KR20200113201A - Wearable diagnostic device - Google Patents

Abstract

A wearable diagnosis device that is worn on a user's body and provides various types of health-related information to the user is described. It is possible to provide real-time, non-surgical, accurate, and continuous data related to the user's heart rate, hemoglobin level, body temperature, oxygen level, glucose level, and blood pressure. In some implementations, the wearable diagnostic device may provide detection of electrocardiogram (EKG) data and Parkinson's symptoms such as involuntary movement of a user's hand. The user may configure the wearable diagnostic device to provide information on one, a plurality, or all of the health-related information described above.

Description

Wearable diagnostic device

The present invention is directed to wearable diagnostic devices, methods, and systems for performing one or more medical diagnostic tests.

With the increasing interest in the health of individuals, there is an increasing demand among users to receive personal health information in a simple and convenient manner. Often users may have to wear multiple devices, each providing information on one aspect of the user's health. There is a need for a device capable of providing information related to a user's health or various aspects in a simple, non-surgical, and convenient manner.

Innovative aspects of the present invention describe wearable diagnostic devices, methods, and systems for performing one or more medical diagnostic tests.

In some implementations, a system may store one or more computer devices and instructions that, when executed by the one or more computer devices, cause the one or more computer devices to perform operations. Storage devices, the operations comprising: receiving an input corresponding to a request to detect a medical condition of a user by initiating a non-surgical diagnostic test; Identifying one or more sensors for performing the non-surgical diagnostic test; Activating the identified one or more sensors based on the non-surgical diagnostic test; Receiving signal data through the one or more sensors; Obtaining a predicted value for the non-surgical 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 through a display or speaker.

Each of the implementation examples may include at least one of the following features. For example, the non-surgical 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.

In some implementations, the operations may 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, wherein determining the test result based on the predicted value and the received signal data includes the test result based on the determined values. It includes making decisions.

In some implementations, the operations may include: obtaining user clinical data and user demographic data from one or more databases; Determining a range of a clinical dataset based on the acquired user clinical data and the user demographic data; And mapping the clinical dataset range to the predicted value for the non-surgical diagnostic test.

The actions include: determining a second non-surgical diagnostic test, wherein the second non-surgical diagnostic test comprises i) a user pattern associating the second non-surgical diagnostic test with the non-surgical diagnostic test, and ii) Being performed based on the user's medical record; Activating one or more sensors of a second set based on the second non-surgical diagnostic test; Receiving second signal data through one or more sensors of the second set; Obtaining a second predicted value for the second non-surgical 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 predicted value for the non-surgical diagnostic test includes determining the predicted value for the non-surgical diagnostic test using the second test result.

In some embodiments, the second non-surgical diagnostic test is a cholesterol test performed concurrently with the non-surgical diagnostic test, which is a glucose test.

In some implementations, the non-surgical diagnostic test and the second non-surgical diagnostic test are performed by a watch comprising the one or more computer devices.

In some implementations, the one or more sensors include one or more of a wireless heart electrode, piezo vibration sensor, infrared sensor, temperature sensor, accelerometer, and MEMS.

According to aspects of the disclosed feature, a watch is one or more computer devices and one or more storing instructions that cause the one or more computer devices to perform actions when executed by the one or more computer devices. Including the above storage devices, the operations include: selecting a glucose test and a cholesterol test based on a user profile; Identifying one or more sensors for performing the glucose test and cholesterol test; Activating the identified one or more sensors; Receiving signal data through the one or more sensors; Obtaining a first predicted value for 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 through the display or speaker of the watch.

In some implementations, the one or more sensors include an infrared sensor and a piezo vibration sensor, and 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, wherein determining the glucose test result based on the first predicted value and the received signal data is performed based on the determined values. It involves determining the glucose test results.

