VERSATILE SENSORS WITH DATA FUSION FUNCTIONALITY

• US 20130217979A1
• Filed: 03/15/2013
• Published: 08/22/2013
• Est. Priority Date: 12/02/2011
• Status: Active Grant

• Current Assignee: Luminradx UK Ltd.
• Original Assignee: Luminradx UK Ltd.
• Inventors: Blackadar, Thomas P., Monahan, David P.
• Granted Patent: US 9,734,304 B2
• Time in Patent Office: Days
• US Class Current: 600/301
• CPC Class Codes: A61B 2560/0209 adapted for power saving
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0024 for multiple sensor units attached to the patient, e.g. using a body or personal area network
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/6823 Trunk, e.g., chest, back, abdomen, hip
• CPC Class Codes: A61B 5/6824 Arm or wrist
• CPC Class Codes: A61B 5/6828 Leg
• CPC Class Codes: A61B 5/6829 Foot or ankle
• CPC Class Codes: A61B 5/7239 using differentiation including higher order derivatives
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: G01C 22/006 Pedometers
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 40/67 for remote operation

Abstract

Apparatuses and methods are disclosed for identifying with a single, small, intelligent activity monitor a particular type of activity from among a plurality of different activities. The monitor may include a multi-axis accelerometer and microcontroller configured to combine and process accelerometer data so as to generate features representative of an activity. The features may be processed to identify a particular activity (e.g., running, biking, swimming) from among a plurality of different activities that may include activities not performed by a human subject. An intelligent activity monitor may be configured to operate as a versatile sensor, or to operate in combination with a versatile sensor, to further receive and process physiological data and compute a fitness metric for a subject.

Claims

1. A fitness sensing system configured to be supported by a subject, the fitness sensing system comprising:

a processor;

at least one display;

a non-invasive blood glucose monitor; and

an intelligent activity monitor comprising an accelerometer;

wherein the processor is configured to process data received from the accelerometer to identify, from among a plurality of different activities, an activity performed by the subject and to compute at least one metric representative of the identified activity, and is further configured to process data from the blood glucose monitor in combination with the data received from the accelerometer to determine an operational mode, from among a plurality of operational modes, of the blood glucose monitor.

2. The fitness sensing system of claim 1, wherein the operational mode comprises a frequency at which blood glucose measurements are made.

3. The fitness sensing system of claim 1, wherein the operational mode comprises a calibration mode that calibrates an accuracy of blood glucose level readings for the non-invasive blood glucose monitor.

4. The fitness sensing system of claim 3, further comprising a blood glucose reader for disposable strips, wherein the calibration mode compensates for a difference between a blood glucose reading obtained by the non-invasive blood glucose monitor and a reading obtained by the blood glucose reader for the disposable strips.

5. The fitness sensing system of claim 1, wherein the processor is further configured to present on the at least one display an indication of blood glucose level responsive to a user tapping a housing containing the accelerometer.

6. The fitness sensing system of claim 1, wherein the processor is further configured to present on the at least one display an indication of the subject'"'"'s progress toward an activity goal responsive to a user tapping a housing containing the accelerometer.

7. The fitness sensing system of claim 1, wherein the blood glucose monitor comprises:

at least one laser source for interrogating a region of the subject; and

at least one photodetector for receiving radiation from the at least one laser source via the interrogated region of the subject.

8. The fitness sensing system of claim 7, wherein the processor is further configured to analyze plethysmography waveform data received from the at least one photodetector.

9. The fitness sensing system of claim 8, wherein the processor is further configured to compute a blood oxygenation level from the data received from the at least one photodetector.

10. The fitness sensing system of claim 9, wherein the processor is further configured to compute a fitness metric representative of a fitness level of the subject, wherein the fitness metric comprises a measured rate of caloric burn during an activity, a heart rate during the activity, and a blood oxygenation level during the activity.

11. The fitness sensing system of claim 7, wherein the processor is further configured to activate the blood glucose monitor to sense and provide heart rate data to the processor for a selected duration of time.

