Health management

Abstract

A method includes capturing continuously vital signs and motion data from one or more sensors adapted to be coupled to a user; capturing food consumption of the user; predicting a predetermined health condition of the user based on the vital signs; generating a plan for the predetermined health condition; and prompting the user to execute the plan with a closed-loop feedback based on sensor data.

What is claimed is: 
     
         1 . A method, comprising:
 characterizing a personalized glucose prediction for a plurality of food types for a user;   capturing vital signs and motion data including one or more non-invasive glucose, vital sign and motion sensors adapted to be coupled to a user;   predicting a predetermined health condition of the user based on the personalized user glucose prediction, vital sign, and motion data detected by the wearable device;   generating a plan to control user glucose by for controlling diet and physical activity with feedback from the vital sign, motion data and glucose level; and   prompting the user with the wearable device to execute the plan.   
     
     
         2 . A method, comprising:
 capturing vital signs and motion data from a wearable device including one or more blood pressure, vital sign and motion sensors adapted to be coupled to a user;   performing non-invasive sensing using the wearable device to detect blood pressure level;   predicting a predetermined health condition of the user based on the vital signs and motion data collected by the wearable device;   generating a plan with the mobile device including addressing the predetermined health condition of blood pressure by controlling physical activity with feedback from the vital signs, motion data and blood pressure level; and   prompting the user to execute the plan.   
     
     
         3 . The method of  claim 1 , comprising determining an effect of a predetermined food item on glucose. 
     
     
         4 . The method of  claim 3 , comprising determining an effect of a second food item on glucose when taken with the predetermined food item. 
     
     
         5 . The method of  claim 1 , comprising prompting the user with the mobile device to perform a physical activity within a selected time period after a meal to keep glucose level in a predetermined range. 
     
     
         6 . The method of  claim 1 , comprising providing customized coaching with the mobile device to the user to achieve the plan. 
     
     
         7 . The method of  claim 1 , comprising adjusting via the mobile device a timing or intensity of a selected physical activity to maintain a glucose target. 
     
     
         8 . The method of  claim 1 , comprising estimating a glucose level using bio-impedance sensors. 
     
     
         9 . The method of  claim 1 , comprising measuring a user weight with a weight sensor and determining a weight loss program. 
     
     
         10 . The method of  claim 1 , comprising measuring user activity or exercise with the mobile device and determining a weight loss program. 
     
     
         11 . The method of  claim 1 , comprising determining via the mobile device user smoking or drug dependency and weaning the user from smoking or drug through a series of behavioral changes including exercise. 
     
     
         12 . The method of  claim 1 , wherein the predetermined health condition comprises high blood pressure. 
     
     
         13 . The method of  claim 1 , comprising cuff-less blood pressure sensing in a closed-loop feedback. 
     
     
         14 . The method of  claim 1 , comprising upon detecting a blood pressure above a threshold, immediately reducing blood pressure with a diuretics, an angiotensin-converting enzyme (ACE) inhibitor, an angiotensin II receptor blocker (ARB), calcium channel blocker, alpha blocker, alpha-beta blocker, beta blocker, renin inhibitor, aldosterone antagonist, garlic, music therapy, biofeedback, deep breathing, or exercise. 
     
     
         15 . The method of  claim 1 , comprising providing a biofeedback sensor to reduce blood pressure in real time. 
     
     
         16 . The method of  claim 1 , comprising training a neural network with user data from the mobile device to model user states. 
     
     
         17 . The method of  claim 16 , comprising performing with a remote computer distributed edge learning with differential privacy. 
     
     
         18 . The method of  claim 1 , comprising social networking with people having selected user health characteristics. 
     
     
         19 . The method of  claim 1 , wherein the coaching provides exercise feedback. 
     
     
         20 . The method of  claim 1 , comprising using a wrist device with a deep penetrating optical beam to detect glucose from veins under a skin.