DETERMINING CARDIOVASCULAR INTENSITY USING HEART RATE DATA

• US 20140309542A1
• Filed: 06/24/2014
• Published: 10/16/2014
• Est. Priority Date: 09/04/2012
• Status: Abandoned Application

• Current Assignee: Whoop, Inc.
• Original Assignee: Whoop, Inc.
• Inventors: Ahmed, William, Capodilupo, John, Nicolae, Aurelian
• Application Number:
  US14/313,537
• Publication Number:
  US 20140309542A1
• Time in Patent Office: Days
• US Class Current: 600/509
• CPC Class Codes: A61B 2560/0214 of power generation or supply
• CPC Class Codes: A61B 2560/0443 Modular apparatus
• CPC Class Codes: A61B 2560/0475 Special features of memory means, e.g. removable memory cards
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 2562/0238 Optical sensor arrangements for performing transmission measurements on body tissue
• CPC Class Codes: A61B 2562/0257 Proximity sensors
• CPC Class Codes: A61B 2562/227 Sensors with electrical connectors
• CPC Class Codes: A61B 5/0004 characterised by the type of physiological signal transmitted
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/0082 adapted for particular medical purposes
• 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 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/02427 Details of sensor
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/0255 Recording instruments specially adapted therefor
• 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 5/1112 Global tracking of patients, e.g. by using GPS
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/441 : Skin evaluation, e.g. for skin disorder diagnosis
• CPC Class Codes: A61B 5/4806 : Sleep evaluation A61B5/4821 takes precedence; devices for inducing sleep A61M21/02
• CPC Class Codes: A61B 5/4809 : Sleep detection, i.e. determining whether a subject is asleep or not
• CPC Class Codes: A61B 5/4812 : Detecting sleep stages or cycles
• CPC Class Codes: A61B 5/4815 : Sleep quality
• CPC Class Codes: A61B 5/4866 : Evaluating metabolism A61B5/083 takes precedence
• CPC Class Codes: A61B 5/681 : Wristwatch-type devices
• CPC Class Codes: A61B 5/6813 : Specially adapted to be attached to a specific body part A61B5/6802 takes precedence
• CPC Class Codes: A61B 5/6824 : Arm or wrist
• CPC Class Codes: A61B 5/6829 : Foot or ankle
• CPC Class Codes: A61B 5/6831 : Straps, bands or harnesses
• CPC Class Codes: A61B 5/684 : Indicating the position of the sensor on the body
• CPC Class Codes: A61B 5/6844 : Monitoring or controlling distance between sensor and tissue
• CPC Class Codes: A61B 5/7221 : Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• CPC Class Codes: A61B 5/7267 : involving training the classification device
• 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 5/7282 : Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 5/7285 : for synchronising or triggering a physiological measurement or image acquisition with a physiological event or waveform, e.g. an ECG signal A61B6/5205, A61B6/541, A61B8/5207, A61B8/543 take precedence
• CPC Class Codes: A61B 5/742 : using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/7475 : User input or interface means, e.g. keyboard, pointing device, joystick
• CPC Class Codes: A63B 2024/0065 : Evaluating the fitness, e.g. fitness level or fitness index
• CPC Class Codes: A63B 2220/803 : Motion sensors
• CPC Class Codes: A63B 2225/50 : Wireless data transmission, e.g. by radio transmitters or telemetry
• CPC Class Codes: A63B 2230/06 : heartbeat rate only
• CPC Class Codes: A63B 24/0003 : Analysing the course of a movement or motion sequences during an exercise or trainings sequence, e.g. swing for golf or tennis
• CPC Class Codes: A63B 24/0062 : Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance
• CPC Class Codes: A63B 24/0075 : Means for generating exercise programs or schemes, e.g. computerized virtual trainer, e.g. using expert databases
• CPC Class Codes: A63B 24/0087 : Electric or electronic controls for exercising apparatus of groups A63B21/00 - A63B23/00, e.g. controlling load
• CPC Class Codes: G06F 1/163 : Wearable computers, e.g. on a belt
• CPC Class Codes: G16H 20/30 : relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 20/40 : relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• CPC Class Codes: G16H 40/63 : for local operation

Abstract

Embodiments provide physiological measurement systems, devices and methods for continuous health and fitness monitoring. A wearable strap may automatically and continually sense and collect data corresponding to the heart rate of a user, which may then be used to determine cardiovascular intensity experienced by the user. Specifically, the heart rate data may be transformed into a time series of heart rate reserve data which is then weighted according to a weighting scheme. The weighted heart rate reserve data may be the basis for programmatically generating an indicator of cardiovascular intensity experienced by the user, which is displayed for a user on the user interface of a display device.

