Measurement relating to human body

• US 6,540,686 B2
• Filed: 02/21/2001
• Issued: 04/01/2003
• Est. Priority Date: 02/23/2000
• Status: Expired due to Term

• Current Assignee: Polar Electro Oy
• Original Assignee: Polar Electro Oy
• Inventors: Nissilä, Seppo, Heikkilä, Ilkka
• Primary Examiner(s): Hindenburg, Max F.
• Assistant Examiner(s): Natnithithadha, Navin
• Application Number:
  US09/789,868
• Publication Number:
  US 20010016689A1
• Time in Patent Office: 769 Days
• Field of Search: 600/483, 600/481, 600/484, 600/485, 600/486, 600/490, 600/500, 600/508, 600/509, 600/300, 600/301, 128/897, 128/898, 128/925, 128/920, 128/923, 128/924
• US Class Current: 600/483
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/222 combined with detection or measurement of physiological parameters, e.g. heart rate
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: Y10S 128/925 Neural network

Abstract

A heart rate measurement arrangement wherein the heart rate measurement arrangement comprises a calculating unit comprising a mathematical model arranged to form a person'"'"'s energy metabolism level as an output parameter of the model using as input parameters of the model one or more heart rate parameters and one or more physiological parameters each describing a physiological characteristic of the person, the heart rate measurement arrangement further comprising display means for displaying information formed in the calculating unit.

Claims

1. A method for measuring human energy metabolism comprising the steps of:

measuring information on a person'"'"'s heart rate, the information including one or more heart rate parameters;

forming the person'"'"'s energy metabolism level as at least one output parameter by means of a mathematical model which uses input parameters, the input parameters including one or more of the heart rate parameters and one or more physiological parameters, each of the one or more physiological parameters describing a physiological characteristic of the person, the output parameter being at least one of;

a quality of energy metabolism;

an amount of energy left in the body; and

an amount of energy in the body as energy quality; and

estimating the person'"'"'s energy metabolism using the output parameter of the model.

2. A method as claimed in claim 1, wherein the output parameter of the model describing energy metabolism is formed on account of the person'"'"'s exercise stress level, the exercise stress level being estimated in the model by means of the information on the person'"'"'s heart rate and one or more exercise stress parameters describing the stressfulness of a fitness exercise.

3. A method as claimed in claim 1, wherein at least one of the energy metabolism quality and energy quality describe energy consumption in one or more energy components including at least one of:

carbohydrates;

fats; and

proteins.

4. A method for measuring human energy metabolism comprising the steps of:

measuring information on a person'"'"'s heart rate, the information including one or more heart rate parameters;

forming the person'"'"'s energy metabolism level as at least one output parameter by means of a mathematical model which uses input parameters, the input parameters including one or more of the heart rate parameters and one or more physiological parameters, each of the one or more physiological parameters describing a physiological characteristic of the person, the at least one output parameter describing energy metabolism formed on account of the person'"'"'s exercise stress level, the exercise stress level being estimated in the model by means of the information on the person'"'"'s heart rate level and one or more exercise stress parameters describing the stressfulness of a fitness exercise; and

estimating the person'"'"'s energy metabolism using the output parameter of the model.

5. A method for measuring human energy metabolism comprising the steps of:

measuring information on a person'"'"'s heart rate, the information including one or more heart rate parameters;

forming the person'"'"'s energy metabolism level as at least one output parameter by means of a mathematical model which uses input parameters, the input parameters including one or more of the heart rate parameters and one or more physiological parameters, each of the one or more physiological parameters describing a physiological characteristic of the person, the input parameters including at least one of;

an amount of obtained energy;

a quality of obtained energy;

a person'"'"'s activity;

a temperature of the person'"'"'s skin;

a temperature of the air surrounding the person'"'"'s body;

a person'"'"'s blood pressure;

an exercise stress parameter describing the hardness of exercise; and

an amount of respiratory gases; and

estimating the person'"'"'s energy metabolism using the output parameter of the model.

6. A method as claimed in claim 5, wherein the energy metabolism describe energy consumption in one or more energy components including at least one of:

carbohydrates;

fats; and

proteins.

