VERSATILE SENSORS WITH DATA FUSION FUNCTIONALITY

• US 20140156043A1
• Filed: 02/19/2014
• Published: 06/05/2014
• Est. Priority Date: 12/02/2011
• Status: Abandoned Application

• Current Assignee: Luminradx UK Ltd.
• Original Assignee: Fitlinxx Incorporated
• Inventors: Blackadar, Thomas P., Monahan, David P.
• Application Number:
  US14/184,042
• Publication Number:
  US 20140156043A1
• Time in Patent Office: Days
• US Class Current: 700/91
• 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:

an intelligent activity monitor comprising an accelerometer;

a heart-rate sensor; and

a processor in communication with the accelerometer and heart-rate sensor, wherein the processor is configured to process data received from the accelerometer and heart-rate sensor to compute a fitness metric F_m value that comprises a ratio of a pace P of an activity performed by the subject and a heart-rate H of the subject performing the activity.

2. The fitness sensing system of claim 1, further comprising a blood oxygenation sensor, wherein F_m further comprises a blood oxygenation level O of the subject performing the activity.

3. The fitness sensing system of claim 1, further comprising a respiratory sensor, wherein F_m further comprises a respiratory rate R of the subject performing the activity.

4. The fitness sensing system of claim 1, further comprising:

a respiratory sensor; and

a blood oxygenation sensor, wherein F_m is equivalent to the following expression;

5. The fitness sensing system of claim 4, wherein the blood oxygenation sensor comprises at least one light emitter.

6. The fitness sensing system of claim 4, wherein the respiratory sensor comprises at least one acoustic sensor.

7. The fitness sensing system of claim 1, further comprising a blood pressure sensor, wherein F_m further comprises a blood pressure of the subject performing the activity.

8. The fitness sensing system of claim 1, wherein F_m further comprises a body mass index.

9. The fitness sensing system of claim 1, wherein the processor is further configured to calculate a metabolic rate or caloric burn rate of the subject performing the activity and F_m further comprises the metabolic rate and/or caloric burn rate.

10. The fitness sensing system of claim 1, wherein P represents an equivalent pace calculated from a different activity using metabolic equivalents.

11. The fitness sensing system of claim 1, wherein the fitness metric is representative of a VO₂ max value.

12. The fitness sensing system of claim 1, wherein the fitness metric is representative of a standardized health index value.

13. The fitness sensing system of claim 1, wherein the fitness sensing system is disposed in a single wearable band.

14. The fitness sensing system of claim 1, further comprising a display configured to indicate a progress toward activity goals.

15. The fitness sensing system of claim 14, wherein the display comprises a plurality of light emitters arranged in a circle.

16. A method of operating a fitness sensing system configured to be supported by a subject, the method comprising:

receiving, by a processor, acceleration data from an accelerometer of the fitness sensing system;

receiving, by the processor, heart-rate data from a heart-rate sensor of the fitness sensing system;

computing a fitness metric F_m value that comprises a ratio of a pace P of an activity performed by the subject and a heart-rate H of the subject performing the activity.

17. The method of claim 16, further comprising receiving, by the processor, blood oxygenation data from a blood oxygenation sensor, wherein F_m further comprises a blood oxygenation level O of the subject performing the activity.

18. The method of claim 16, further comprising receiving, by the processor, respiratory data from a respiratory sensor, wherein F_m further comprises a respiratory rate R of the subject performing the activity.

19. The method of claim 16, further comprising:

receiving, by the processor, respiratory data from a respiratory sensor; and

receiving, by the processor, blood oxygenation data from a blood oxygenation sensor, wherein F_m is equivalent to the following expression;

20. The method of claim 16, further comprising receiving, by the processor, blood pressure data from a blood pressure sensor, wherein F_m further comprises a blood pressure of the subject performing the activity.

21. The method of claim 16, wherein computing F_m is further based on a body mass index for the subject.

22. The method of claim 16, further comprising:

calculating a metabolic rate or caloric burn rate of the subject performing the activity; and

computing F_m is further based on the metabolic rate and/or caloric burn rate.

23. The method of claim 16, further comprising computing an equivalent pace for P from a different activity using metabolic equivalents.

24. The method of claim 16, further comprising computing the fitness metric to be representative of a VO₂ max value.

25. The method of claim 16, further comprising computing the fitness metric to be representative of a standardized health index value.

26. The method of claim 16, further comprising indicating a stage of a disease based upon the fitness metric.

27. The method of claim 16, further comprising indicating a progress towards a vigorous activity goal recommended by the Center for Disease Control.