JP7591595B2 - Wearable Diagnostic Devices - Google Patents

Description

(background)
As individuals become increasingly health conscious, there is a growing demand among users to receive personal health information in a simple and convenient manner. In many cases, a user may be required to wear multiple devices, each of which provides information about one aspect of the user's health. There is a need for devices that can provide information about multiple aspects of a user's health in a simple, non-invasive, and convenient manner.

(overview)
In general, the subject innovative aspects describe wearable diagnostic devices and methods and systems for performing one or more medical diagnostic tests.

In some implementations, a system includes one or more computing devices and one or more storage devices storing instructions that, when executed by the one or more computing devices, cause the one or more computing devices to perform operations such as receiving an input corresponding to a request to initiate a non-invasive diagnostic test to detect a medical condition of a user, identifying one or more sensors for performing the non-invasive diagnostic test, and, based on the non-invasive diagnostic test,
The method includes activating the identified one or more sensors, receiving signal data via the one or more sensors, obtaining a predicted value for the non-invasive diagnostic test based in part on a user profile, determining a test result based on the predicted value and the received signal data, and outputting the test result via a display or speaker.

Each implementation may optionally include one or more of the following features: For example, in some implementations, the non-invasive diagnostic tests include one or more of a glucose test, a cholesterol test, a hemoglobin test, an oxygen saturation level test, and an electrocardiogram monitoring test.

In some implementations, the operations further include selecting a path based on raw data obtained from the received signal data and obtaining a set of determined values based on the selected path. Determining a test result based on the predicted values and the received signal data includes determining a test result based on the determined values.

In some implementations, the operations further include obtaining user clinical data and user demographic data from one or more databases, determining a range of a clinical dataset based on the obtained user clinical data and user demographic data, and mapping the range of the clinical dataset to a predictive value of a non-invasive diagnostic test.

In some implementations, the operations further include determining a second non-invasive diagnostic test to be performed based on (i) a user pattern associating the second non-invasive diagnostic test with a non-invasive diagnostic test, and (ii) the user's medical history; activating a second set of one or more sensors based on the second non-invasive diagnostic test; receiving second signal data via the second set of one or more sensors; obtaining a second predicted value of the second non-invasive diagnostic test based in part on the user profile; and determining a second test result based on the second predicted value and the received second signal data.

In some implementations, obtaining the predictive value of the non-invasive diagnostic test includes determining the predictive value of the non-invasive diagnostic test using the second test results.

In some implementations, the second non-invasive diagnostic test is a cholesterol test that is run simultaneously with the non-invasive diagnostic test that is a glucose test.

In some implementations, the non-invasive diagnostic test and the second non-invasive diagnostic test are performed by a watch that includes one or more computing devices.

In some implementations, the one or more sensors include one or more of a wireless cardiac electrode, a piezoelectric vibration sensor, an infrared sensor, a temperature sensor, an accelerometer, and a microelectromechanical system (MEMS) sensor.

According to an aspect of the disclosed subject matter, a watch includes one or more computing devices and one or more storage devices storing instructions that, when executed by the one or more computing devices, cause the one or more computing devices to perform operations including selecting a glucose test and a cholesterol test based on a user profile, identifying one or more sensors for performing the glucose test and the cholesterol test, activating the identified one or more sensors, receiving signal data via the one or more sensors, and activating a first step of the glucose test based in part on the user profile.
determining a glucose test result and a cholesterol test result based on the first predicted value and the received signal data, and outputting the glucose test result and the cholesterol test result via a display or speaker of the watch.

In some implementations, the one or more sensors include an infrared sensor and a piezoelectric vibration sensor. The operations include selecting a path based on raw data obtained from the received signal data and obtaining a set of determined values based on the selected path. Determining a glucose test result based on the first predicted value and the received signal data includes determining the glucose test result based on the determined values.

The above aspects and implementations described further herein provide several advantages. For example, a single wearable diagnostic device can perform multiple non-invasive diagnostic tests, including a non-invasive glucose test, a non-invasive cholesterol test, and a non-invasive hemoglobin test. The wearable diagnostic device can also obtain electrocardiogram (EKG) data, or detection of symptoms of Parkinson's disease, such as involuntary movements of the user's hands. The wearable diagnostic device with wireless electrodes can record the user's history and medical condition, and can utilize predictive values, algorithms, and mapping databases to provide accurate and reliable results of glucose, cholesterol, and/or hemoglobin tests. The predictive values include clinical and demographic information of the user, so that the calculations can more accurately take into account parameters such as skin color and age that may affect glucose or hemoglobin levels. The predictive values can also be used to correlate the predictive values with the user's susceptibility to certain diseases.

Other aspects include corresponding methods, systems, apparatus, computer readable storage media, and computer programs configured to perform operations of the above methods.

The details of one or more aspects described herein are set forth in the accompanying drawings and the description below. Other features, aspects, and advantages of the subject matter will become apparent from the following description, drawings, and claims.

BRIEF DESCRIPTION OF THE DRAWINGS
1-5 show an exemplary implementation of a wearable diagnostic device.

FIG. 6 illustrates an exemplary system implemented in a wearable diagnostic device.

FIG. 7 shows a flowchart of an exemplary method for determining a user's glucose level.

FIG. 8 shows a flow chart of an exemplary method for determining a glucose predictive value.

FIG. 9 shows a flowchart of an exemplary method for determining a user's hemoglobin level.

FIG. 10 shows a flow chart of an exemplary method for determining a hemoglobin predictor value.

FIG. 11 shows a flow chart of an exemplary method for determining a user's blood pressure level.

FIG. 12 shows a flow chart of an exemplary method for determining a blood pressure prediction value.

FIG. 13 shows a flow chart of an exemplary method for determining a user's cholesterol level.

Like reference numbers and names in the various drawings indicate like elements.

Detailed Description
The present disclosure generally relates to a wearable diagnostic device that can be worn on a user's body to provide various types of health-related information to the user. In some implementations, the wearable diagnostic device can perform one or more medical diagnostic tests and provide real-time, non-invasive, accurate, continuous data regarding the user's heart rate, hemoglobin level, body temperature, oxygen level, glucose level, cholesterol, and blood pressure. In some implementations, the wearable diagnostic device can also provide electrocardiogram (EKG) data and detection of symptoms of Parkinson's disease, such as involuntary movements of the user's hands. A user can configure the wearable diagnostic device to provide information regarding one or more of the above diagnostic tests. Implementations of the wearable diagnostic device are described below with reference to the figures.

Figures 1-5 show different views of a wearable diagnostic device. In some implementations, the wearable diagnostic device may be implemented in the form of a watch as shown in Figures 1-5. Figure 6 shows further details of components included in a wearable diagnostic device according to some implementations. In general, the wearable diagnostic device may be implemented in a variety of suitable shapes, forms, and sizes, and may be any electronic device that can be attached to the left arm of a user and obtain multiple health diagnostic measurements of the user.

1 to 5, the wearable watch includes a casing 101, a display 102, removable wireless cardiac electrodes 103, 113, belts 104, 109, a piezoelectric vibration sensor 105, an infrared (IR)/
It may include a light/laser sensor 106, a connector 107, a temperature sensor 108, control buttons 110, 111, a back cover 112, a belt connector 114, a belt fastener 115, a charging connector 116, a power source 117, an outer belt layer 118, 120, insulated wires 119, an accelerometer 121, a power manager 122, a system on chip (SoC) 123, and a power switch 124. The wearable watch may be worn on the left arm 150 of a user.