Claims

1. A computer program product comprising non-transitory computer executable code embodied in a non-transitory computer readable medium that, when executing on one or more computing devices, performs the steps of:

programmatically receiving, using a computer system, data corresponding to heart rate of a user during an exercise routine;

transforming the heart rate data to a time series of heart rate reserve data using a processing module of the computer system;

weighting the heart rate reserve data according to a weighting scheme using the processing module of the computer system;

programmatically generating, using the processing module of the computer system, an indicator of cardiovascular intensity based on the weighted heart rate reserve data; and

displaying, on a user interface rendered on a display device of the computer system, the indicator of cardiovascular intensity.

2. The computer program product of claim 1, wherein the weighting scheme uses a trained machine learning system implementing a machine learning algorithm embodied on one or more non-transitory computer-readable media, the machine learning system trained to correlate heart rate data to cardiovascular intensities.

3. The computer program product of claim 2, wherein the trained machine learning algorithm is retrained to adjust perceived difficulties of exercise routines as the user'"'"'s fitness improves.

4. The computer program product of claim 1, wherein the weighting scheme accounts for cardiovascular efficiencies at different intensity levels.

5. The computer program product of claim 1, further comprising code that performs the step of:

displaying qualitative information associated with the intensity score.

6. The computer program product of claim 5, wherein the qualitative information comprises one or more of:

an indication of whether the user exceeded the user'"'"'s anaerobic threshold during the exercise routine;

an indication of whether the user is likely to experience muscle soreness;

an indication of a level of recovery required after the exercise routine; and

an indication of one or more future alterations to the exercise routine that is required based on one or more health-related goals of the user.

7. The computer program product of claim 1, wherein the indicator corresponds to a perceived difficulty of the exercise routine by the user, the code further performing the step of:

displaying, on the user interface, the perceived difficulty of the exercise routine.

8. The computer program product of claim 1, further comprising code that performs the steps of:

based on the indicator of the intensity of the exercise, automatically altering an exercise plan according to one or more health goals of the user; and

displaying, on the user interface, the altered exercise plan.

9. The computer program product of claim 1, further comprising code that performs the steps of:

programmatically receiving data corresponding to heart rate of a second user during an exercise routine;

transforming the heart rate data to a time series of heart rate reserve data;

weighting the heart rate reserve data according to a weighting scheme;

programmatically generating a second indicator of cardiovascular intensity based on the weighted heart rate reserve data; and

displaying, on the user interface rendered on the display device, the indicator corresponding to the user and the second indicator corresponding to the second user.

10. The computer program product of claim 9, wherein the heart rate data of the user and the second user are obtained from different user-selected time periods.

11. A wearable physiological measurement device comprising:

a wearable strap couplable to an appendage of a user configured to monitor and determine a heart rate of the user, the wearable strap including a processing module configured to;

programmatically receive data corresponding to the heart rate of a user during an exercise routine;

transform the heart rate data to a time series of heart rate reserve data;

weight the heart rate reserve data according to a weighting scheme;

programmatically generate an indicator of cardiovascular intensity based on the weighted heart rate reserve data; and

display, on a user interface rendered on a display device, the indicator of cardiovascular intensity.

12. The wearable physiological measurement device of claim 11, wherein the weighting scheme uses a trained machine learning system implementing a machine learning algorithm embodied on one or more non-transitory computer-readable media, the machine learning system trained to correlate heart rate data to cardiovascular intensities.

13. The wearable physiological measurement device of claim 12, wherein the trained machine learning algorithm is retrained to adjust perceived difficulties of exercise routines as the user'"'"'s fitness improves.

14. The wearable physiological measurement device of claim 11, wherein the weighting scheme accounts for cardiovascular efficiencies at different intensity levels.

15. The wearable physiological measurement device of claim 11, wherein the processing module is further configured to:

display qualitative information associated with the intensity score.

16. The wearable physiological measurement device of claim 15, wherein the qualitative information comprises one or more of:

an indication of whether the user exceeded the user'"'"'s anaerobic threshold during the exercise routine;

an indication of whether the user is likely to experience muscle soreness;

an indication of a level of recovery required after the exercise routine; and

an indication of one or more future alterations to the exercise routine that is required based on one or more health-related goals of the user.

17. The wearable physiological measurement device of claim 11, wherein the indicator corresponds to a perceived difficulty of the exercise routine by the user, and wherein the processing module is further configured to:

display, on the user interface, the perceived difficulty of the exercise routine.

18. The wearable physiological measurement device of claim 11, wherein the processing module is further configured to:

based on the indicator of the intensity of the exercise, automatically alter an exercise plan according to one or more health goals of the user; and

display, on the user interface, the altered exercise plan.

19. The wearable physiological measurement device of claim 11, wherein the processing module is further configured to:

programmatically receive data corresponding to heart rate of a second user during an exercise routine;

transform the heart rate data to a time series of heart rate reserve data;

weight the heart rate reserve data according to a weighting scheme;

programmatically generate a second indicator of cardiovascular intensity based on the weighted heart rate reserve data; and

display, on the user interface rendered on the display device, the indicator corresponding to the user and the second indicator corresponding to the second user.

20. The wearable physiological measurement device of claim 19, wherein the heart rate data of the user and the second user are obtained from different user-selected time periods.