The aspects described above and the implementation examples further described in this specification have several advantages. For example, one wearable diagnostic device may perform a number of non-surgical diagnostic tests including a non-surgical glucose test, a non-surgical cholesterol test, and a non-surgical hemoglobin test. In addition, the wearable diagnosis apparatus may obtain electrocardiogram (EKG) data or detect Parkinson's symptoms such as involuntary movement of a user's hand. The wearable diagnostic device with wireless electrodes can track user histories and medical conditions, and use predicted values, algorithms, and mapping database to obtain more accurate and reliable results for glucose, cholesterol and/or hemoglobin tests. Can provide. The predicted values contain the user's clinical and demographic information and therefore make calculations more accurate taking into account parameters such as skin color and age that can affect glucose or hemoglobin levels. The predicted values can also be used to correlate the predicted values with the user's sensitivity to a particular disease.

Other aspects include those methods, systems, apparatus, computer-readable storage media, and computer programs configured to implement the operations of the methods described above.

Details of one or more aspects described in this specification are set forth in the drawings and the description below. From the corresponding description, drawings, and claims, other features, aspects, and advantages of the invention will also appear.

1, 2, 3, 4, and 5 illustrate exemplary implementation examples of a wearable diagnostic device.
6 shows an exemplary system implemented in a wearable diagnostic device.
7 shows a flowchart of an exemplary method for determining a user's glucose level.
8 shows a flowchart of an exemplary method for determining a glucose prediction value.
9 shows a flowchart of an exemplary method for determining a user's hemoglobin level.
10 shows a flowchart of an exemplary method for determining a predicted hemoglobin value.
11 shows a flow chart of an exemplary method for determining a user's blood pressure level.
12 shows a flowchart of an exemplary method for determining a blood pressure predictive value.
13 shows a flow chart of an exemplary method for determining a user's cholesterol level.
Like reference numbers and designations in the various drawings indicate like elements.

The present disclosure relates to a wearable diagnostic device that is worn on a user's body and provides various types of health-related information to the user. In some implementations, the wearable diagnostic device may perform one or more medical diagnostic tests, and transmit data in real time, non-invasive (or non-invasive), accurate, and continuous to the user. It can be provided in relation to data regarding heart rate, hemoglobin level, body temperature, oxygen level, glucose level, cholesterol, and blood pressure. In addition, in some implementations, the wearable diagnostic device may provide detection of electrocardiogram (ECG) data and Parkinson's symptoms such as involuntary movement of a user's hand. The user may configure the wearable diagnostic device to provide information on one or more of the above-described diagnostic tests. Implementations of the wearable diagnostic device are described below with reference to the drawings.

1 to 5 illustrate a wearable diagnostic device from different sides. In some implementation examples, the wearable diagnosis apparatus may be implemented in the form of a watch as illustrated in FIGS. 1 to 5. 6 further depicts detailed configurations included in the wearable diagnostic device according to some examples of implementations. In general, the wearable diagnostic device can be implemented in a variety of suitable shapes, types, and sizes, and can be any electronic device that can be attached to the user's left arm and can obtain a number of medical examination measurements for the user. .

1 to 5, the wearable watch includes a casing 101, a display 102, detachable wireless cardiac electrodes 103, 113, and belts 104. 109), piezo vibration sensor 105, infrared/light/laser sensor 106, connector 107, temperature sensor 108, control buttons 110, 111, rear cover 112, Belt connector 114, belt fastener 115, charging connector 116, power source 117, outer belt layers 118, 120, insulated wire 119, 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 user's left arm 150.

Casing 101 includes a display 102 (for example, a touch display), a charging connector 116, control buttons 110, 111, and a processor, a printed circuit board (PCB), an integrated circuit. : IC), SoC 123, memory, and one or more electronic components such as a wireless transceiver or may be connected to. The display 102 may use any suitable display, including, for example, a liquid crystal display (LCD), a light emitting diode (LED) display, or an organic light emitting diode display to display various data. Can be implemented. In some implementations, display 102 can be a touch screen, such as a capacitive touch screen.

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

One or more processors in the casing 101 may be implemented in a printed circuit board or integrated circuit, and may be electrically connected to memory, display 102, and other electronic components of the wearable diagnostic device such as a wireless transceiver. . For example, the processor may receive data from the display 102 indicative of user selection, determine the type of information requested by the user, and perform one or more actions based on the information requested by the user. You can create commands.