The casing 101 may include or be coupled to a display 102, a charging connector 116, control buttons 110, 111, and one or more electronic components, such as a processor, a printed circuit board (PCB), an integrated circuit (IC), a SoC 123, memory, and a wireless transceiver. The display 102 may be, for example, a liquid crystal display (LCD) for displaying various data.
The display may be implemented using any suitable display, including a CCD, a light emitting diode (LED) display, or an organic LED display.
102 may be a touch screen, such as a capacitive touch screen.

The display 102 can display a user interface configured to output data to and receive input from a user. The control buttons 110, 111 can be used by a user to navigate the user interface, make selections, and perform one or more operations on the wearable diagnostic device. In some implementations, the display 102 can receive a selection from a user and provide information indicative of the user's selection to one or more processors of the wearable watch or network device. In some implementations, the display 102 can receive data from one or more processors and provide the data to the display 102 for output to the user. For example, in some cases, a user can input a request to provide the user's glucose level via a user interface. After determining the user's glucose level, information indicative of the user's glucose level can be output via a user interface displayed on the display 102.

The one or more processors in the casing 101 may be embodied on a PCB or an IC and may be electrically connected to other electronic components of the wearable diagnostic device, such as memory, the display 102, and a wireless transceiver. For example, the processor may receive data indicative of a user's selection from the display 102, determine the type of information requested by the user, and generate commands to perform one or more actions based on the information requested by the user.

In some implementations, the one or more processors can transmit and receive data using a wireless transceiver. Data can be transmitted and received between the wearable diagnostic device and a secondary device. The secondary device can be a device configured to communicate with a device selected by a user, a network server, or a wearable watch. For example, a user can select another device that he or she owns to transmit data to from the wearable diagnostic device. In another example, the wearable diagnostic device can be configured to transmit and receive data to and from one or more network servers according to a user request or a predefined schedule for transmitting and receiving data.

The one or more network servers may provide services to one or more networks, which may include one or more databases, access points, base stations, storage systems, cloud systems, and modules. The one or more servers may be a set of servers running a network operating system. The one or more servers may be used for and/or provide cloud and/or network computing.

The databases in the network may include cloud databases or databases managed by a database management system (DBMS). A DBMS may be implemented as an engine that controls the organization, storage, management, and retrieval of data in a database.
A MS often provides the ability to query, back up, replicate, apply rules, provide security, perform calculations, change and access logs, and automate optimization. A DBMS typically includes a modeling language, data structures, a database query language, and a transaction mechanism. The modeling language is used to define the schema of each database in the DBMS according to a database model, which may include a hierarchical model, a network model, a relational model, an object model, or other applicable known or convenient mechanisms. The data structures may include fields, records, files, objects, and other applicable known or convenient structures for storing data. A DBMS may also include metadata about the data stored.

In some implementations, the database may include a user database that can store information about the user, such as alias names, medical history, and any medical conditions of the user. The user database may also store licenses to access various tools and software,
Data related to authorizations and credentials may also be stored. In some cases, the user may select the option to anonymize the user data such that any information that could identify the user is anonymized and user identifying information is removed prior to storing the data in the user database.

In general, data can be communicated to and from the wearable diagnostic device using a variety of suitable wireless protocols. For example, the wearable diagnostic device can communicate with one or more networks, devices, or servers using WiFi or Bluetooth communications. In general, different types of networks can be communicated and different communication protocols can be used.

The charging connector 116 may be a port configured to connect to a power cable, such as a universal serial bus or a wire. When connected to a power cable, the charging connector 116
serves as a power interface to provide power from an external source to charge the wearable watch's power supply 117. The power supply 117 can be any suitable battery and can power any electrical components within the wearable diagnostic device.

The casing 101 also includes a power switch 124. In some implementations, the power switch 124
In response to a selection of the power switch 124 to a power off position, one or more processors can send a command to the power manager 122 to stop providing power to one or more components of the wearable diagnostic device, such as the display 102. In some implementations, in response to a selection of the power switch 124 to a power on position, the power manager 122 can send a command to the power source 117 to provide power to one or more components of the wearable diagnostic device, such as the display 102.

The casing 101 is connected to the belts 104, 109, connector 107, and belt connector 114, which can be adjusted to secure the wearable diagnostic device around wrists of different sizes. For example, the belts 104, 109, and belt connector 114 can be wrapped around a user's wrist, and a belt fastener 115 can hold the belts 104, 109, and belt connector 114 in place. The back cover 112 and cardiac electrodes 113 can be disposed at the bottom of the casing 101.

One or more insulated wires 119 may be integrated into the structure of the wearable diagnostic device to provide electrical connections to various components of the wearable diagnostic device. For example, as illustrated in FIG. 6, one or more insulated wires 119 may be disposed along the central axis of the wearable diagnostic device to provide electrical connections between the power source 117 and the casing 101, and between the power source 117 and the IR/light/laser sensor 106.
An electrical connection can be provided between the

The wearable diagnostic device also includes cardiac electrodes 103, 113, a piezoelectric vibration sensor 105, an infrared (IR)/
One or more sensors may also be included, such as a light/laser sensor 106, a temperature sensor 108, an accelerometer 121, and an impedance sensor. In some implementations, a piezoelectric vibration sensor 105
may include a microelectromechanical system (MEMS) sensor.

The wireless cardiac electrodes 103, 113 may include a metallic conductive material configured to detect cardiac potential waveforms, such as voltages generated during contractions of the heart. The wireless cardiac electrodes 103 are disposed on an outer belt layer 118 that corresponds to the outer periphery of the wearable diagnostic device. The wireless cardiac electrodes 113 are disposed on or integrated with the back cover 112 such that they can contact a user's skin when the wearable diagnostic device is secured around the user's arm 150. The wireless cardiac electrodes 103, 113 can be detached from the wearable diagnostic device and reattached, and can wirelessly communicate data to another electronic device.

The IR/light/laser sensor 106 may include a signal generator and a signal detector. The signal generator may generate and transmit an infrared signal having a wavelength in the range of, for example, 650 to 1400 nanometers (nm). This range is particularly useful for obtaining data indicative of the presence of oxygen, glucose, and hemoglobin molecules in the user's blood. The signal detector may be configured to detect the infrared signal received from the user's body.

In some implementations, the IR/light/laser sensor 106 detects the pulse signal and performs an arterial
The detected signals can be processed using Pulse Wave Transit Time (PWTT) in a time-domain method. These pulse signals can be combined with data obtained by the piezoelectric vibration sensor 105 to non-invasively predict blood pressure. Additionally, the IR/light/laser sensor 106 can be used to non-invasively determine data indicative of the oxygen saturation level ( _SpO2 ) in the user's blood.
or can be obtained.

The piezoelectric vibration sensor 105 and the accelerometer 121 can be used to measure the vibration of the user's hand. For example, the accelerometer 121 can detect changes in direction, speed, vibration, and rotation. The temperature sensor 108 can be used to measure the user's body temperature. The temperature sensor 108
can be implemented in a variety of suitable ways. For example, the temperature sensor 108 can be a thermocouple, a silicon band gap sensor, a thermometer, or a thermistor including one or more sense resistors. A change in electrical resistance in the sense resistor can correspond to a change in body temperature. In some implementations, the temperature sensor can include active and passive elements as well as resistors and semiconductor materials fabricated in a single package of an IC.

As described in more detail below, the sensors can be used to obtain one or more measurements. Operations performed using the wearable diagnostic device to determine glucose levels, hemoglobin levels, blood pressure levels, EKG, heart rate levels, body temperature, and hand vibrations are described in more detail below. The operations begin after a user places the wearable diagnostic device on the user's left arm 150 and adjusts one or more of the belts 104, 109, belt connector 114, belt fastener 115, and outer belt layers 118, 120 to secure the wearable diagnostic device around the user's left arm 150.

