Device for physiological monitoring and measurement

• US 10,182,726 B2
• Filed: 01/14/2016
• Issued: 01/22/2019
• Est. Priority Date: 09/04/2012
• Status: Active Grant

• Current Assignee: Whoop, Inc.
• Original Assignee: Whoop, Inc.
• Inventors: Ahmed, William, Capodilupo, John, Nicolae, Aurelian
• Primary Examiner(s): Mallari, Patricia
• Assistant Examiner(s): CATINA, MICHAEL ANTHONY
• Application Number:
  US14/995,874
• Publication Number:
  US 20160129310A1
• Time in Patent Office: 1,104 Days
• 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

Continuous monitoring of a plurality of physiological data may be used for health and fitness improvements for a user. To this end, a physiological monitoring and measurement device may include a wearable strap that receives heart rate data for a user including a time series of heart rate data, a maximum heart rate, and a resting heart rate. A processor may transform the heart rate data into a time series of heart rate reserve data that is weighted, e.g., to account for cardiovascular efficiencies at different intensity levels, to provide a weighted time series of heart rate reserve values. An intensity score that provides an indicator of cardiovascular intensity for an exercise routine may be generated from the weighted time series of heart rate reserve values and displayed to the user on the wearable strap.

Claims

1. A device for physiological monitoring and measurement comprising:

a continuously wearable physiological measurement device including one or more sensors configured to continuously monitor and determine a heart rate of a user;

a display device coupled to the continuously wearable physiological measurement device;

a strap attached to the continuously wearable physiological measurement device and couplable to an appendage of the user; and

a processor in the continuously wearable physiological measurement device, the processor coupled to the one or more sensors and the processor configured to;

programmatically receive heart rate data for the user from the continuously wearable physiological measurement device, the heart rate data including a time series of heart rate data for the user, a maximum heart rate of the user, and a resting heart rate of the user;

transform the heart rate data including the time series of heart rate data, the maximum heart rate and the resting heart rate to a time series of heart rate reserve data;

weight the heart rate reserve data according to a dynamic weighting scheme to provide a dynamically weighted time series of heart rate reserve values, the dynamic weighting scheme based on historical heart rate reserve data;

sum and normalize the dynamically weighted time series of heart rate reserve values for a predetermined unit interval to provide a summed and normalized value;

programmatically generate an intensity score providing an indicator of cardiovascular intensity based on the dynamically weighted time series of heart rate reserve values, wherein generating the intensity score includes scaling the summed and normalized value to a predetermined distribution in a scale, and further wherein a path of the intensity score over time is used to dynamically determine and adjust the dynamic weighting scheme applied to the heart rate reserve data; and

display the intensity score on a user interface rendered on the display device.

2. The device of claim 1, wherein the dynamic weighting scheme is created for the user by a machine learning algorithm trained to correlate heart rate data to cardiovascular intensities.

3. The device of claim 2, wherein the machine learning algorithm is retrained to adjust perceived difficulties of exercise routines as a fitness of the user improves.

4. The device of claim 1, wherein the dynamic weighting scheme accounts for cardiovascular efficiencies at different intensity levels.

5. The device of claim 1, wherein the processor is further configured to display qualitative information associated with the intensity score.

6. The device of claim 5, wherein the qualitative information includes an indication of whether the user exceeded an anaerobic threshold of the user during an exercise routine.

7. The device of claim 5, wherein the qualitative information includes an indication of whether the user is likely to experience muscle soreness.

8. The device of claim 5, wherein the qualitative information includes an indication of a level of recovery required after an exercise routine.

9. The device of claim 5, wherein the qualitative information includes an indication of one or more future alterations to an exercise routine that is required based on one or more health-related goals of the user.

10. The device of claim 1, wherein the indicator corresponds to a perceived difficulty of an exercise routine by the user, and wherein the processor is further configured to:

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

11. The device of claim 1, wherein the processor is further configured to:

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

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

12. The device of claim 1, wherein the predetermined distribution is a linear distribution.

13. The device of claim 1, wherein the predetermined distribution is one or more of an arctangent distribution, a sigmoid distribution, and a sinusoidal distribution.

14. The device of claim 1, wherein the predetermined distribution is obtained by fitting a curve to a predetermined group of canonical exercise routines have predefined intensity scores.

15. The device of claim 1, wherein the processor is further configured to programmatically receive heart rate variability data from the continuously wearable physiological measurement device.

16. The device of claim 15, wherein the processor is further configured to calculate a recovery score for the user based on the heart rate variability data.