7. A method for measuring human energy metabolism comprising the steps of:

measuring information on a person'"'"'s heart rate, the information including one or more heart rate parameters;

forming the person'"'"'s energy metabolism level as at least one output parameter by means of a mathematical model which uses input parameters, the input parameters including one or more of the heart rate parameters and one or more physiological parameters, each of the one or more physiological parameters describing a physiological characteristic of the person; and

estimating the person'"'"'s energy metabolism using the output parameter of the model, the energy metabolism being described in one or more energy components including at least one of;

carbohydrates;

fats; and

proteins.

8. A method for measuring human energy metabolism comprising the steps of:

measuring information on a person'"'"'s heart rate, the information including one or more heart rate parameters;

forming the person'"'"'s energy metabolism level as at least one output parameter by means of a mathematical model which uses input parameters, the mathematical model being a neural network and the input parameters including one or more of the heart rate parameters and one or more physiological parameters, each of the one or more physiological parameters describing a physiological characteristic of the person; and

estimating the person'"'"'s energy metabolism using the output parameter of the model.

9. A method as claimed in claim 8, wherein the model is trained on a basis of a collection of a large group of user information.

10. A method as claimed in claim 9, wherein in the training of the neural network, weighting coefficients between neurons of the neural network are calibrated by means of feedback by utilizing a value of one or more output parameters.

11. A method for measuring human energy metabolism comprising the steps of:

measuring information on a person'"'"'s heart rate, the information including one or more heart rate parameters, the heart rate parameters including at least one of;

a heart rate describing a heart beat frequency;

a standard deviation of the heart rate; and

a rate of change of the heart rate;

forming the person'"'"'s energy metabolism level as at least one output parameter by means of a mathematical model which uses input parameters, the input parameters including one or more of the heart rate parameters and one or more physiological parameters, each of the one or more physiological parameters describing a physiological characteristic of the person; and

estimating the person'"'"'s energy metabolism using the output parameter of the model.

12. A method for measuring human energy metabolism comprising the steps of:

measuring information on a person'"'"'s heart rate, the information including one or more heart rate parameters;

the information on the person'"'"'s heart rate being measured by a heart rate monitor;

forming the person'"'"'s energy metabolism level as at least one output parameter by means of a mathematical model which uses input parameters, the input parameters including one or more of the heart rate parameters and one or more physiological parameters, each of the one or more physiological parameters describing a physiological characteristic of the person; and

estimating the person'"'"'s energy metabolism using the output parameter of the model.

13. A method for measuring human energy metabolism comprising the steps of:

measuring information on a person'"'"'s heart rate, the information including one or more heart rate parameters;

forming the person'"'"'s energy metabolism level as at least one output parameter by means of a mathematical model contained in a heart rate monitor which uses input parameters, the input parameters including one or more of the heart rate parameters and one or more physiological parameters, each of the one or more physiological parameters describing a physiological characteristic of the person; and

estimating the person'"'"'s energy metabolism using the output parameter of the model.

14. A method for measuring human energy metabolism comprising the steps of:

measuring information on a person'"'"'s heart rate, the information including one or more heart rate parameters;

forming the person'"'"'s energy metabolism level as at least one output parameter by means of a mathematical model which uses input parameters, the input parameters including one or more of the heart rate parameters and one or more physiological parameters, each of the one or more physiological parameters describing a physiological characteristic of the person, the output parameter being readable from a display of a heart rate monitor; and

estimating the person'"'"'s energy metabolism using the output parameter of the model.

15. A method for measuring the amount of glucose in a human body comprising the steps of:

measuring information on a person'"'"'s heart rate, the information including one or more heart rate parameters;

forming at least one of the person'"'"'s glucose content level and an amount of glucose left in the person'"'"'s body as at least one output parameter by means of a mathematical model for modelling human glucose consumption which uses input parameters, the input parameters including one or more of the heart rate parameters and one or more physiological parameters, each of the one or more physiological parameters describing a physiological characteristic of the person, the output parameter being formed on account of the person'"'"'s exercise stress level, the exercise stress level being estimated in the model by means of the information on the person'"'"'s heart rate and one or more exercise stress parameters which describe a stressfulness of a fitness exercise; and

estimating at least one of an amount of glucose used and the amount of glucose left in the body using the output parameter of the model.

16. A method as claimed in claim 15, wherein the glucose consumption is described in one or more energy components including at least one of:

carbohydrates;

fats; and

proteins.