When the wearable diagnostic device is secured to the user's left arm 150, the IR/light/laser sensor 106
may contact the bottom of the user's left wrist just above the user's radial-ulnar artery. As described above, the IR/light/laser sensor 106 may include a signal generator for generating and transmitting an infrared signal. The signal may be transmitted from the IR/light/laser sensor 106 toward the user's skin at the bottom of the user's left wrist.

The IR/light/laser sensor 106 can detect the reflection of a signal from the skin of the user. The detected signal, including the absorption spectrum data, is then sent to an analog-to-digital converter (
The signal received from the user's skin can be converted into a digital signal using an ADC (analog-to-digital converter). The result of the conversion is raw digital data corresponding to the signal received from the user's skin. The raw digital data can be processed to determine an absorption spectrum corresponding to the presence of various molecules in the user's blood, such as oxygen, glucose, hemoglobin, etc. By determining the absorption spectrum, the presence or absence and corresponding levels of certain molecules present in the user's blood can be inferred.

In some implementations, multiple signal measurements may be obtained such that multiple sets of raw digital data are obtained at different times. The various sets of raw digital data may be accumulated and averaged to obtain a single set of raw data for the user.

In some implementations, the wearable diagnostic device may obtain information related to the health of the user, such as, but not limited to, the user's location, the user's physician, the user's pharmacist, the user's medical record holder, the user's demographic associations with one or more groups, the user's diet, an indication of the number of children the user has, the user's medical history, one or more past or present medical conditions, such as the user's allergies,
These may include surgeries, genetic conditions, one or more health concerns of the user, and one or more blood content levels for which the user is interested in obtaining details. For example, the user may indicate that they are particularly interested in their glucose levels or blood pressure levels. The user may also indicate how frequently they would like to obtain information about their glucose levels or blood pressure levels.

The information related to the user's health can be used to create a user profile. The user profile can be stored locally on the wearable diagnostic device or in a database or server located remotely from the wearable diagnostic device. In some implementations, the user profile data can be processed in one or more ways before being stored or used so that personally identifiable information is removed. For example, the user's personal information can be processed to generalize the user's geographic location, personal information, or demographic background to the extent desired by the user so that the user's personally identifiable information cannot be identified or specific details of the user cannot be identified. Thus, the user can control what information is collected and how that information is used. To accomplish this, the user can be provided with controls via the wearable diagnostic device that allow the user to choose whether or when the systems, programs, or functions described herein can collect or provide user information.

In some implementations, a user may choose to subscribe or join a service that allows the wearable diagnostic device to receive and update the user's medical information in real time. In particular, the wearable diagnostic device may update test results, consultation results, diagnoses, or prescriptions. For example, a user may allow the user's doctor, pharmacist, or medical record owner to provide the user's medical information to a server or database that stores a user profile. This server or database may be managed by a subscription service that may collect information from various sources to update the user profile. The wearable diagnostic device may receive updates regarding the user's medical information in real time, periodically, or upon the user's request.

In some implementations, a user can directly input information about the user's health and background using a user interface of the wearable diagnostic device, and the inputted user's medical information can then be stored remotely or on the wearable diagnostic device.

Although the features described in connection with FIGS. 1-6 relate to a wearable diagnostic device worn on the left arm of a user, the wearable diagnostic device may have a variety of other forms.
It should be understood that in some cases the wearable diagnostic device may be worn or applied to other parts of the user's body. Additionally, one or more additional components may be included in or coupled to the wearable diagnostic device. For example, in some implementations, the wearable diagnostic device may include a speaker that outputs information using sound waves, and one or more microphones for receiving voice input, e.g., corresponding to a user's commands, requests, or feedback.

In some implementations, the wearable diagnostic device may be further configured to perform one or more diagnostic tests to obtain one or more of a glucose level, a hemoglobin level, or a blood pressure level for a user wearing the wearable diagnostic device based on received or acquired information related to the user's health. These processes are further described in Figures 7-10.

7, 9, and 13, the wearable diagnostic device may receive an indication that a test needs to be performed (702, 902, 1302). For example, the wearable diagnostic device may receive an indication that a glucose test (702), a hemoglobin test (902), or a cholesterol test (1302) needs to be performed.
An indication that a test should be performed may include one or more of the following: a selection made by a user to perform a test;
and requests made by one or more processors according to scheduled times. For example,
The wearable diagnostic device can be programmed to provide a blood pressure measurement at a particular time or after a certain period of time. The wearable diagnostic device can then schedule a date and time when a particular measurement, such as a blood pressure measurement, should be provided to the user and can initiate a method of measuring blood pressure and oxygen saturation levels at the scheduled time.

After receiving an indication that a test needs to be performed (702, 902, 1302), one or more processors of the wearable diagnostic device can determine the type of sensor to be utilized for the test and activate the determined type of sensor, which in the case of a glucose test or a hemoglobin test may include one or more IR/light/laser sensors (704, 904). In the case of a cholesterol test, the activated sensors may include one or more of an IR/light/laser sensor, an impedance sensor, and an electromagnetic sensor. Activating a sensor may include operations including, but not limited to, increasing power to the sensor, warming up the sensor to emit or receive a signal, such as an IR signal, or configuring or calibrating the sensor.

The activated sensor or sensors can obtain measurements that characterize the user (see 7
06, 906, 1306). For example, one or more IR/light/laser sensors may each be configured to transmit an infrared or pulsed signal for a short period of time, e.g., 30 seconds, and detect reflection of the transmitted signal from the user's skin. In some cases, the reflection measurements and current measurements using the impedance sensor may be taken repeatedly or under different circumstances, such as different pulsed powers or magnetic fields. For example, for cholesterol measurements, an initial current or infrared signal measurement may be taken without applying a magnetic field, and one or more other current or infrared measurements may be taken after magnetic fields of various low power intensities are generated for a period of time, e.g., 10 seconds.

For glucose and hemoglobin tests, the measured detection signals are processed and a particular path is selected based on the raw data values (708, 908). In particular, each of the detected signals includes absorption spectrum data that can be converted to raw digital data using an analog-to-digital converter (ADC). Processing may optionally include additional processing operations such as down-conversion, filtering, convolution, blending, and other suitable signal processing operations.

The raw digital data corresponding to the signal received from the user's skin is used as a basis for selecting a particular pathway and corresponding predicted and slope regression values (710, 910). For example, if a glucose test is being performed, an exemplary mapping such as that shown in Table I may be used to select a particular pathway and corresponding predicted and slope regression values. If the raw value is between 500 and 700, pathway A and a corresponding glucose (G) predicted value of 917 and a G slope regression value of 0.838 are selected. If the raw value is between 701 and 850, pathway B and a corresponding G predicted value of 919 and a G slope regression value of 0.838 are selected. If the raw value is between 851 and 850, pathway C and a corresponding G predicted value of 919 and a G slope regression value of 0.838 are selected.
If the raw value is greater than or equal to 950, route C and the corresponding G-predicted value of 1001 and G-slope regression value of 0.838 are selected. If the raw value is greater than or equal to 951 and less than or equal to 1100, route D and the corresponding G-predicted value of 1004 and G-slope regression value of 0.838 are selected.
If the raw value is greater than or equal to 1101, then route E is selected with a corresponding G predicted value of 981 and G slope regression value of 0.838.

Table I

The resulting predicted and gradient regression values can then be used to determine the user's glucose level 712. To determine the glucose level, the wearable diagnostic device can apply generalizability theory and G-gradient regression to the set of raw data to determine a value indicative of the glucose level using Equation 1, as set forth below.
Glucose value = predicted value ( _{G-clinical sampling regression} ) - (G regression slope) x (raw data) [
Formula 1]

The determined glucose value can then be output by a variety of suitable methods (e.g.,
714). For example, the determined glucose value may be output on a display of the wearable diagnostic device or may be output by a speaker of the wearable diagnostic device.

