# US 2021/0330216 A1 · Low power receiver for in vivo channel sensing and ingestible sensor detection with wandering frequency

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- Number: US 2021/0330216 A1
- Document ID: US20210330216A1
- Status: Published application
- Kind: A1
- Application number: 17/252,600
- Filed: 2019-06-14
- Published: 2021-10-28
- Earliest claimed: 2018-06-15
- Assignees: Otsuka Pharmaceutical Co., Ltd.
- Applicants: Otsuka Pharmaceutical Co. Ltd.
- Inventors: Robert AZEVEDO; Neraj P. BOBRA; Aditya DUA; Ronny X. LI; William A. WEEKS
- Web page: https://patentgrep.com/patent/US20210330216A1

## CPC

- A61B 5/1118: {Determining activity level}
- A61B 5/0002: {Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network (endoradiosondes A61B5/07)}
- A61B 5/0022: {Monitoring a patient using a global network, e.g. telephone networks, internet}
- A61B 5/02405: {Determining heart rate variability}
- A61B 5/02438: {with portable devices, e.g. worn by the patient}
- A61B 5/073: {Intestinal transmitters}
- A61B 5/318: Heart-related electrical modalities, e.g. electrocardiography [ECG]
- A61B 5/6801: {specially adapted to be attached to or worn on the body surface}
- A61B 5/6861: {Capsules, e.g. for swallowing or implanting}
- A61B 5/725: {using specific filters therefor, e.g. Kalman or adaptive filters (specific diagnostics methods using using bioelectric or biomagnetic signals A61B5/316)}
- A61B 5/7282: {Event detection, e.g. detecting unique waveforms indicative of a medical condition (cough events A61B5/0823; seizures A61B5/4094; sleep apnoea A61B5/4818)}
- G01C 22/006: {Pedometers}
- G16H 20/30: relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
- G16H 40/67: for remote operation
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- A61B 5/0006: {ECG or EEG signals}
- A61B 5/0245: by using sensing means generating electric signals, {i.e. ECG signals}
- A61B 5/1116: {Determining posture transitions}
- A61B 5/112: {Gait analysis}
- A61B 5/1123: {Discriminating type of movement, e.g. walking or running (A61B5/1116, A61B5/112 take precedence)}
- A61B 5/4806: {Sleep evaluation (A61B5/4821 takes precedence; devices for inducing sleep A61M21/02)}
- A61B 5/7239: {using differentiation including higher order derivatives}
- A61B 5/7246: {using correlation, e.g. template matching or determination of similarity}
- A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems}
- A61B 2560/0209: adapted for power saving
- A61B 2562/0219: Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
- G16H 20/10: relating to drugs or medications, e.g. for ensuring correct administration to patients
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

A system, a wearable device, and a method are provided which can increase the accuracy of physiological metrics while detecting if the patient ingested digital medicine and/or improve performance of the wearable device. The wearable device can comprise machine executable instructions that when executed by the processor, cause the processor to perform various algorithms, such as, for example, at least one of a step count algorithm, a body angle algorithm, a heart rate algorithm, a peak finder algorithm, an adaptive thresholding algorithm, a heart rate variability algorithm, a R-R cleaning Algorithm, a deltaR-R cleaning algorithm, a merge twin interval algorithm, a split tall intervals algorithm, an absorb short intervals algorithm, a bimodal detection algorithm, and a resting algorithm.

## Claims

### Claim at position 1

-193. (canceled)

### Claim 194

A system for determining a step count of a patient, the system comprising:
a wearable device comprising an accelerometer, the wearable device configured to be coupled to a body of the patient and configured to detect discontinuous data utilizing at least the accelerometer, the discontinuous data comprising frames of ingestible sensor data and frames of physiological data of the patient interspersed in time gaps between the frames of the ingestible sensor data; and
a processor of at least one of the wearable device and a remote device communicatively coupled to the wearable device, where the processor is configured to:
extract accelerometer data from the physiological data;
enhance the accelerometer data;
calculate a number of level crossings in the enhanced accelerometer data by:
computing initial level crossings in portions of the accelerometer data that are uninterrupted by the ingestible sensor data;
identifying boundary conditions in the discontinuous data where the physiological data switches to the ingestible sensor data, and the ingestible sensor data switches to the physiological data;
determining a direction of the level crossing of the accelerometer data at each of the boundary conditions; and
incrementing the number of level crossings responsive to a determination a level crossing at the end of a first frame of physiological data is in the same direction as a level crossing at the start of a second frame of physiological data, the first and second frames being sequentially positioned in the physiological data; and
determine the step count in a speed range of 80 to 150 steps per minute using the calculated number of level crossings with a mean error of plus or minus 3 percent as measured over at least 1000 steps.

### Claim 195 (depends on 194)

The system of claim 194, wherein the processor is configured to enhance the accelerometer data responsive to a determination that a total activity of the accelerometer data is less than or equal to a first threshold and greater than or equal to a second threshold.

### Claim 196 (depends on 194)

The system of claim 194, wherein the processor is configured to enhance the accelerometer data comprises the processor configured to process the accelerometer data through at least one of:
a box car filter;
an absolute value filter;
a low pass filter;
a high pass filter; and
a mean adjustment.

