Claims:WE CLAIM:
1. A method for non-invasive monitoring of a blood glucose level of a user, the method comprising:
determining, by a glucose monitoring device, an electrical skin impedance between a first point and a second point of a surface of skin of the user using a skin impedance sensor, wherein the electrical skin impedance is indicative of an opacity of the surface of the skin between the first point and the second point;
determining, by the glucose monitoring device, a temperature and a hyper spectral signature of the skin of the user using a temperature sensor and a hyperspectral sensor;
updating, by the glucose monitoring device, a light intensity of a light source based on the temperature and the hyperspectral signature, wherein the surface of the skin is illuminated based on the updated light intensity of the light source;
computing, by the glucose monitoring device, a blood glucose level using the temperature, the hyper spectral signature, and the electrical skin impedance; and
providing, by the glucose monitoring device, the computed blood glucose level to the user.
2. The method of claim 1, wherein the updation of the light intensity of the light source is based on a pre-trained machine learning regression model.
3. The method of claim 1, further comprising determining an updated temperature and an updated hyper spectral signature of the skin of the user after the surface of the skin is illuminated based on the updated light intensity of the light source.
4. The method of claim 3, wherein the updation of the light intensity of the light source is performed iteratively until the updated temperature and the updated hyper spectral signature is within a pre-defined range.
5. The method of claim 1, wherein an average blood glucose level is determined based on a number of historical data of the blood glucose level.
6. The method of claim 1, wherein the electrical skin impedance is utilized to detect a skin touch.
7. The method of claim 1, further comprising transmitting at least one of the electrical skin impedance, the temperature, the hyperspectral signature, and the computed blood glucose level to a user-computing device, wherein the user-computing device transmits one or more control signals to the glucose monitoring device.
8. The method of claim 7, wherein the user-computing device performs one or more operations comprising running data acquisition, stabilization of the hyperspectral signature and analyzing spectral algorithm.
9. A glucose monitoring device to monitor a blood glucose level of a user, the glucose monitoring device comprising:
a processor; and
a memory communicatively coupled to the processor, wherein the memory stores
processor instructions, which, on execution, causes the processor to:
determine an electrical skin impedance between a first point and a second point of a surface of skin of the user using a skin impedance sensor, wherein the electrical skin impedance is indicative of an opacity of the surface of the skin between the first point and the second point;
determine a temperature and a hyper spectral signature of the skin of the user using a temperature sensor and a hyperspectral sensor;
update a light intensity of a light source based on the temperature and the hyper spectral signature, wherein the surface of the skin is illuminated based on the updated light intensity of the light source;
compute a blood glucose level using the temperature, the hyper spectral signature, and the electrical skin impedance; and
provide the computed blood glucose level to the user.
10. The glucose monitoring device of claim 9, wherein the processor is further configured to
update the light intensity of the light source is based on a pre-trained machine learning regression model.
11. The glucose monitoring device of claim 9, wherein the processor is further configured to determine an updated temperature and an updated hyper spectral signature of the skin of the user after the surface of the skin is illuminated based on the updated light intensity of the light source.
12. The glucose monitoring device of claim 11, wherein the processor is further configured to update the light intensity of the light source iteratively until the updated temperature and the updated hyper spectral signature is within a pre-defined range.
13. The glucose monitoring device of claim 9, wherein the processor is further configured to determine an average blood glucose level based on a number of historical data of the blood glucose level.
14. The glucose monitoring device of claim 9, wherein the processor is further configured to utilize the electrical skin impedance to detect a skin touch.
15. The glucose monitoring device of claim 9, wherein the processor is further configured to transmit at least one of the electrical skin impedance, the temperature, the hyperspectral signature, and the computed blood glucose level to a user-computing device, wherein the user-computing device transmits one or more control signals to the glucose monitoring device.
16. The glucose monitoring device of claim 15, wherein the user-computing device performs one or more operations comprising running data acquisition, stabilization of the hyperspectral signature and analyzing spectral algorithm.

Dated this 21st day of March, 2017

R Ramya Rao
Of K&S Partners
Agent for the Applicant
, Description:TECHNICAL FIELD
The present subject matter is related, in general to monitoring blood glucose levels and more specifically, but not exclusively to a method and a device for non-invasive monitoring of blood glucose level of a user.