When a hemoglobin test is being performed, an exemplary mapping as shown in Table II can be used to select a particular pathway and corresponding predicted and slope regression values. If the raw value is greater than or equal to 500 and less than or equal to 700, pathway A and a corresponding hemoglobin (Hb) predicted value of 31.5 and Hb slope regression value of 0.114 are selected. If the raw value is greater than or equal to 701 and less than or equal to 850, pathway B and a corresponding Hb predicted value of 67 and Hb slope regression value of 0.114 are selected. If the raw value is greater than or equal to 851 and less than or equal to 950, pathway C and a corresponding Hb predicted value of 81 and Hb slope regression value of 0.114 are selected.
If the raw value is ≥ 951 and ≤ 1100, then pathway D is selected with a corresponding Hb predicted value of 98 and a Hb slope regression value of 0.114. If the raw value is ≥ 1101, then pathway E is selected with a corresponding Hb predicted value of 100.5 and a Hb slope regression value of 0.114.
The Hb slope regression value is selected.

Table II

The resulting predicted and slope regression values can then be used to determine the user's hemoglobin level 912. To determine the hemoglobin level, the wearable diagnostic device can apply Hb slope regression to the set of raw data to determine a value indicative of the hemoglobin level using Equation 2, as described below.
Hb value = ((Hb regression slope) x (raw data)) - predicted value ( _{Hb-clinical sampling regression} ) [Equation 2]

The determined hemoglobin value may then be output by various suitable methods (914). For example, the determined hemoglobin value may be output on a display of the wearable diagnostic device or may be output by a speaker of the wearable diagnostic device.

For a raw data value below 500 for a glucose or hemoglobin test, the wearable diagnostic device may determine that the value is erroneous and initiate another attempt to obtain a raw data value via one or more IR/light/laser sensors. If a value above 500 cannot be obtained after three attempts, the wearable diagnostic device may output an error message to the user indicating that a glucose or hemoglobin test cannot be performed at this time.

When a cholesterol test is being performed to determine cholesterol levels (e.g., HDL, LDL, triglyceride molecules) for a user wearing a wearable diagnostic device, the lower left wrist of the user is exposed to infrared signals emitted from an IR/light/laser sensor. This area of the user's skin can also be exposed to a magnetic field for a short period of time, e.g., 10 seconds or 30 seconds, after which the blood particles, excluding the cholesterol molecules, will orient themselves according to the positive and negative electromagnetic poles. Data indicative of infrared absorption spectra can be obtained (1306) before and after application of the magnetic field, and the obtained data can be subsequently processed (1308).
Processing of the resulting data may include converting the data into a digital signal using an analog-to-digital converter (ADC) and calculating the difference between the absorption spectrum values before and after application of the magnetic field to obtain the raw values.

The predicted cholesterol level values are then obtained from a database (1310). The database may include a mapping of cholesterol levels to raw values and may be organized according to demographic classes. The database may be constructed based on testing of multiple human subjects, as described below.

First, a demographic profile of the subject can be recorded. The demographic profile can include various types of descriptive information about the subject, such as the subject's age, sex, ethnicity, skin color, marital status, and/or medical condition. For example, a subject can have a demographic profile of: 48 years old, white male, widowed, skin cancer. Invasive cholesterol testing can be performed on each subject using various suitable methods, and the test results can be added to the subject's profile.

Current and infrared signal measurements of the subject can then be obtained using the resistive and infrared sensors, respectively, as described above. Current and infrared signal measurements can be taken before application of the magnetic field and after application of the magnetic field for a period of time. Derivatives of the measured signals can be calculated and stored as raw values. The raw values are mapped to cholesterol levels obtained from an invasive cholesterol test and stored in the subject's profile.

With a large sample size of subjects, hundreds and thousands of subjects can be tested so that a database can be constructed that maps raw values to cholesterol levels according to one or more demographic classes (e.g., gender, ethnicity, age, etc.) In some implementations, statistical data can be extracted from the tests so that mean, median, and mode cholesterol levels and raw values can be determined by demographic class.

In general, various kinds of demographic classes can be formed. For example, in some cases, a demographic class can be limited to age, e.g., people in the 40s age group. In some cases, a demographic class can include multiple demographic characteristics, such as age and ethnicity, or age, ethnicity, and gender. Thus, the database can include a mapping table that maps estimated cholesterol levels based on raw values associated with a particular demographic class. It is noted that the database can be constructed anonymously such that personal information of the subjects is not revealed or known.

Returning to Figure 13, a predicted cholesterol level value is obtained (1310) from a database based on the raw value obtained in operation 1308. In particular, the cholesterol level mapped to the raw value obtained in operation 1308 and the demographic profile of the user wearing the wearable diagnostic device is obtained by referencing a mapping table in the database.

The predicted cholesterol level value may then be determined to be an estimated cholesterol level for the user wearing the wearable diagnostic device (1312). In some implementations, the predicted cholesterol level value may be compared to a previously obtained cholesterol level for the user. If the difference between the predicted cholesterol level and the user's last obtained cholesterol level is greater than a threshold, for example 3%, the wearable diagnostic device performs operation 1.
302 through 1310 may be repeated to obtain a new predicted cholesterol level. Such iterations may continue until the predicted cholesterol level value is within a threshold difference of the user's last obtained cholesterol level. In some cases, if three iterations have been performed without meeting the threshold difference, the predicted cholesterol level value obtained in the third iteration may be determined as the estimated cholesterol level of the user wearing the wearable diagnostic device. The determined estimated cholesterol level of the user may then be displayed by the display 102 of the wearable diagnostic device.

In the above exemplary implementations, the predicted values were utilized to determine glucose levels, hemoglobin levels, or cholesterol levels. The predicted values can be determined based on a number of factors using a variety of different methods. Methods for obtaining predicted values for cholesterol levels are described above. Methods for obtaining predicted values for glucose levels and hemoglobin levels are further described in connection with FIG. 8 and FIG. 10.

8, to obtain a predicted glucose level for a user, the wearable diagnostic device can obtain user profile information (802). The user profile information can include information about the user's location, the user's physician, the user's pharmacist, the user's medical record owner, and the user's current medical record.
These may include one or more of the user's demographic associations with one or more groups, the user's diet, an indication of the number of children the user has, the user's medical history, one or more past or current medical conditions, such as the user's allergies, surgeries, genetic status, one or more health concerns of the user, and one or more blood content levels about which the user is interested in obtaining details.

Using information from the user's profile, the wearable diagnostic device can determine a clinical correlation information value (cCIG) for glucose for the user based on the user's blood pressure and body temperature (804). In some implementations, the cCIG can be a matrix of values representing estimated blood pressure and estimated body temperature. In general, various suitable methods can be utilized to obtain the user's blood pressure and body temperature. In some implementations, the blood pressure can be obtained as described herein with reference to FIGS. 11 and 12, and the body temperature can be obtained using a temperature sensor included in the wearable diagnostic device. In some cases, the cCIG can be determined using clinical information obtained from the user profile, such as the user's medical history, which can include data such as physician reports, past or current medical conditions, clinical diagnoses, etc.