12. A method for operating a fitness sensing system that is configured to be supported by a subject, the method comprising:

processing, by a processor adapted to control the fitness sensing system, first data received from an accelerometer of the fitness sensing system to identify, from among a plurality of different activities, an activity performed by the subject;

computing, by the processor, at least one metric representative of the identified activity;

processing, by the processor, second data received from a non-invasive blood glucose monitor of the fitness sensing system; and

determining, by the processor, an operational mode, from among a plurality of operational modes, of the blood glucose monitor based upon the results of the at least one metric and the processing of the second data.

13. The method of claim 12, wherein the determining further comprises issuing, by the processor, a control signal to the blood glucose monitor to change a frequency at which blood glucose measurements are made.

14. The method of claim 12, wherein the determining further comprises:

detecting, by the processor, a low level of activity of the subject; and

issuing, by the processor, a control signal to the blood glucose monitor to reduce a frequency at which blood glucose measurements are made.

15. The method of claim 12, wherein the determining further comprises:

detecting, by the processor, a rate of change of a blood glucose level that exceeds a predetermined threshold; and

issuing, by the processor, a control signal to the intelligent activity monitor to collect acceleration data at an increased frequency.

16. The method of claim 12, wherein the determining further comprises:

detecting, by the processor, a blood glucose level that exceeds a predetermined threshold;

presenting, via the at least one display, a first signal to the subject to induce the subject to increase an activity level;

measuring, by the processor, an amount of activity performed by the subject subsequent the presenting of the first signal; and

presenting, via the at least one display, a second signal to the subject indicating that the subject has performed a predetermined recommended amount of activity.

17. The method of claim 12, wherein the determining further comprises:

detecting, by the processor, a rate of change in a blood glucose level that exceeds a predetermined threshold;

detecting, by the processor, a period of inactivity of the subject that encompasses the detected rate of change;

presenting, via the at least one display, a signal to the subject to induce the subject to perform an invasive blood glucose measurement using a disposable strip.

18. The method of claim 17, further comprising issuing, by the processor, a control signal to execute a calibration process that calibrates an accuracy of blood glucose level readings for the non-invasive blood glucose monitor.

19. The method of claim 18, further comprising:

receiving, by the processor, third data from a blood glucose reader for the disposable strip, wherein the blood glucose reader is controlled by the processor;

computing, by the processor, a first blood glucose level from the second data;

computing, by the processor, a second blood glucose level from the third data;

comparing, by the processor, the first blood glucose level and the second blood glucose level; and

adjusting, by the processor, a scaling factor for the non-invasive blood glucose monitor such that the first blood glucose level becomes approximately equal to the second blood glucose level.

20. The method of claim 12, further comprising:

detecting, by the processor, data representative of a tapping motion of the accelerometer; and

presenting, via the at least one display, an indication of blood glucose level responsive to the detected tapping.

21. The method of claim 12, further comprising:

detecting, by the processor, data representative of a tapping motion of the accelerometer; and

presenting, via the at least one display, an indication of the subject'"'"'s progress toward an activity goal responsive to the detected tapping.

22. The method of claim 12, further comprising:

interrogating, by at least one light source of the non-invasive blood glucose monitor, a region of the subject; and

receiving, by at least one photodetector, radiation from the at least one light source via the interrogated region of the subject.

23. The method of claim 22, further comprising analyzing, by the processor, a plethysmography waveform from data received from the at least one photodetector.

24. The method of claim 23, further comprising computing, by the processor, a blood oxygenation level from the data received from the at least one photodetector.

25. The method of claim 24, further comprising computing, by the processor, a fitness metric representative of a fitness level of the subject, wherein the fitness metric comprises a measured rate of caloric burn during an activity, a heart rate during the activity, and a blood oxygenation level during the activity.

26. The method of claim 22, further comprising:

activating, by the processor, the blood glucose monitor;

measuring, by the at least one laser source, a heart rate of the subject for a duration of time determined by the processor; and

providing, by the blood glucose monitor, heart rate data representative of the measured heart rate to the processor.

27. The method of claim 26, further comprising:

computing, by the processor, a fitness index value for the subject based at least upon the received heart rate data.