# US 11,298,036 B2 · Wearable device including PPG and inertial sensors for assessing physical activity and biometric parameters

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- Number: US 11,298,036 B2
- Document ID: US11298036B2
- Status: Granted
- Kind: B2
- Application number: 16/809,106
- Filed: 2020-03-04
- Published: 2020-06-25
- Granted: 2022-04-12
- Earliest claimed: 2014-02-28
- Assignees: Valencell, Inc.
- Applicants: Valencell, Inc.
- Inventors: Steven F. LeBoeuf; Michael Edward Aumer; Eric Douglas Romesburg
- Web page: https://patentgrep.com/patent/US11298036B2

## CPC

- A61B 5/02416: {using photoplethysmograph signals, e.g. generated by infrared radiation (A61B5/14552 takes precedence)}
- A61B 5/02055: {Simultaneously evaluating both cardiovascular condition and temperature}
- A61B 5/02108: {from analysis of pulse wave characteristics}
- A61B 5/02438: {with portable devices, e.g. worn by the patient}
- A61B 5/026: Measuring blood flow {(A61B3/1233, A61B3/1241 take precedence)}
- A61B 5/1118: {Determining activity level}
- A61B 5/112: {Gait analysis}
- A61B 5/1123: {Discriminating type of movement, e.g. walking or running (A61B5/1116, A61B5/112 take precedence)}
- A61B 5/4205: {Evaluating swallowing}
- A61B 5/6815: {Ear}
- 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}
- A61B 5/7235: {Details of waveform analysis (detecting specific parameters of the electrocardiograph cycle A61B5/349)}
- A61B 5/7239: {using differentiation including higher order derivatives}
- A61B 5/7242: {using integration}
- 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}
- A61B 5/7278: {Artificial waveform generation or derivation, e.g. synthesizing signals from measured signals}
- G16H 20/17: delivered via infusion or injection
- G16H 20/30: relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
- G16H 20/40: relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
- G16H 40/63: for local operation
- G16H 40/67: for remote operation
- A61B 5/021: Measuring pressure in heart or blood vessels
- A61B 5/02405: {Determining heart rate variability}
- A61B 5/0816: {Measuring devices for examining respiratory frequency (measuring frequency of electric signals G01R23/00)}
- A61B 5/091: Measuring volume of inspired or expired gases, e.g. to determine lung capacity
- A61B 5/352: Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
- A61B 5/7203: {for noise prevention, reduction or removal}
- A61B 2505/09: Rehabilitation or training
- A61B 2562/0219: Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

A wearable device collects a plurality of photoplethysmography (PPG) waveforms from a PPG sensor in the wearable device and collects inertial data associated with subject motion from an inertial sensor in the wearable device. The wearable device processes the inertial data in an assessment processor of the wearable device to determine a data integrity of the plurality of PPG waveforms and, responsive to the determined data integrity, processes the plurality of PPG waveforms in the assessment processor using a neural network comprising thousands of coefficients to generate an assessment of the subject blood pressure.

## Claims

### Claim 1

A wearable device configured to assess subject blood pressure, the wearable device comprising:
a photoplethysmography (PPG) sensor;
an inertial sensor configured to sense subject motion;
an assessment processor operatively connected to the PPG sensor and the inertial sensor, the assessment processor being configured to:
process inertial signals output by the inertial sensor to determine a data integrity of a plurality of PPG waveforms output by the PPG sensor; and
responsive to the determined data integrity, conditionally process the plurality of PPG waveforms using a neural network comprising thousands of coefficients to generate an assessment of the subject blood pressure, the condition comprising the determined data integrity assuring the integrity of the plurality of PPG waveforms.

### Claim 2 (depends on 1)

The wearable device of claim 1, wherein the wearable device comprises a device worn at the ear.

### Claim 3 (depends on 1)

The wearable device of claim 1, wherein the wearable device comprises an audio earbud.

### Claim 4 (depends on 3)

The wearable device of claim 3, wherein the PPG sensor is configured to detect blood flow changes between an anti-tragus and a concha of an ear of the subject.

### Claim 5 (depends on 1)

The wearable device of claim 1, wherein the assessment processor is further configured to change a polling of the PPG sensor responsive to the assessment processor detecting a steady subject cadence from the inertial signals output by the inertial sensor and/or a steady subject heart rate from the PPG waveforms output by the PPG sensor.

### Claim 6 (depends on 1)

The wearable device of claim 1, wherein the assessment processor is further configured to recalibrate the neural network for a specific subject.

### Claim 7 (depends on 1)

The wearable device of claim 1, wherein the assessment processor is further configured to change a sampling rate of the PPG sensor responsive to the assessment processor detecting a steady subject cadence from the inertial signals output by the inertial sensor and/or detecting a steady subject heart rate from the PPG waveforms output by the PPG sensor.