The wearable diagnostic device then measures the user’s glucose clinically relevant demographic values (
A cLDG (cLDG) may be determined (806). The cLDG may be determined in part based on one or more characteristics of the user, such as the user's demographic group, the user's age, and other personal characteristics of the user. As an example, if the raw glucose values above are associated with a particular route, such as route A, or if the user profile indicates a particular demographic origin or profile of the user, a cLDG value may be determined such that the cLDG value corresponds to the raw glucose value or the particular demographic origin or profile of the user. In some implementations, the cLDG may be a matrix of values representing various characteristics, such as the user's age, diet, and sex, if the user agrees to provide the following personal information:

After obtaining the cCIG and cLDG, the wearable diagnostic device can determine 810 a range of clinical datasets based on the cCIG and cLDG. The determined range of clinical datasets can be:
The user's glucose level is mapped to a glucose predicted value 812. A database storing various clinical dataset ranges and glucose predicted values can be queried with the determined clinical dataset range and can return a glucose predicted value that maps to the determined clinical dataset range. A glucose predicted value can then be provided and utilized in determining the user's glucose level 814.

10, to obtain a predicted value of a user's hemoglobin level, the wearable diagnostic device can obtain user profile information (1002), including the user's location, the user's physician, the user's pharmacist, the user's medical record owner,
This may include one or more of the user's demographic associations with one or more groups, the user's diet, an indication of the user's number of children, the user's medical history, one or more past or current medical conditions, such as the user's allergies, surgeries, genetic conditions, the user's one or more health concerns, and one or more blood content levels about which the user is interested in obtaining details.

Using information from the user's profile, the wearable diagnostic device can determine the user's clinical correlate of hemoglobin (cCIHb) value (1004). The cCIHb can be determined using clinical information from the user profile, such as the user's medical history, which can include data such as physician reports, past or current medical conditions, clinical diagnoses, etc. In some implementations,
The cCIHb can be a matrix of values representing various aspects of a user's medical history, provided that the user agrees to provide the following personal information:

The wearable diagnostic device may then determine 1006 a clinically relevant demographic value for hemoglobin (cLDHb) for the user by obtaining the oxygen saturation data and temperature data for the user. In some implementations, the cLDHb may be a matrix of values representing the user's estimated oxygen saturation and temperature. In general, various suitable methods may be utilized to obtain the user's oxygen saturation and temperature. In some implementations, the oxygen saturation may be obtained as described herein with reference to FIG. 11, and the temperature may be obtained using a temperature sensor included in the wearable diagnostic device. In some implementations, the cLDHb may be determined in part based on information provided by a user profile, such as one or more user characteristics, such as the user's demographic group, the user's age, and other personal characteristics of the user.

After obtaining the cCIHb and cLDHb, the wearable diagnostic device can determine a clinical dataset range based on the cCIHb and cLDHb (1010). The determined clinical dataset range is mapped to a hemoglobin predicted value for the user's hemoglobin level (1012). A database storing various clinical dataset ranges and hemoglobin predicted values can be queried with the determined clinical dataset range and can return a hemoglobin predicted value that maps to the determined clinical dataset range. A hemoglobin predicted value can then be provided, and the hemoglobin predicted value can be utilized to determine the user's hemoglobin level (1014).

By utilizing the blood pressure and temperature data as well as the predicted values to determine the glucose and hemoglobin levels of the user, the determined glucose and hemoglobin levels of the user have a much higher accuracy compared to methods of determining glucose and hemoglobin levels without utilizing the predicted values. The predicted values include the clinical and demographic information of the user, so that the calculations can more accurately take into account parameters such as skin color and age that may affect the glucose or hemoglobin levels. The predicted values can also be used to correlate the user's susceptibility to certain diseases, so that the user can take preventative measures to minimize the probability of contracting these diseases and improve the health and life expectancy of the user. Furthermore, these multiple tests can be performed and the test results can be obtained on demand by a single device, providing a high degree of convenience to the user.

In addition to obtaining the user's glucose and hemoglobin levels, the wearable diagnostic device can determine the user's blood pressure and oxygen saturation levels. A flow chart of an exemplary method for determining a user's blood pressure and oxygen saturation levels is shown in FIG. 11. Initially, the wearable diagnostic device can receive an indication that a blood pressure measurement needs to be taken (1102). The indication that a blood pressure measurement needs to be taken can include one or more selections made by the user to provide a blood pressure measurement and a request made by one or more processors according to a scheduled time. For example, the wearable diagnostic device can be programmed to provide a blood pressure measurement at a specific time or after a certain period of time. The wearable diagnostic device can then schedule a date and time when a blood pressure measurement needs to be provided to the user and can initiate a method of determining blood pressure and oxygen saturation levels at the scheduled time.

After receiving an indication that a blood pressure measurement needs to be performed (1102), the wearable diagnostic device can determine the type of sensor to be utilized for the blood pressure test and activate the determined type of sensor, which in the case of a blood pressure test may include a piezoelectric vibration sensor and
An IR/light/laser sensor may be included 1104. Activating the sensor may include several operations including, but not limited to, increasing power to the sensor and configuring or calibrating the sensor.

The activated piezoelectric vibration sensor and IR/light/laser sensor can obtain measurements of the user (1106). For example, the piezoelectric vibration sensor can obtain measurements of the user's hand movement, position, proximity, speed,
and direction to generate an electrical signal corresponding to one or more of the detected touch, vibration, and shock motion. The IR/light/laser sensor is configured to emit an infrared signal and detect a reflection of the emitted infrared signal from the user's skin. In some implementations, a preamplifier can be used to amplify the weak pulse signal obtained by the piezoelectric vibration sensor.

The signals detected by the piezoelectric vibration sensor and the IR/light/laser sensor may be processed, for example, by performing analog-to-digital conversion (ADC) and filtering operations to generate an infrared target detection (IRTD) value and a piezoelectric vibration (PV) value (1108).
Various signal processing operations may be performed on the signals detected by the piezoelectric vibration sensor and the IR/light/laser sensor. In some implementations, operations 1104-1108 may be repeated multiple times and averages of multiple IRTD and PV values may be determined.

Processing may also include comparing the determined IRTD and PV values to predicted IRTD and PV values. This comparison produces two sets of blood pressure values. Each of the two sets of blood pressure values is averaged 1112 to obtain a systolic blood pressure value and a diastolic blood pressure value, respectively. The systolic and diastolic blood pressure values may be utilized to determine a mean arterial pressure (MAP) using Equation 3 below.
MAP = diastolic blood pressure + (1/3) (systolic blood pressure - diastolic blood pressure) [Formula 3]

The generation of predicted values for blood pressure measurements is described below with reference to Figure 12. In some implementations, the oxygen saturation level of the user's blood may also be determined using Equation 4 (1
110).
Oxygen saturation level = (( _CHbO2 )/( _CHbO2 + _CHb )) x 100 [Equation 4]

In Equation 4, C _HbO2 is equal to the concentration of oxygenated hemoglobin, and C _Hb is equal to the concentration of deoxygenated hemoglobin. The values of C _HbO2 and C _Hb can be obtained by using an infrared sensor.

After operations 1110 and 1112, the blood pressure and oxygen saturation values are output (1114). For example, in some implementations, the blood pressure and oxygen saturation values are displayed on a display. In some implementations, the blood pressure and oxygen saturation values are output from an audio speaker. In some implementations, the blood pressure and oxygen saturation values can be communicated to another electronic device using a message, such as email, SMS, or MMS. The message can be automatically generated and added without user input. The user can be prompted to confirm whether a message including the blood pressure and oxygen saturation values should be sent to the other electronic device.

In the above exemplary implementation, a predictive value was utilized to determine the user's blood pressure. The predictive value can be determined based on a number of factors using a variety of different methods.
Referring to FIG. 12, to obtain a predicted value of the user's blood pressure, the wearable diagnostic device performs operation 80.
2 and 1002, user profile information can be obtained (1202
). The wearable diagnostic device can obtain clinical and demographic information about the user from the user profile.