24. A method as claimed in claim 15, wherein the one or more physiological parameters include at least one of age, sex, height, and weight.

25. A method as claimed in claim 15, wherein the information on the person'"'"'s heart rate is measured by a heart rate monitor.

17. A method for measuring the amount of glucose in a human body comprising the steps of:

measuring information on a person'"'"'s heart rate, the information including one or more heart rate parameters;

forming at least one of the person'"'"'s glucose content level and an amount of glucose left in the person'"'"'s body as at least one output parameter by means of a mathematical model for modelling human glucose consumption which uses input parameters, the input parameters including one or more of the heart rate parameters and one or more physiological parameters, each of the one or more physiological parameters describing a physiological characteristic of the person, the input parameters including at least one of;

an amount of obtained energy;

a quality of obtained energy;

a person'"'"'s activity;

a temperature of the person'"'"'s skin;

a temperature of the air surrounding the person'"'"'s body;

a person'"'"'s blood pressure;

an exercise stress parameter describing the hardness of exercise; and

an amount of respiratory gases; and

estimating at least one of an amount of glucose used and the amount of glucose left in the body using the output parameter of the model.

18. A method as claimed in claim 17, wherein the glucose consumption is described in one or more energy components including at least one of:

carbohydrates;

fats; and

proteins.

19. A method for measuring the amount of glucose in a human body comprising the steps of:

measuring information on a person'"'"'s heart rate, the information including one or more heart rate parameters;

forming at least one of the person'"'"'s glucose content level and an amount of glucose left in the person'"'"'s body as at least one output parameter by means of a mathematical model for modelling human glucose consumption which uses input parameters, the input parameters including one or more of the heart rate parameters and one or more physiological parameters, each of the one or more physiological parameters describing a physiological characteristic of the person, the glucose consumption is described in one or more energy components including at least one of;

carbohydrates;

fats; and

proteins; and

estimating at least one of an amount of glucose used and the amount of glucose left in the body using the output parameter of the model.

20. A method for measuring the amount of glucose in a human body comprising the steps of:

measuring information on a person'"'"'s heart rate, the information including one or more heart rate parameters;

forming at least one of the person'"'"'s glucose content level and an amount of glucose left in the person'"'"'s body as at least one output parameter by means of a mathematical model for modelling human glucose consumption which uses input parameters, the mathematical model being a neural network and the input parameters including one or more of the heart rate parameters and one or more physiological parameters, each of the one or more physiological parameters describing a physiological characteristic of the person; and

estimating at least one of an amount of glucose used and the amount of glucose left in the body using the output parameter of the model.

21. A method as claimed in claim 20, wherein the model is trained on a basis of a collection of a large group of user information.

22. A method as claimed in claim 21, wherein in the training of the neural network, weighting coefficients between neurons of the neural network are calibrated by means of feedback by utilizing a value of one or more output parameters.

23. A method for measuring the amount of glucose in a human body comprising the steps of:

measuring information on a person'"'"'s heart rate, the information including one or more heart rate parameters, the heart rate parameters including at least one of;

a heart rate describing a heart beat frequency;

a standard deviation of the heart rate; and

a rate of change of the heart rate;

forming at least one of the person'"'"'s glucose content level and an amount of glucose left in the person'"'"'s body as at least one output parameter by means of a mathematical model for modelling human glucose consumption which uses input parameters, the input parameters including one or more of the heart rate parameters and one or more physiological parameters, each of the one or more physiological parameters describing a physiological characteristic of the person; and

estimating at least one of an amount of glucose used and the amount of glucose left in the body using the output parameter of the model.

26. A method for measuring the amount of glucose in a human body comprising the steps of:

measuring information on a person'"'"'s heart rate, the information including one or more heart rate parameters;

forming at least one of the person'"'"'s glucose content level and an amount of glucose left in the person'"'"'s body as at least one output parameter by means of a mathematical model for modelling human glucose consumption which uses input parameters, the mathematical model being contained in a heart rate monitor and the input parameters including one or more of the heart rate parameters and one or more physiological parameters, each of the one or more physiological parameters describing a physiological characteristic of the person; and

estimating at least one of an amount of glucose used and the amount of glucose left in the body using the output parameter of the model.