### Claim 197 (depends on 194)

The system of claim 194, further comprising the remote device, wherein the remote device is configured to receive the discontinuous data from the wearable device.

### Claim 198 (depends on 194)

The system of claim 194, wherein the ingestible sensor data is generated by an ingestible event marker after contact with a bodily fluid of the patient.

### Claim 199 (depends on 194)

The system of claim 194, wherein the wearable device is configured to be removably attached to skin of the patient.

### Claim 200 (depends on 194)

The system of claim 194, wherein the processor is configured to detect a change in a slope of the frames of physiological data and increment the number of level crossings responsive to the slope change.

### Claim 201

A wearable device comprising:
a disposable component configured to attach to a patient and receive discontinuous data comprising frames of ingestible sensor data and frames of physiological data of the patient interspersed in time gaps between the frames of the ingestible sensor data;
a reusable component coupled to the disposable component and comprising:
a processor;
a non-transitory memory coupled to the processor and configured to store the discontinuous data. wherein the non-transitory memory comprises machine executable instructions that when executed by the processor, cause the processor to:
extract accelerometer data from the physiological data;
enhance the accelerometer data;
calculate a number of level crossings in the enhanced accelerometer data by:
computing initial level crossings in portions of the accelerometer data that are uninterrupted by the ingestible sensor data;
identifying boundary conditions in the discontinuous data where the physiological data switches to the ingestible sensor data, and the ingestible sensor data switches to the physiological data;
determining a direction of the level crossing of the accelerometer data at each of the boundary conditions; and
incrementing the number of level crossings responsive to a determination a level crossing at the end of a first frame of physiological data is in the same direction as a level crossing at the start of a second frame of physiological data, the first and second frames being sequentially positioned in the physiological data; and
determine a step count of the patient in a speed range of 80 to 150 steps per minute using the calculated number of level crossings with a mean error of plus or minus 3 percent as measured over at least 1000 steps.

### Claim 202 (depends on 201)

The wearable device of claim 201, wherein the machine executable instructions that when executed by the processor, further cause the processor to enhance the accelerometer data responsive to a determination a total activity of the accelerometer data is less than or equal to a first threshold and greater than or equal to a second threshold.

### Claim 203 (depends on 201)

The wearable device of claim 201, wherein the machine executable instructions that when executed by the processor, cause the processor to enhance the accelerometer data comprises machine executable instructions that when executed by the processor, further cause the processor to process the accelerometer data through at least one of:
a box car filter;
an absolute value filter;
a low pass filter;
a high pass filter; and
a mean adjustment.

### Claim 204 (depends on 201)

The wearable device of claim 201, wherein the ingestible sensor data is generated by an ingestible event marker after contact with a bodily fluid of the patient.

### Claim 205 (depends on 201)

The wearable device of claim 201, wherein the wearable device is configured to be removably attached to skin of the patient.

### Claim 206 (depends on 201)

The wearable device of claim 201, the machine executable instructions that when executed by the processor, further cause the processor to detect a change in a slope of the frames of physiological data and increment the number of level crossings responsive to the slope change.

### Claim 207

A method of a wearable device for determining a step count of a patient from discontinuous data, the method comprising:
receiving, by the wearable device, discontinuous data derived from the patient, the discontinuous data comprising frames of ingestible sensor data and frames of physiological data of the patient interspersed in time gaps between the frames of the ingestible sensor data;
extracting accelerometer data from the physiological data;
enhancing the accelerometer data;
calculating a number of level crossings in the enhanced accelerometer data by:
computing initial level crossings in portions of the accelerometer data that are uninterrupted by the ingestible sensor data;
identifying boundary conditions in the discontinuous data where the physiological data switches to the ingestible sensor data, and the ingestible sensor data switches to the physiological data;
determining a direction of the level crossing of the accelerometer data at each of the boundary conditions; and
incrementing the number of level crossings responsive to a determination a level crossing at the end of a first frame of physiological data is in the same direction as a level crossing at the start of a second frame of physiological data, the first and second frames being sequentially positioned in the physiological data; and
determining the step count in a speed range of 80 to 150 steps per minute using the calculated number of level crossings with a mean error of plus or minus 3 percent as measured over at least 1000 steps.

### Claim 208 (depends on 207)

The method of claim 207, wherein enhancing the accelerometer data is responsive to determining a total activity of the accelerometer data is less than or equal to a first threshold and greater than or equal to a second threshold.

### Claim 209 (depends on 207)

The method of claim 207, wherein enhancing the accelerometer data comprises processing the accelerometer data through at least one of:
a box car filter;
an absolute value filter;
a low pass filter;
a high pass filter; and
a mean adjustment.

### Claim 210 (depends on 2074)

The method of claim 2074, further comprising generating the ingestible sensor data by contacting an ingestible event marker with a bodily fluid of the patient.

### Claim 211 (depends on 207)

The method of claim 207, wherein the wearable device is configured to be removably attached to skin of the patient.

### Claim 212 (depends on 207)

The method of claim 207, further comprising detecting a change in a slope of the frames of physiological data and increment the number of level crossings responsive to the slope change.