Using information from the user's profile, the wearable diagnostic device can determine the user's clinical correlate of blood pressure (cCIBP) and obtain EKG data (1204). The cCIBP can be determined using clinical information obtained from the user's profile, such as the user's medical history, which may include data such as physician reports, past or current medical conditions, clinical diagnoses, etc. The EKG data can be obtained from a variety of suitable sources, such as the user's medical history.

The EKG data is then processed (1206) to determine peak values and time differences between peak values, particularly between R-waves. The wearable diagnostic device may also determine (1206) a clinically relevant demographic value of the user's blood pressure (cLDBP). In some implementations, one or more processors of the wearable diagnostic device may execute one or more programs and algorithms to detect peak values and time differences between peak values detected in the user's EKG. In some implementations, the cLDBP may be determined in part based on one or more user characteristics, such as the user's demographic group, the user's age, and other personal characteristics of the user. In some implementations, the cLDBP may be a matrix of values representing various characteristics, such as the user's age, diet, sex, etc., if the user agrees to provide the following personal information:

After obtaining the cCIBP and cLDBP, the wearable diagnostic device can determine a clinical dataset range based on the cCIBP and cLDBP (1208). The determined clinical dataset range is mapped to a blood pressure prediction value for the user's glucose level (1210). A database storing various clinical dataset ranges and blood pressure prediction values can be queried with the determined clinical dataset range and can return a blood pressure prediction value that maps to the determined clinical dataset range. A blood pressure prediction value can then be provided and the blood pressure prediction value can be utilized to determine the user's blood pressure level (1212).
.

In some implementations, the wearable diagnostic device may execute a process to obtain an EKG and heart rate level of a user wearing the wearable diagnostic device.

When the wearable diagnostic device is fixed to the left arm of a user, a first cardiac electrode can be disposed on or within the bottom surface of the wearable diagnostic device and can be in contact with the upper side of the user's left wrist. A second cardiac electrode is disposed on or within the top surface of the wearable diagnostic device without contacting the skin of the user's left arm. The first cardiac electrode can obtain a signal from the upper part of the wrist of the user's left arm, and the second cardiac electrode can obtain two signals with the second cardiac electrode disposed on the user's chest. The obtained signals can include cardiac potential waveforms, such as voltages generated during contraction of the heart. Data obtained from the three signals is measured using a PQRST (Provocat
The PQRST waves are converted into PQRST (proportional, ion, quality, radiation, severity, time) waves. The PQRST waves can be used to generate Einthoven's triangle, EKG plots, and to calculate additional information such as the user's heart rate. In some cases, the PQRST waves can be used to diagnose cardiac conditions.

In some implementations, a wearable diagnostic device can execute a process to obtain a body temperature for a user wearing the wearable diagnostic device. When the wearable diagnostic device is wrapped around a user's arm and in contact with the user's skin, a temperature sensor of the wearable diagnostic device obtains the user's body temperature based on the skin contact. For example, a temperature sensor can be in contact with a wrist and obtain body temperature data of the user over a period of time. The temperature sensor provides sensor data to one or more processors, which can convert the received data to a Fahrenheit scale and average the obtained body temperature data over one or more periods of time to obtain an estimated body temperature of the user.

In some implementations, the wearable diagnostic device may execute a process to obtain hand vibrations for a user wearing the wearable diagnostic device. The one or more processors may execute operations using a timer and an accelerometer or piezoelectric vibration sensor to:
Hand vibration measurements can be obtained. For example, upon receiving an indication that a user is interested in reviewing information regarding hand vibrations, the processor can set a timer for a particular period of time, e.g., 60 seconds, and instruct the accelerometer or piezoelectric vibration sensor to obtain vibration data over the period of time. The accelerometer or piezoelectric vibration sensor can sense the number and strength of vibrations of the user's hands over a period of time and provide data indicative of the number of vibrations to the processor.

The processor may receive data indicative of the number of vibrations and may determine the frequency of the vibrations. If the vibrations have a frequency between 3 and 7 Hertz (Hz), the vibrations may be classified as vibrations associated with Parkinson's tremor. The processor may also obtain amplitude information from an accelerometer or piezoelectric vibration sensor to determine the strength of any vibrations associated with Parkinson's tremor. Data indicative of the timing, intensity, and frequency of the vibrations may be stored in a user profile in storage and/or presented to the user via a display.

User's glucose level, hemoglobin level, blood pressure level, EKG, heart rate level,
The above processes for obtaining information on body temperature, hand vibration, and cholesterol levels can be performed in parallel, simultaneously, or at different times, with the wearable diagnostic device having sufficient processing power to perform any of these processes at any time and upon the user's request.

In some implementations, multiple diagnostic tests can be performed sequentially or simultaneously. For example,
G tests can be done before or during blood pressure tests. Oxygen saturation tests and temperature tests can be done
The hemoglobin test may be performed before or during the glucose test. The blood pressure and temperature tests may be performed before or during the glucose test. Other variations and combinations are possible. As an example, if a user has a history of a particular condition, such as diabetes, the wearable diagnostic device may periodically perform a test, such as a glucose diagnostic test, that is most relevant to the user's condition, and other tests, such as a blood pressure test, cholesterol test, or oxygen saturation test, may be performed before, after, or during the most appropriate diagnostic test.

In some implementations, a user may input a request to perform one type of diagnostic test, e.g., a glucose test, but the wearable diagnostic device may determine one or more tests, e.g., a blood pressure test, to be performed along with the diagnostic test requested by the user. Additional tests, e.g., frequently selected tests, performed simultaneously, sequentially, or paired by the user, by default settings, by manufacturer settings, or by physician recommendation, may be selected from the list of tests, e.g., the wearable diagnostic device may determine one or more tests, e.g., a blood pressure test, to be performed along with the diagnostic test requested by the user, by default settings, by manufacturer settings, or by physician recommendation ...
The decision may be based on one or more criteria. In some implementations, the decision to perform the additional test may be based on the user's medical history. For example, if a user has diabetes and high blood pressure, each time the user selects a blood pressure test, the wearable diagnostic device:
Glucose testing can also be performed, and if the user selects glucose testing, the wearable diagnostic device can also perform a blood pressure test.

Results from one or more of the above processes can be stored in memory within the wearable diagnostic device, or in a database or cloud account associated with the user. Results from one or more of the above processes can also be stored in a memory within the wearable diagnostic device, or in a database or cloud account associated with the user.
In some implementations, the wearable diagnostic device may display the determined glucose level, hemoglobin level, blood pressure level, EKG, heart rate level, body temperature,
If one or more of the physical, mental, and hand vibrations indicate a serious medical condition, a visual, audio, or electronic alarm may be output. For example, the wearable diagnostic device may generate an audio output, such as a sound wave, that advises the user to see a doctor if, for example, an EKG contains signs of abnormal heart activity or if the temperature is above 102 degrees Fahrenheit.

It will be understood that the implementations and/or operations described herein can be implemented in digital electronic circuitry, or computer software, firmware, or hardware, including the structures disclosed herein and structural equivalents thereof, or a combination of one or more of these. The implementations can be implemented as one or more computer program products, e.g., one or more modules of computer program instructions encoded in a computer-readable medium for execution by or controlling the operation of a data processing apparatus. A computer-readable medium can be a machine-readable storage device, a machine-readable storage substrate,
A data processing device may be a memory device, a composition of matter that generates a machine-readable propagated signal, or one or more combinations thereof. The term "data processing device" encompasses all devices, apparatus, and machines for processing data, including, by way of example, a programmable processor, a computer, or multiple processors or computers. In addition to hardware, a device may include code that creates an execution environment for the computer program, such as code that constitutes a processor firmware, a protocol stack, a database management system, an operating system, or one or more combinations thereof. A propagated signal is an artificially generated signal, such as a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to an appropriate receiving device.

