METHOD AND APPARATUS FOR PROVIDING DERVIED GLUCOSE INFORMATION UTILIZING PHYSIOLOGICAL AND/OR CONTEXTUAL PARAMETERS

• US 20140343370A1
• Filed: 04/14/2014
• Published: 11/20/2014
• Est. Priority Date: 10/09/2002
• Status: Abandoned Application

• Current Assignee: JB IP Acquisitions LLC
• Original Assignee: BodyMedia Incorporated (AliphCom Corp.)
• Inventors: Stivoric, John M., Teller, Eric, Andre, David, Vyas, Nisarg, Farringdon, Jonathan, Wolf, Donna, Pacione, Christopher, Vishnubhatla, Suresh, Safier, Scott, Pelletier, Raymond
• Application Number:
  US14/252,090
• Publication Number:
  US 20140343370A1
• Time in Patent Office: Days
• US Class Current: 600/301
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/002 Monitoring the patient using a local or closed circuit, e.g. in a room or building A61B5/0017 takes precedence
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/01 Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/0533 Measuring galvanic skin response
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/411 Detecting or monitoring allergy or intolerance reactions to an allergenic agent or substance
• CPC Class Codes: A61B 5/4866 Evaluating metabolism A61B5/083 takes precedence
• CPC Class Codes: A61B 5/6801 specially adapted to be attached to or worn on the body surface
• CPC Class Codes: A61B 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/6824 Arm or wrist
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 5/7475 User input or interface means, e.g. keyboard, pointing device, joystick
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 15/00 ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 20/30 : relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 20/60 : relating to nutrition control, e.g. diets
• CPC Class Codes: G16H 40/63 : for local operation
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders

Abstract

Systems and methods for non-invasively determining parameters related to blood glucose are disclosed. Embodiments are disclosed wherein a wearable sensor device comprises non-invasive sensors generating various sensed data which is then utilized to determine a glucose-related parameter.

Claims

1-11. -11. (canceled)

12. A system for non-invasively predicting data related to an individual'"'"'s glucose levels, the system comprising:

a wearable sensor device comprising;

(i) a first physiological sensor generating data indicative of a first physiological parameter when the sensor device is worn by the individual, (ii) a second physiological sensor generating data indicative of a second physiological parameter when the sensor device is worn by the individual, wherein each of the sensors is in noninvasive contact with the individual'"'"'s body, wherein the first physiological sensor generates data indicative of the individual'"'"'s heart, and the second physiological sensor is an accelerometer generating data indicative of the individual'"'"'s movement; and

(iii) a processor in electronic communication with the first and the second physiological sensors, the processor programmed using a model that includes as selected input parameters data from the first and the second physiological sensors of the wearable sensor device, the processor programmed to utilize the data indicative of the first physiological parameter and the data indicative of the second physiological parameter to generate output comprising a prediction of the individual'"'"'s blood glucose level;

wherein the processor requires only data from the first and the second physiological sensors to generate the prediction of the individual'"'"'s blood glucose level.

13. The system of claim 12 wherein the data indicative of the individual'"'"'s heart is heart rate.

14. The system of claim 12 wherein the data indicative of the individual'"'"'s heart is heart rate variability.

19. A system for non-invasively predicting data related to an individual'"'"'s glucose levels, the system comprising:

a wearable sensor device comprising;

(i) a first physiological sensor generating data indicative of a first physiological parameter when the sensor device is worn by the individual, and (ii) a second physiological sensor generating data indicative of a second physiological parameter when the sensor device is worn by the individual, wherein each of the sensors is in noninvasive contact with the individual'"'"'s body, wherein the first physiological sensor generates data indicative of the individual'"'"'s heart, and the second physiological sensor is a galvanic skin response sensor generating data indicative of the resistance of the individual'"'"'s skin to an applied electric current; and

(iii) a processor in electronic communication with the first and the second physiological sensors, the processor programmed using a model that includes as selected input parameters data from the first and the second physiological sensors of the wearable sensor device, the processor programmed to utilize the data indicative of the first physiological parameter and the data indicative of the second physiological parameter to generate output comprising a prediction of the individual'"'"'s blood glucose level;

wherein the programmed processor requires only data from the first and the second physiological sensors to generate the prediction of the individual'"'"'s blood glucose level.

20. The system of claim 19 wherein the data indicative of the individual'"'"'s heart is heart rate.

21. The system of claim 19 wherein the data indicative of the individual'"'"'s heart is heart rate variability.

22. The system of claim 19, further including a heat flux sensor generating data indicative of the rate of flow of heat off the individual'"'"'s body, and wherein the processor is programmed to utilize the data from the heat flux sensor in the prediction of the individual'"'"'s blood glucose level.

23. The system of claim 19 wherein the processor is further programmed to utilize data related to the individual'"'"'s food intake to generate the output comprising a prediction of the individual'"'"'s blood glucose level.

24. The system of claim 19 wherein the first and the second physiological sensors continuously generate data.