Vital signs monitoring system

Abstract

A vital signs monitoring system comprises a processing unit ( 300 ) for estimating an activity energy expenditure (AEE), a first activity energy expenditure determining unit ( 320 ) for determining a first activity energy expenditure (AEEHR) based on heart rate data (HR), a second activity energy expenditure determining unit ( 330 ) for determining a second activity energy expenditure (AEEAC) based on motion data (AC), and a weighting unit ( 340 ) for determining a first and second weighting factor (wHR, wAC) based on a first and second probability relating to a high exertion (hH) and relating to a low exertion (hL), and activity energy expenditure calculating unit ( 350 ) for computing an overall activity energy expenditure (AEEO) based on the first activity energy expenditure (AEEHR) weighted by the first weighting factor (wHR) and on the second activity energy expenditure (AEEAC) weighted by the second weighting factor (wAC).

1 . A method for measuring an overall activity expenditure of a user with a vital signs monitoring device, comprising the steps of:
 receiving heart rate data from at least one heart rate sensor of the vital signs monitoring device, the sensor configured to measure or determine a heart rate of the user;   receiving motion or acceleration data from at least one motion sensor of the vital signs monitoring device, the sensor configured to detect motion or acceleration data of the user;   determining, by a processor of the vital signs monitoring device, a first activity energy expenditure based on the received heart rate data;   determining, by the processor, a second activity energy expenditure based on the received motion or acceleration data;   calculating, by the processor, a first probability of a high exertion of the user and a second probability of a low exertion of the user;   extracting, by the processor, a feature set F based on at least one of the heart rate data and the motion or acceleration data, wherein the feature set F serve as predictors of a high or low exertion;   classifying, by the processor, an exertion level and outputting the first and second probability as a function of the feature set F;   determining, by the processor, a first weighting factor based on the first probability and a second weighting factor based on the second probability; and   computing, by the processor, an overall activity expenditure based on: (i) the first activity energy expenditure based on heart rate data and weighted by the first weighting factor;   and (ii) the second activity energy expenditure based on motion or acceleration data and weighted by the second weighting factor;   wherein the first probability of a high exertion of the user (h H ) is calculated using the formula: h H (F)=P(y=1/F; B c ), and the second probability of a low exertion of the user (h L ) is calculated using the formula: h L (F)=P(y=0|F; B c ), where B c  is a parameter set derived during a training phase for the user.   
     
     
         2 . The method of  claim 1 , wherein the at least one heart rate sensor is a photoplethysmographic sensor. 
     
     
         3 . The method of  claim 1 , wherein the at least one heart rate sensor comprises a contact surface configured to contact skin of the user. 
     
     
         4 . The method of  claim 1 , wherein the at least one motion sensor is an accelerometer. 
     
     
         5 . The method of  claim 1 , wherein the first weighting factor and the second weighting factor are a value between and including 0 and 1. 
     
     
         6 . The method of  claim 1 , wherein the step of classifying an exertion level by the processor comprises parameter pc configured to control a sensitivity of the exertion level classification. 
     
     
         7 . The method of  claim 1 , wherein the step of determining a first activity energy expenditure based on the received heart rate data comprises parameter P HR  configured to control a sensitivity of the determining the first activity energy expenditure. 
     
     
         8 . The method of  claim 1 , wherein the step of determining a second activity energy expenditure based on the received motion or acceleration data comprises parameter P AC  configured to control a sensitivity of the determining the second activity energy expenditure. 
     
     
         9 . The method of  claim 1 , wherein the vital signs monitoring device is a wearable device. 
     
     
         10 . The method of  claim 9 , wherein the wearable vital signs monitoring device is configured to be worn the user's arm, wrist, or hand. 
     
     
         11 . The method of  claim 9 , wherein the wearable vital signs monitoring device is configured to be worn on or about the user's head. 
     
     
         12 . A wearable system configured to monitor a user, comprising:
 at least one heart rate sensor configured to measure or determine a heart rate of the user;   at least one motion sensor configured to detect motion or acceleration data of the user; and   a processor configured to: (i) determine a first activity energy expenditure based on the received heart rate data; (ii) determine a second activity energy expenditure based on the received motion or acceleration data; (iii) calculate a first probability of a high exertion of the user and a second probability of a low exertion of the user; (iv) extract a feature set F based on at least one of the heart rate data and the motion or acceleration data, wherein the feature set F serve as predictors of a high or low exertion; (v) classify an exertion level and outputting the first and second probability as a function of the feature set F; (vi) determine a first weighting factor based on the first probability and a second weighting factor based on the second probability; and (vii) compute an overall activity expenditure based on the first activity energy expenditure based on heart rate data and weighted by the first weighting factor, and the second activity energy expenditure based on motion or acceleration data and weighted by the second weighting factor;   wherein the first probability of a high exertion of the user (h H ) is calculated by the processor using the formula: h H (F)=P(y=1/F; B c ), and the second probability of a low exertion of the user (h L ) is calculated using the formula: h L (F)=P(y=0|F; B c ), where B c  is a parameter set derived during a training phase for the user.   
     
     
         13 . The wearable system of  claim 12 , wherein the at least one heart rate sensor is a photoplethysmographic sensor. 
     
     
         14 . The wearable system of  claim 12 , wherein the at least one heart rate sensor comprises a contact surface configured to contact skin of the user. 
     
     
         15 . The wearable system of  claim 12 , wherein the vital signs monitoring device is a wearable device. 
     
     
         16 . The wearable system of  claim 12 , wherein the first weighting factor and the second weighting factor are a value between and including 0 and 1. 
     
     
         17 . The wearable system of  claim 12 , wherein classifying an exertion level by the processor comprises parameter pc configured to control a sensitivity of the exertion level classification. 
     
     
         18 . The wearable system of  claim 12 , wherein determining a first activity energy expenditure by the processor based on the received heart rate data comprises parameter P HR  configured to control a sensitivity of the determining the first activity energy expenditure. 
     
     
         19 . The wearable system of  claim 12 , wherein determining a second activity energy expenditure by the processor based on the received motion or acceleration data comprises parameter P AC  configured to control a sensitivity of the determining the second activity energy expenditure. 
     
     
         20 . The wearable system of  claim 12 , wherein the wearable system is a wearable device configured to be worn the user's arm, wrist, or hand, or to be worn on or about the user's head.