1. A method comprising, by one or more processors associated with one or more computing devices: accessing, by one or more of the processors, one or more data streams from a plurality of sensors, the sensors comprising a mood sensor and one or more of a heart-rate monitor, a blood-pressure monitor, a pulse oximeter, or an accelerometer, wherein: the data streams comprise self-reported mood data of a person from the mood sensor and one or more of heart-rate data of the person from the heart-rate monitor, blood-pressure data of the person from the blood-pressure monitor, pulse-oximetry data of the person from the pulse oximeter, or accelerometer data of the person from the accelerometer; a first data set from the data streams was collected from the person at a first time, the person being substantially stressed at the first time; and a second data set from the data streams was collected from the person at a second time, the person being substantially unstressed at the second time; accessing, by one or more of the processors, a stress model comprising baseline renal-Doppler data, baseline self-reported mood data, and one or more of baseline heart-rate data, baseline blood-pressure data, baseline pulse-oximetry data, or baseline accelerometer data, wherein the baseline-renal-Doppler data measures a stress response of the sympathetic nervous system, and wherein the stress model correlates the baseline renal-Doppler data with the baseline self-reported mood data and one or more of the baseline heart-rate data, the baseline blood-pressure data, the baseline pulse-oximetry data, or the baseline accelerometer data; analyzing, by one or more of the processors, the first data set and second data set with respect to each other and with respect to the stress model; and determining, by one or more of the processors, a current stress index of the person based on the analysis of the first data set and second data set with respect to each other and with respect to the stress model.