A computer program (also known as a program, software, software application, script, or code) can be written in any type of programming language, including compiled or interpreted languages, and can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program may be part of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to that program, or in several cooperating files (e.g., one
The computer program may be stored in a program or a file that stores one or more modules, subprograms, or portions of code. A computer program may be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.

The processes and logic flows described herein may be performed by one or more programmable processors executing one or more computer programs to perform operations by operating on input data to generate output. The processes and logic flows may also be implemented using special purpose logic circuitry, such as FPGAs (Field Programmable Gate Arrays) or
This may be done by ASIC (Application Specific Integrated Circuit) or the apparatus may be implemented as special purpose logic circuitry.

A processor of a computer that executes a computer program includes, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor receives instructions and data from a read-only memory or a random access memory, or both.

Although the present specification contains many specifics, these should not be interpreted as limitations on the scope of the disclosure or what may be claimed, but rather as descriptions of features specific to a particular embodiment. Certain features described in the present specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable subcombination. Furthermore, features may be described above as acting in a particular combination and may even be claimed as such, but one or more features from the claimed combination may, in some cases, be deleted from the combination, and the claimed combination may be directed to a subcombination or a variation of the subcombination.

It should be understood that the phrases "one or more" and "at least one" include any combination of elements. For example, the phrase "one or more of A and B" includes A, B, or both A and B.
Similarly, the phrase "at least one of A and B" includes A, B, or both A and B.

Thus, certain implementations have been described. Other implementations are within the scope of the following claims. For example, the actions recited in the claims can be performed in a different order and still achieve desirable results.
The present application provides the following aspects of the invention.
(Aspect 1)
1. A system comprising one or more computing devices and one or more storage devices, the one or more storage devices providing operations that, when executed by the one or more computing devices, cause the one or more computing devices to:
receiving input corresponding to a request to initiate a non-invasive diagnostic test to detect a medical condition of the user;
identifying one or more sensors for conducting the non-invasive diagnostic test;
activating the identified one or more sensors based on the non-invasive diagnostic test;
receiving signal data via the one or more sensors;
obtaining a predictive value of the non-invasive diagnostic test based in part on a user profile;
determining a test result based on the predicted value and the received signal data; and outputting the test result via a display or speaker.
(Aspect 2)
2. The system of embodiment 1, wherein the non-invasive diagnostic test includes one or more of a glucose test, a cholesterol test, a hemoglobin test, an oxygen saturation level test, and an electrocardiogram monitoring test.
(Aspect 3)
The act of:
selecting a path based on raw data derived from the received signal data; and obtaining a set of determined values based on the selected path.
2. The system of embodiment 1, wherein determining a test result based on the predicted value and received signal data comprises determining the test result based on the determined value.
(Aspect 4)
The act of:
Obtaining user clinical data and user demographic data from one or more databases;
2. The system of embodiment 1, further comprising: determining a range of a clinical dataset based on the obtained user clinical data and the user demographic data; and mapping the range of the clinical dataset to a predictive value of the noninvasive diagnostic test.
(Aspect 5)
The act of:
determining a second non-invasive diagnostic test to be performed based on (i) a user pattern relating the second non-invasive diagnostic test to the non-invasive diagnostic test, and (ii) a medical history of the user;
activating a second set of one or more sensors based on the second non-invasive diagnostic test;
receiving second signal data via a second set of the one or more sensors;
2. The system of embodiment 1, further comprising: obtaining a second predictive value for the second non-invasive diagnostic test based in part on the user profile; and determining a second test result based on the second predictive value and the received second signal data.
(Aspect 6)
6. The system of embodiment 5, wherein obtaining the predictive value of the non-invasive diagnostic test comprises determining the predictive value of the non-invasive diagnostic test using the second test results.
(Aspect 7)
6. The system of embodiment 5, wherein the second non-invasive diagnostic test is a cholesterol test run simultaneously with the non-invasive diagnostic test which is a glucose test.
(Aspect 8)
6. The system of embodiment 5, wherein the non-invasive diagnostic test and the second non-invasive diagnostic test are performed by a watch comprising the one or more computing devices.
(Aspect 9)
2. The system of embodiment 1, wherein the one or more sensors include one or more of a wireless cardiac electrode, a piezoelectric vibration sensor, an infrared sensor, a temperature sensor, an accelerometer, and a microelectromechanical system (MEMS) sensor.
(Aspect 10)
receiving input corresponding to a request to initiate a non-invasive diagnostic test to detect a medical condition of the user;
identifying, by one or more computing devices, one or more sensors for conducting the non-invasive diagnostic test;
activating the identified one or more sensors based on the non-invasive diagnostic test;
receiving signal data via the one or more sensors;
obtaining, by the one or more computing devices, a predictive value of the non-invasive diagnostic test based in part on a user profile;
determining, by the one or more computing devices, a test result based on the predictive value and the received signal data; and
A computer-implemented method including outputting, by one or more computing devices, the test results via a display or speaker.
(Aspect 11)
11. The computer-implemented method of embodiment 10, wherein the non-invasive diagnostic test includes one or more of a glucose test, a cholesterol test, a hemoglobin test, an oxygen saturation level test, and an electrocardiogram monitoring test.
(Aspect 12)
The act of:
selecting a path based on raw data derived from the received signal data; and obtaining a set of determined values based on the selected path.
11. The computer-implemented method of embodiment 10, wherein determining a test result based on the predicted value and received signal data comprises determining the test result based on the determined value.
(Aspect 13)
The act of:
obtaining user clinical data and user demographic data from one or more databases;
11. The computer-implemented method of embodiment 10, further comprising: determining a range of a clinical dataset based on the obtained user's clinical data and the user's demographic data; and mapping the range of the clinical dataset to a predictive value of the non-invasive diagnostic test.
(Aspect 14)
The act of:
determining a second non-invasive diagnostic test to be performed based on (i) a user pattern relating the second non-invasive diagnostic test to the non-invasive diagnostic test, and (ii) a medical history of the user;
activating a second set of one or more sensors based on the second non-invasive diagnostic test;
receiving second signal data via a second set of the one or more sensors;
11. The computer-implemented method of embodiment 10, further comprising: obtaining a second predictive value of a second non-invasive diagnostic test based in part on the user profile; and determining an outcome of the second test based on the second predictive value and the received second signal data.
(Aspect 15)
20. The computer-implemented method of embodiment 14, wherein obtaining the predictive value of the non-invasive diagnostic test comprises determining the predictive value of the non-invasive diagnostic test using the second test results.
(Aspect 16)
15. The computer-implemented method of embodiment 14, wherein the second non-invasive diagnostic test is a cholesterol test conducted simultaneously with the non-invasive diagnostic test being a glucose test.
(Aspect 17)
15. The computer-implemented method of embodiment 14, wherein the non-invasive diagnostic test and the second non-invasive diagnostic test are performed by a watch comprising the one or more computing devices.
(Aspect 18)
[0023] Aspect 10. The one or more sensors include one or more of a wireless cardiac electrode, a piezoelectric vibration sensor, an infrared sensor, a temperature sensor, an accelerometer, and a microelectromechanical system (MEMS) sensor.
The computer-implemented method described herein.
(Aspect 19)
A watch comprising one or more computer devices and one or more storage devices,
One or more storage devices may be configured to execute the one or more computer devices.
2. The method of claim 1, further comprising:
selecting glucose and cholesterol tests based on a user profile;
identifying one or more sensors for performing the glucose test and the cholesterol test;
activating the identified sensor or sensors;
receiving signal data via the one or more sensors;
obtaining a first predicted value of the glucose test based in part on the user profile;
determining a glucose test result and a cholesterol test result based on the first predicted value and the received signal data; and outputting the glucose test result and the cholesterol test result via a display or speaker of the watch.
(Aspect 20)
the one or more sensors include an infrared sensor and a piezoelectric vibration sensor;
The act of:
selecting a path based on raw data derived from the received signal data; and obtaining a set of determined values based on the selected path;
20. The watch of embodiment 19, wherein determining a glucose test result based on the first predicted value and the received signal data includes determining the glucose test result based on the determined value.