### Claim 8 (depends on 1)

The wearable device of claim 1, wherein the wearable device comprises a device worn at the wrist, arm, leg, digits, nose, head, neck, or torso.

### Claim 9

A method of assessing subject blood pressure via wearable device, the method comprising:
collecting a plurality of photoplethysmography (PPG) waveforms from a PPG sensor in the wearable device;
collecting inertial data associated with subject motion from an inertial sensor in the wearable device;
process the inertial data in an assessment processor operatively connected to the PPG sensor and the inertial sensor to determine a data integrity of the plurality of PPG waveforms; and
responsive to the determined data integrity, conditionally processing the plurality of PPG waveforms in the assessment processor using a neural network comprising thousands of coefficients to generate an assessment of the subject blood pressure, the condition comprising the determined data integrity assuring the integrity of the plurality of PPG waveforms.

### Claim 10 (depends on 9)

The method of claim 9, wherein the wearable device comprises a device worn at the ear of the subject, and wherein the collecting the plurality of PPG waveforms comprises collecting the plurality of PPG waveforms from the ear of the subject.

### Claim 11 (depends on 9)

The method of claim 9, wherein the collecting the plurality of PPG waveforms comprises collecting the plurality of PPG waveforms by detecting blood flow changes between an anti-tragus and a concha of an ear of the subject.

### Claim 12 (depends on 9)

The method of claim 9, further comprising the assessment processor changing a polling of the PPG sensor responsive to a detection, by the assessment processor, of a steady subject cadence from the inertial data and/or of a steady subject heart rate from the plurality of PPG waveforms.

### Claim 13 (depends on 9)

The method of claim 9, further comprising the assessment processor recalibrating the neural network for a specific subject.

### Claim 14 (depends on 9)

The method of claim 9, further comprising the assessment processor:
buffering the plurality of PPG waveforms; and
generating a plurality of representations of the plurality of PPG waveforms;
wherein the processing of the plurality of PPG waveforms comprises processing, by the assessment processor, the plurality of representations of the plurality of PPG waveforms using the neural network comprising thousands of coefficients to generate the assessment of the subject blood pressure.

### Claim 15 (depends on 14)

The method of claim 14, wherein the generating the plurality of representations comprises computing a derivative of at least one of the splines of the plurality of PPG waveforms to generate at least one of the plurality of representations.

### Claim 16 (depends on 14)

The method of claim 14, wherein the generating the plurality of representations comprises computing an integral of at least one of the splines of the plurality of PPG waveforms to generate at least one of the plurality of representations.

### Claim 17 (depends on 14)

The method of claim 14, wherein at least one of the plurality of representations comprises a spectral representation of at least one of the plurality of PPG waveforms.

### Claim 18 (depends on 14)

The method of claim 14, further comprising the assessment processor creating a spline for each of the plurality of PPG waveforms, wherein the generating the plurality of representations of the plurality of PPG waveforms comprises generating the plurality of representations of the plurality of PPG waveforms from the splines.

### Claim 19 (depends on 9)

The method of claim 9, further comprising determining a spectral representation of at least one of the PPG waveforms, wherein the processing of the plurality of PPG waveforms comprises, responsive to the determined data integrity, processing in the assessment processor the spectral representation using the neural network to generate the assessment of the subject blood pressure.

### Claim 20 (depends on 9)

The method of claim 9, further comprising the assessment processor changing a sampling rate of the PPG sensor responsive to the assessment processor detecting a steady subject cadence from the inertial signals output by the inertial sensor and/or detecting a steady subject heart rate from the PPG waveforms output by the PPG sensor.

### Claim 21 (depends on 9)

The method of claim 9, wherein the wearable device comprises a device worn at the wrist, arm, leg, digits, nose, head, neck, or torso, and wherein the collecting of the plurality of PPG waveforms comprises collecting the plurality of PPG waveforms from the wrist, arm, leg, digits, nose, head, neck, or torso of the subject.

### Claim 22

A wearable device configured to assess subject blood pressure, the wearable device comprising:
a photoplethysmography (PPG) sensor;
an inertial sensor configured to sense subject motion;
an assessment processor operatively connected to the PPG sensor and the inertial sensor, the assessment processor being configured to:
process inertial signals output by the inertial sensor to determine a data integrity of a plurality of PPG waveforms output by the PPG sensor;
responsive to the determined data integrity, process the plurality of PPG waveforms using a neural network comprising thousands of coefficients to generate an assessment of the subject blood pressure; and
recalibrate the neural network for a specific subject.

### Claim 23 (depends on 22)

The wearable device of claim 22, wherein the wearable device comprises a device worn at the ear.

