Method and apparatus for generating assessments using physical activity and biometric parameters

• US 9,788,794 B2
• Filed: 02/27/2015
• Issued: 10/17/2017
• Est. Priority Date: 02/28/2014
• Status: Active Grant

• Current Assignee: Valencell, Inc.
• Original Assignee: Valencell, Inc.
• Inventors: LeBoeuf, Steven F., Aumer, Michael Edward, Romesburg, Eric Douglas
• Primary Examiner(s): Thompson, Jewel V
• Application Number:
  US15/120,766
• Publication Number:
  US 20160361020A1
• Time in Patent Office: 963 Days
• Field of Search: 73718, 73726
• CPC Class Codes: A61B 2505/09 Rehabilitation or training
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/02108 from analysis of pulse wave characteristics
• 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 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/026 Measuring blood flow A61B3/1233, A61B3/1241 take precedence
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/091 Measuring volume of inspired or expired gases, e.g. to determine lung capacity
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/112 Gait analysis
• 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 5/352 Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
• CPC Class Codes: A61B 5/4205 Evaluating swallowing
• CPC Class Codes: A61B 5/6815 Ear
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/721 using a separate sensor to detect motion or using motion information derived from signals other than the physiological signal to be measured
• CPC Class Codes: A61B 5/7235 Details of waveform analysis A61B5/349 takes precedence
• CPC Class Codes: A61B 5/7239 : using differentiation including higher order derivatives
• CPC Class Codes: A61B 5/7242 : using integration
• 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: G16H 20/17 : delivered via infusion or injection
• 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
• CPC Class Codes: G16H 40/67 : for remote operation

Abstract

The methods and apparatuses presented herein determine and/or improve the quality of one or more physiological assessment parameters, e.g., response-recovery rate, based on biometric signal(s) and/or motion signal(s) respectively output by one or more biometric and/or motion sensors. The disclosed methods and apparatuses also estimate a user'"'"'s stride length based on a motion signal and a determined type of user motion, e.g., walking or running. The speed of the user may then be estimated based on the estimated stride length.

Claims

1. An assessment generation system configured to assess a fitness level of a subject, the system comprising one or more processing circuits configured to:

measure an average heart rate of the subject with a heart rate sensor worn by the subject over a period of time;

measure an average cadence of the subject with a cadence sensor worn by the subject over the period of time; and

calculate a ratio of the average heart rate to the average cadence with a processor operatively coupled to the heart rate sensor and the cadence sensor;

wherein the one or more processing circuits are further configured to determine a relative body temperature using the calculated ratio of the average heart rate to the average cadence, wherein the higher the calculated ratio the higher the relative body temperature.

2. The assessment generation system of claim 1 wherein the fitness level is inversely proportional to the ratio of the average heart rate to the average cadence.

3. An assessment generation system configured to evaluate an exercise activity comprising one or more sets of exercise repetitions performed by a user via at least one biometric sensor and at least one motion sensor comprised in a wearable monitoring device, the assessment generation system comprising:

one or more processing circuits configured to;

process a motion signal output by the at least one motion sensor and a biometric signal output by the at least one biometric sensor to identify a type of the exercise activity;

process the motion signal using the identified exercise activity to determine an exercise cadence;

process the biometric signal using the identified exercise activity to determine at least one biometric parameter;

wherein the one or more processing circuits are further configured to estimate at least one of;

a number of repetitions of the identified exercise activity using an integral of the exercise cadence; and

a number of sets of the identified exercise activity using a time-dependent change of the at least one biometric parameter.

4. The assessment generation system of claim 3 wherein the one or more processing circuits are further configured to estimate an intensity of the identified exercise activity using the integral of the exercise cadence and an integral of the at least one biometric parameter.

5. An assessment generation system configured to determine a blood pressure characteristic of a user using a device comprising a photoplethysmography sensor and a motion sensor, wherein the photoplethysmography sensor is proximal to at least some skin of the user, the assessment generation system comprising one or more processing circuits configured to:

screen data from the motion sensor to determine an integrity of data from the photoplethysmography sensor;

buffer a plurality of pulses, at least some of the buffered pulses having integrity;

generate an average pulse shape using the buffered plurality of pulses; and

determine the blood pressure characteristic using a waveform representation of the average pulse shape.

6. The assessment generation system of claim 5 wherein the one or more processing circuits screen the data from the motion sensor by determining whether a user cadence is below a threshold to determine the integrity of the data from the photoplethysmography sensor.

7. The assessment generation system of claim 5 wherein the one or more processing circuits generate the average pulse shape by averaging a spline representation of each of the buffered plurality of pulses to generate an average spline representation.

8. The assessment generation system of claim 5 wherein the one or more processing circuits are further configured to generate the waveform representation by computing at least one of an integral of the average pulse shape and a derivative of the average pulse shape.

9. An assessment generation system configured to determine a blood pressure characteristic of a user using a device comprising a photoplethysmography sensor and a motion sensor, wherein the photoplethysmography sensor is proximal to at least some skin of the user, the system comprising a processing circuit configured to:

screen data from the motion sensor to determine an integrity of data from the photoplethysmography sensor;

process the data from the photoplethysmography sensor, at least some processed data having integrity, to generate an average pulse shape;

compute at least one of an integral of the average pulse shape and a derivative of the average pulse shape; and

determine the blood pressure characteristic using the average pulse shape and at least one of the integral and the derivative.