Claims (20)

1. A system comprising one or more computing devices and one or more storage devices storing instructions that cause the one or more computing devices to perform the following operations :
The operation is
receiving input corresponding to a request to initiate a non-invasive diagnostic test to detect a medical condition of the user;
identifying one or more sensors for conducting the non-invasive diagnostic test;
activating the identified one or more sensors based on the non-invasive diagnostic test;
receiving signal data via the one or more sensors;
obtaining a predictive value of the non-invasive diagnostic test based on a user profile including at least a user's clinical data and a user's demographic data ;
determining a test result based on the predicted value and the received signal data; and outputting the test result via a display or speaker.
Including ,
the one or more computer devices include a wearable diagnostic device for providing the user with information regarding the non-invasive diagnostic test of the user based on the received signal data ; and the one or more sensors include a pair of removable first and second cardiac electrodes configured to detect the user's cardiac potential waveform , the first cardiac electrode being disposed on an outer belt layer of the wearable diagnostic device and the second cardiac electrode being disposed on a back cover of the wearable diagnostic device so as to directly contact the user's skin. The system of claim 1, wherein the non-invasive diagnostic tests include one or more of a glucose test, a cholesterol test, a hemoglobin test, an oxygen saturation level test, and an electrocardiogram monitoring test. The act of:
selecting a path based on raw data derived from the received signal data; and obtaining a set of determined values based on the selected path.
The system of claim 1 , wherein determining a test result based on the predicted value and the received signal data comprises determining the test result based on the determined value. The act of:
obtaining clinical data of the user and demographic data of the user from one or more databases;
2. The system of claim 1, further comprising: determining a range of a clinical dataset based on the obtained clinical data of the user and the obtained demographic data of the user; and mapping the range of the clinical dataset to a predictive value of the noninvasive diagnostic test. The act of:
determining a second non-invasive diagnostic test to be performed based on (i) a user pattern relating the second non-invasive diagnostic test to the non-invasive diagnostic test, and (ii) a medical history of the user;
activating a second set of one or more sensors based on the second non-invasive diagnostic test;
receiving second signal data via a second set of the one or more sensors;
10. The system of claim 1, further comprising: obtaining a second predicted value for the second noninvasive diagnostic test based on the user profile; and determining a second test result based on the second predicted value and the received second signal data. The system of claim 5, wherein obtaining the predictive value of the non-invasive diagnostic test includes determining the predictive value of the non-invasive diagnostic test using the second test results. The system of claim 5, wherein the second non-invasive diagnostic test is a cholesterol test performed simultaneously with the non-invasive diagnostic test, which is a glucose test. The system of claim 5, wherein the non-invasive diagnostic test and the second non-invasive diagnostic test are performed by a watch that includes the one or more computing devices. The system of claim 1, wherein the one or more sensors further include one or more of a piezoelectric vibration sensor, an infrared sensor, a temperature sensor, an accelerometer, and a microelectromechanical system (MEMS) sensor. receiving, by one or more sensors, an input corresponding to a request to initiate a non-invasive diagnostic test to detect a medical condition of a user;
identifying, by one or more computing devices, the one or more sensors for conducting the non-invasive diagnostic test;
activating, by the one or more computing devices, the identified one or more sensors based on the non-invasive diagnostic test;
receiving signal data via the one or more sensors;
obtaining, by the one or more computing devices, a predictive value of the non-invasive diagnostic test based on a user profile including at least the user's clinical data and the user's demographic data ;
determining, by the one or more computing devices, a test result based on the predicted value and the received signal data; and outputting, by the one or more computing devices, the test result via a display or speaker,
the one or more computer devices include a wearable diagnostic device for providing the user with information regarding the non-invasive diagnostic test of the user based on the received signal data ; and the one or more sensors include a pair of removable first and second cardiac electrodes configured to detect the user's cardiac potential waveform , the first cardiac electrode being disposed on an outer belt layer of the wearable diagnostic device and the second cardiac electrode being disposed on a back cover of the wearable diagnostic device so as to directly contact the user's skin. The computer-implemented method of claim 10, wherein the non-invasive diagnostic tests include one or more of a glucose test, a cholesterol test, a hemoglobin test, an oxygen saturation level test, and an electrocardiogram monitoring test. The computer-implemented method further comprising:
selecting a path based on raw data derived from the received signal data; and obtaining a set of determined values based on the selected path.
11. The computer-implemented method of claim 10, wherein determining a test result based on the predicted value and the received signal data comprises determining the test result based on the determined value. The computer-implemented method comprising:
obtaining said user clinical data and said user demographic data from one or more databases;
11. The computer-implemented method of claim 10, further comprising: determining a range of a clinical dataset based on the obtained clinical data of the user and the demographic data of the user; and mapping the range of the clinical dataset to a predictive value of the non-invasive diagnostic test. The computer-implemented method further comprising:
determining a second non-invasive diagnostic test to be performed based on (i) a user pattern relating the second non-invasive diagnostic test to the non-invasive diagnostic test, and (ii) a medical history of the user;
activating a second set of one or more sensors based on the second non-invasive diagnostic test;
receiving second signal data via a second set of the one or more sensors;
11. The computer-implemented method of claim 10, further comprising: obtaining a second predicted value for the second non-invasive diagnostic test based on the user profile; and determining a second test result based on the second predicted value and the received second signal data. The computer-implemented method of claim 14, wherein obtaining the predictive value of the non-invasive diagnostic test includes determining the predictive value of the non-invasive diagnostic test using the second test results. The computer-implemented method of claim 14, wherein the second noninvasive diagnostic test is a cholesterol test that is performed simultaneously with the noninvasive diagnostic test that is a glucose test. The computer-implemented method of claim 14, wherein the non-invasive diagnostic test and the second non-invasive diagnostic test are performed by a watch comprising the one or more computing devices. The computer-implemented method of claim 10, wherein the one or more sensors include one or more of a cardiac electrode, a piezoelectric vibration sensor, an infrared sensor, a temperature sensor, an accelerometer, and a microelectromechanical system (MEMS) sensor. 1. An apparatus comprising: a wearable diagnostic watch; and one or more storage devices storing instructions for causing the wearable diagnostic watch to perform the following operations:
The operation is
selecting a glucose test and a cholesterol test based on a user profile including at least clinical and demographic data of a user of the wearable diagnostic watch ;
identifying one or more sensors for performing the glucose test and the cholesterol test;
activating the identified sensor or sensors;
receiving signal data via the one or more sensors;
obtaining a first predicted value of the glucose test based on the user profile;
determining a glucose test result and a cholesterol test result based on the first predicted value and the received signal data; and outputting the glucose test result and the cholesterol test result via a display or a speaker of the wearable diagnostic watch.
Including ,
The device, wherein the one or more sensors include a pair of removable first and second cardiac electrodes configured to detect the user's cardiac potential waveform , the first cardiac electrode being disposed on an outer belt layer of the wearable diagnostic watch and the second cardiac electrode being disposed on a back cover of the wearable diagnostic watch so as to be in direct contact with the user's skin. the one or more sensors include an infrared sensor and a piezoelectric vibration sensor;
The act of:
selecting a path based on raw data derived from the received signal data; and obtaining a set of determined values based on the selected path;
20. The apparatus of claim 19, wherein determining a glucose test result based on the first predicted value and the received signal data comprises determining the glucose test result based on the determined value.

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