Abstract: An exemplary method can include using meta-learning on various biological and/or behavior related data sets to generate model parameters for predicting biological and/or behavior predictions. Meta-learned model parameters can configure learning algorithms to rapidly train model/functions for predicting user biological and/or behavioral responses. In some embodiments, recommendations can be generated for a user based on predicted biological and/or one or more behavioral predictions. Corresponding systems are also disclosed.
Abstract: Various systems and methods are disclosed. One or more of the methods disclosed uses machine learning algorithms to predict biophysical responses from biophysical data, such as heart rate monitor data, food logs, or glucose measurements. Biophysical responses may include behavioral responses. Additional systems and methods extract nutritional information from food items by parsing strings containing names of food items.
Abstract: Various systems and methods are disclosed. One or more of the methods disclosed uses machine learning algorithms to predict biophysical responses from biophysical data, such as heart rate monitor data, food logs, or glucose measurements. Biophysical responses may include behavioral responses. Additional systems and methods extract nutritional information from food items by parsing strings containing names of food items.