27. A method for measuring the amount of glucose in a human body comprising the steps of:

measuring information on a person'"'"'s heart rate, the information including one or more heart rate parameters;

forming at least one of the person'"'"'s glucose content level and an amount of glucose left in the person'"'"'s body as at least one output parameter by means of a mathematical model for modelling human glucose consumption which uses input parameters, the input parameters including one or more of the heart rate parameters and one or more physiological parameters, each of the one or more physiological parameters describing a physiological characteristic of the person, the output parameter being readable from a display of a heart rate monitor; and

estimating at least one of an amount of glucose used and the amount of glucose left in the body using the output parameter of the model.

28. A heart rate measurement arrangement comprising:

a calculating unit having a mathematical model arranged to form a person'"'"'s energy metabolism level as an output parameter of the model using as input parameters of the model one or more heart rate parameters and one or more physiological parameters each describing a physiological characteristic of the person;

a display means for displaying information formed in the calculating unit;

an electrode belt for measuring heart rate, the electrode belt having a transmitter; and

a receiver unit having a receiver for receiving information transmitted from at least one of the transmitter, the calculating unit, and the display means.

29. A heart rate measurement arrangement as claimed in claim 28, wherein the display means is a display of the receiver unit.

30. A heart rate measurement arrangement comprising:

a calculating unit having a mathematical model arranged to form a person'"'"'s energy metabolism level as an output parameter of the model using as input parameters of the model one or more heart rate parameters and one or more physiological parameters each describing a physiological characteristic of the person, the mathematical model being configured so that the output parameter is at least one of;

a quality of energy metabolism;

an amount of energy left in the body; and

an amount of energy in the body as energy quality; and

a display means for displaying information formed in the calculating unit.

31. A heart rate measurement arrangement as claimed in claim 30, wherein at least one of the quality of energy metabolism and the amount of energy in the body as energy quality describe energy consumption in one or more energy components including at least one of:

carbohydrates;

fats; and

proteins.

32. A heart rate measurement arrangement as claimed in claim 30, wherein the mathematical model is arranged to form the output parameter on account of the person'"'"'s exercise stress level, the exercise stress level being formed in the model by means of the person'"'"'s heart rate and one or more exercise stress parameters describing the stressfulness of a fitness exercise.

33. A heart rate measurement arrangement comprising:

a calculating unit having a mathematical model arranged to form a person'"'"'s energy metabolism level as an output parameter of the model using as input parameters of the model one or more heart rate parameters and one or more physiological parameters each describing a physiological characteristic of the person, the output parameter being formed on account of the person'"'"'s exercise stress level, the exercise stress level being formed in the model by means of the information on the person'"'"'s heart rate and one or more exercise stress parameters which describe a stressfulness of a fitness exercise; and

a display means for displaying information formed in the calculating unit.

34. A heart rate measurement arrangement comprising:

a calculating unit having a mathematical model arranged to form a person'"'"'s energy metabolism level as an output parameter of the model using as input parameters of the model one or more heart rate parameters and one or more physiological parameters each describing a physiological characteristic of the person; and

a display means for displaying information formed in the calculating unit,

wherein the person'"'"'s energy metabolism level is described as energy consumption in at least one energy components including at least one of;

carbohydrates;

fats; and

proteins.

35. A heart rate measurement arrangement as claimed in claim wherein the input parameters including at least one of:

an amount of obtained energy;

a quality of obtained energy;

a person'"'"'s activity;

a temperature of the person'"'"'s skin;

a temperature of the air surrounding the person'"'"'s body;

a person'"'"'s blood pressure;

an exercise stress parameter describing the hardness of exercise; and

an amount of respiratory gases.

36. A heart rate measurement arrangement comprising:

a calculating unit having a mathematical model, the mathematical model being a neural network arranged to form a person'"'"'s energy metabolism level as an output parameter of the model using as input parameters of the model one or more heart rate parameters and one or more physiological parameters each describing a physiological characteristic of the person; and

a display means for displaying information formed in the calculating unit.

37. A heart rate measurement arrangement as claimed in claim 36, wherein the model is trained on a basis of a collection of a large group of user information.

38. A heart rate measurement arrangement as claimed in claim 37, wherein in the training of the neural network, weighting coefficients between neurons of the neural network are calibrated by means of feedback by utilizing the value of one or more output parameters.