### Claim 24 (depends on 22)

The wearable device of claim 22, wherein the wearable device comprises an audio earbud.

### Claim 25 (depends on 24)

The wearable device of claim 24, wherein the PPG sensor is configured to detect blood flow changes between an anti-tragus and a concha of an ear of the subject.

### Claim 26 (depends on 22)

The wearable device of claim 22, wherein the assessment processor is further configured to change a polling of the PPG sensor responsive to the assessment processor detecting a steady subject cadence from the inertial signals output by the inertial sensor and/or a steady subject heart rate from the PPG waveforms output by the PPG sensor.

### Claim 27 (depends on 22)

The wearable device of claim 22, wherein the assessment processor is further configured to change a sampling rate of the PPG sensor responsive to the assessment processor detecting a steady subject cadence from the inertial signals output by the inertial sensor and/or detecting a steady subject heart rate from the PPG waveforms output by the PPG sensor.

### Claim 28 (depends on 22)

The wearable device of claim 22, wherein the wearable device comprises a device worn at the wrist, arm, leg, digits, nose, head, neck, or torso.

### Claim 29

A method of assessing subject blood pressure via wearable device, the method comprising:
collecting a plurality of photoplethysmography (PPG) waveforms from a PPG sensor in the wearable device;
collecting inertial data associated with subject motion from an inertial sensor in the wearable device;
process the inertial data in an assessment processor operatively connected to the PPG sensor and the inertial sensor to determine a data integrity of the plurality of PPG waveforms;
responsive to the determined data integrity, processing the plurality of PPG waveforms in the assessment processor using a neural network comprising thousands of coefficients to generate an assessment of the subject blood pressure; and
recalibrating the neural network for a specific subject.

### Claim 30 (depends on 29)

The method of claim 29, wherein the wearable device comprises a device worn at the ear of the subject, and wherein the collecting the plurality of PPG waveforms comprises collecting the plurality of PPG waveforms from the ear of the subject.

### Claim 31 (depends on 29)

The method of claim 29, wherein the collecting the plurality of PPG waveforms comprises collecting the plurality of PPG waveforms by detecting blood flow changes between an anti-tragus and a concha of an ear of the subject.

### Claim 32 (depends on 29)

The method of claim 29, further comprising the assessment processor changing a polling of the PPG sensor responsive to a detection, by the assessment processor, of a steady subject cadence from the inertial data and/or of a steady subject heart rate from the plurality of PPG waveforms.

### Claim 33 (depends on 29)

The method of claim 29, further comprising the assessment processor:
buffering the plurality of PPG waveforms; and
generating a plurality of representations of the plurality of PPG waveforms;
wherein the processing of the plurality of PPG waveforms comprises processing, by the assessment processor, the plurality of representations of the plurality of PPG waveforms using the neural network comprising thousands of coefficients to generate the assessment of the subject blood pressure.

### Claim 34 (depends on 33)

The method of claim 33, wherein the generating the plurality of representations comprises computing a derivative of at least one of the splines of the plurality of PPG waveforms to generate at least one of the plurality of representations.

### Claim 35 (depends on 33)

The method of claim 33, wherein the generating the plurality of representations comprises computing an integral of at least one of the splines of the plurality of PPG waveforms to generate at least one of the plurality of representations.

### Claim 36 (depends on 33)

The method of claim 33, wherein at least one of the plurality of representations comprises a spectral representation of at least one of the plurality of PPG waveforms.

### Claim 37 (depends on 29)

The method of claim 29, further comprising the assessment processor creating a spline for each of the plurality of PPG waveforms, wherein the generating the plurality of representations of the plurality of PPG waveforms comprises generating the plurality of representations of the plurality of PPG waveforms from the splines.

### Claim 38 (depends on 29)

The method of claim 29, further comprising determining a spectral representation of at least one of the PPG waveforms, wherein the processing of the plurality of PPG waveforms comprises, responsive to the determined data integrity, processing in the assessment processor the spectral representation using the neural network to generate the assessment of the subject blood pressure.

### Claim 39 (depends on 29)

The method of claim 29, further comprising the assessment processor changing a sampling rate of the PPG sensor responsive to the assessment processor detecting a steady subject cadence from the inertial signals output by the inertial sensor and/or detecting a steady subject heart rate from the PPG waveforms output by the PPG sensor.

### Claim 40 (depends on 29)

The method of claim 29, wherein the wearable device comprises a device worn at the wrist, arm, leg, digits, nose, head, neck, or torso, and wherein the collecting of the plurality of PPG waveforms comprises collecting the plurality of PPG waveforms from the wrist, arm, leg, digits, nose, head, neck, or torso of the subject.