in a suitable format machine learning procedures can discover a hidden relationship between vectors of input data variables x and output data variables y on the basis of past data 151 collected and organized into a training set of m examples where each example is a pair x k y k k 1 to m the input vector x x 1 x 2 x n has n components that can either be direct biometric measurements 102 for example skin temperature or heart rate from the wearable device or environmental conditions 101 and 103 or can be derived from them by means of mathematical expressions or extraction procedures for example estimates of overall activity levels or calories burned computed from accelerometer sensors that measure motion and activity some components of the input vector x i can be time averages of biometric measurements are used as input features of the predictive model such as the average heart rate over the last 10 minutes or derived expressions that involve this data the output variable y 170 indicates the.