39. A heart rate measurement arrangement comprising:

a calculating unit having a mathematical model arranged to form a person'"'"'s energy metabolism level as an output parameter of the model using as input parameters of the model one or more heart rate parameters and one or more physiological parameters each describing a physiological characteristic of the person, the heart rate parameters including at least one of;

a heart rate describing a heart beat frequency;

a standard deviation of the heart rate; and

a rate of change of the heart rate; and

a display means for displaying information formed in the calculating unit.

40. A heart rate measurement arrangement comprising:

a calculating unit having a mathematical model arranged to form at least one of a person'"'"'s glucose content level and amount of glucose left in a person'"'"'s body as an output parameter of the model using as input parameters of the model one or more heart rate parameters and one or more physiological parameters each describing a physiological characteristic of the person;

a display means for displaying information formed in the calculating unit;

an electrode belt for measuring heart rate, the electrode belt having a transmitter; and

a receiver unit having a receiver for receiving information transmitted from at least one of the transmitter, the calculating unit, and the display means.

41. A heart rate measurement arrangement as claimed in claim 40, wherein the electrode belt includes at least two electrodes for identifying a heart beat from an electric signal transmitted by a heart and for transmitting the measured heart rate information to the receiver unit.

42. A heart rate measurement arrangement as claimed in claim 40, wherein the display means is a display of the receiver unit.

43. A heart rate measurement arrangement as claimed in claim 41, wherein the receiver of the receiver unit is arranged to receive the information transmitted wirelessly by the transmitter of the electrode belt.

44. A heart rate measurement arrangement comprising:

a calculating unit having a mathematical model arranged to form at least one of a person'"'"'s glucose content level and an amount of glucose left in a person'"'"'s body as an output parameter of the model using as input parameters of the model one or more heart rate parameters and one or more physiological parameters each describing a physiological characteristic of the person, the output parameter of the model describing glucose consumption being formed on account of a person'"'"'s exercise stress level, the exercise stress level being estimated in the model by means of the person'"'"'s heart rate parameters and one or more exercise stress parameters which describe the stressfulness of a fitness exercise; and

a display means for displaying information formed in the calculating unit.

45. A heart rate measurement arrangement comprising:

a calculating unit having a mathematical model arranged to form at least one of a person'"'"'s glucose content level and amount of glucose left in a person'"'"'s body as an output parameter of the model using as input parameters of the model one or more heart rate parameters and one or more physiological parameters each describing a physiological characteristic of the person, the input parameters including at least one of;

an amount of obtained energy;

a quality of obtained energy;

a person'"'"'s activity;

a temperature of the person'"'"'s skin;

a temperature of the air surrounding the person'"'"'s body;

a person'"'"'s blood pressure;

an exercise stress parameter describing the hardness of exercise; and

an amount of respiratory gases; and

a display means for displaying information formed in the calculating unit,

wherein the output parameter is described as energy consumption in at least one energy components including at least one of;

carbohydrates;

fats; and

proteins.

46. A heart rate measurement arrangement comprising:

a calculating unit having a mathematical model, the mathematical model being a neural network arranged to form at least one of a person'"'"'s glucose content level and amount of glucose left in a person'"'"'s body as an output parameter of the model using as input parameters of the model one or more heart rate parameters and one or more physiological parameters each describing a physiological characteristic of the person; and

a display means for displaying information formed in the calculating unit.

47. A heart rate measurement arrangement as claimed in claim 46, wherein the model is trained on a basis of a collection of a large group of user information.

48. A heart rate measurement arrangement as claimed in claim 47, wherein in the training of the neural network, weighting coefficients between neurons of the neural network are calibrated by means of feedback by utilizing the value of one or more output parameters.

49. A heart rate measurement arrangement comprising:

a calculating unit having a mathematical model arranged to form at least one of a person'"'"'s glucose content level and amount of glucose left in a person'"'"'s body as an output parameter of the model using as input parameters of the model one or more heart rate parameters and one or more physiological parameters each describing a physiological characteristic of the person, the heart rate parameters including at least one of;

a heart rate describing a heart beat frequency;

a standard deviation of the heart rate; and

a rate of change of the heart rate; and

a display means for displaying information formed in the calculating unit.