Abstract: In aspects, the present approaches allow neural networks to be taught to understand patterns of human behavior without the need of expert data labeling or laboratory studies. First and second neural networks are trained to understand these patterns without labeling. Once trained, the neural networks can be deployed with a trained classifier to determine or classify human activity based upon received sensor inputs.

Abstract: An estimate of a functional capacity such as VO2Max is made by applying the vital signs of a monitored human to a trained encoding neural network producing a cardio profile vector. The vector is applied to a trained functional capacity (VO2Max) neural network to estimate the functional capacity. Once estimated, an action is taken.

Abstract: Disclosed herein, in some aspects, are systems and methods for detecting, monitoring, and managing a cardiac health status for a subject using ECG data. In some embodiments, the system receives health parameter measurements from one or more devices that are then used by a cardiac health tool (CHT) to determine a cardiac health status. Exemplary health parameter measurements include electrocardiogram (ECG) data from an ECG device and/or weight (from a weight scale for example). As described herein, in some embodiments, determining the cardiac health status includes a) detecting a cardiac condition in the subject, b) predicting a risk of a subject developing a cardiac condition (“cardiac condition risk”), and/or c) temporal monitoring of a cardiac health status for a subject. In some embodiments, the cardiac health tool is configured to determine the efficacy of a treatment or therapy applied to reduce the severity and/or risk of a cardiac condition.