In some implementations, one or more processors may transmit and receive data using a wireless transceiver. Data may be transmitted/received between the wearable diagnosis device and the secondary device. The secondary device may be a device selected by a user, a network server, or a device capable of communicating with the wearable watch. For example, the user may select another device owned by the user and transmit data from the wearable diagnostic device to the device. As another example, the wearable diagnosis apparatus may be configured to transmit/receive data with one or more network servers according to a user request or a predetermined schedule of transmission/reception data.

The one or more network servers above are services for one or more networks, which may include one or more databases, access points, base stations, storage systems, cloud systems, and modules. Can provide them. One or more servers may be a series of servers running a network operating system. One or more servers may be used or provided for cloud and/or network computing.

Databases in the networks may be a cloud database or a database managed by a database management system (DBMS). The database management system can be implemented as an engine that controls the organization, storage, management, and retrieval of data in the database. Database management systems are often the ability to query, backup and replicate, enforce rules, provide security, perform calculations, perform change and access logging, and automate optimization. Provides. The database management system generally includes a modeling language, a data structure, a database query language, and a transaction mechanism. Depending on the database model, which can include a hierarchical model, network model, relational model, object model, or any other applicable well-known convenient organization, the modeling language It is used to define the schema of the database. Data structures may include fields, records, files, objects, and any other applicable well-known convenient structures to store data. In addition, the data management system may include metadata for stored data.

In some implementations, the database may include a user database that may store information of the user, such as an alias, medical record (medical history), and any medical condition of the user. The user database may also store data related to licenses, permissions, and certifications to access various tools and software. In some cases, the user may select an option to anonymize user data so that information that can identify the user is anonymized and the user identification information is deleted, prior to storing the data in the user database.

In general, various suitable wireless protocols can be used to communicate data with the wearable diagnostic device. For example, the wearable diagnostic device may communicate with one or more networks, devices, or servers using Wi-Fi or Bluetooth communications. In general, various types of networks can be communicated, and various 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 conductive wire. Being connected to a power cable, the charging connector 116 may function as a power interface for charging the power source 117 in the wearable watch by providing power from an external source. The power source 117 may be any suitable battery and may provide power to any electronic component in the wearable diagnostic device.

Casing 101 also includes a power switch 124. In some implementations, in response to selecting the power switch 124 so that the power switch 124 is in a power off position, one or more processors send a command to the power manager 122 It is possible 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 selecting the power switch 124 so that the power switch 124 is in a power on position, the power manager 122 sends a command to the power source 117 to transmit a wearable. One or more components of the diagnostic device may be provided, such as to provide power to the display 102.

The casing 101 is connected to belts 104 and 109, a connector 107, and a belt connector 114 that can be adjusted to secure the wearable diagnostic device to wrists of different sizes. For example, the belts 104 and 109 and the belt connector 114 can wrap around the user's wrist, and the belt fastener 115 is a fixed position for the belts 104 and 109 and the belt connector 114 Can be kept in. The rear cover 112 and the heart electrode 113 (ECG EPIC Sensor) may be disposed on the rear portion of the casing 101.

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

In addition, the wearable diagnostic device includes one or more of the heart electrodes 103 and 113, the piezo vibration sensor 105, the infrared/light/laser sensor 106, the temperature sensor 108, the accelerometer 121, and the impedance sensor. It may include sensors. In some implementations, the piezo vibration sensor 105 may include a micro-electro mechanical system (MEMS) sensor.

The wireless heart electrodes 103 and 113 may include a metallic conductive material configured to sense cardiac electrical potential waveforms such as voltages generated during contraction of the heart. The wireless heart electrode 103 is disposed on the outer belt layer 118 corresponding to the outer circumference of the wearable diagnostic device. The wireless heart electrode 113 may be disposed on or integrated in the rear cover 112 to contact the user's skin when the wearable diagnostic device is fixed to the user's arm 15. The wireless heart electrodes 103 and 113 may be detached from and remounted from the wearable diagnostic device, and may wirelessly communicate data with other electronic devices.

The infrared/light/laser sensor 106 may include a signal generator and a signal detector. The signal generator is capable of generating and transmitting infrared signals with wavelengths in the range of 650-1400 nanometers (nm), for example. This range is particularly useful for obtaining data indicating the presence of oxygen, glucose and hemoglobin molecules in the user's blood. The signal detector may be configured to detect infrared signals received from the user's body.

In some implementations, the infrared/light/laser sensor 106 may detect pulse signals and process the detected signals using Pulse Wave Transit Time (PWTT) in the Artery method. These pulse signals may be combined with data obtained by the piezo vibration sensor 105 to predict blood pressure non-invasively. In addition, the infrared/light/laser sensor 106 may be used non-surgically to determine or obtain data representing oxygen saturation levels (SpO ₂ ) in the user's blood.

Piezo vibration sensor 105 and accelerometer 121 can be used to measure vibrations of a user's hand. For example, the accelerometer 121 may detect changes in direction, speed, vibrations, and rotations. The temperature sensor 108 can be used to measure a user's body temperature. The temperature sensor 108 may be a thermocouple, a silicon bandgap sensor, a thermometer, or a thermistor including one or more sensing resistors. A change in electrical resistance within the sensing resistors may correspond to a change in body temperature. In some implementations, temperature sensors may include a resistive and semiconductor material fabricated in a single package of active and passive components and integrated circuits.

The above sensors can be used to obtain one or more measurements as described in detail below. Operations performed using the wearable diagnostic device to determine glucose levels, hemoglobin levels, blood pressure levels, electrocardiogram (EKG), heart rate levels, body temperature, and hand vibrations are described in detail below. . Such actions allow the user to place the wearable diagnostic device on the user's left arm 150, and one or more belts 104, 109, belt connector 114, belt fastener 115, and outer belt layers. It starts by adjusting (118, 120) to fix the wearable diagnosis device to the user's left arm 150.

When the wearable diagnostic device is fixed to the user's left arm 15, the infrared/light/laser sensor 106 contacts the inferior part of the user's left wrist just above the user's radial ulnar artery. can do. As described above, the infrared/light/laser sensor 106 may include a signal generator that generates and transmits an infrared signal. The signal may be transmitted from the infrared/light/laser sensor 106 to the user's skin in the lower part of the user's left wrist.

The infrared/light/laser sensor 106 may detect reflection of a signal from the user's skin. The sensing signal including the absorption spectrum data may be converted into a digital signal using an analog to digital converter (ADS). As a result of the conversion, raw digital data corresponding to the signal received from the user's skin is generated. 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, and hemoglobin. By determining the absorption spectrum, the presence and corresponding levels of specific molecules present in the user's blood can be assessed.

In some implementations, multiple signal measurements may be obtained such that multiple sets of raw digital data are obtained at various times. Different sets of raw digital data can be accumulated and averaged to yield one raw data set for the user.

In some implementation examples, the wearable diagnostic device may obtain information related to the user's health. Information related to the user's health may include, but is not limited to, the user's location of residence, the user's doctor, the user's pharmacist, the user's medical record, demographic associations with one or more groups of the user, The user's food diet, the number of children the user has, the user's medical history, the user's one or more past or present medical conditions, for example allergies the user is interested in obtaining details on. , Surgery, genetic conditions, one or more health concerns of the user, and one or more blood content levels. For example, the user may indicate that the user is particularly interested in glucose or blood pressure levels. The user can also indicate how often the user wants to obtain information about the user's glucose or blood pressure levels.

Information related to the user's health can be used to create a user profile. The user profile may be stored in the wearable diagnostic device or in a database or server located far from the wearable diagnostic device. In some implementations, user profile data may be processed in one or more ways before it is stored or used, such that personally identifiable information is removed. For example, a user's identity may be treated such that personally identifiable information about the user cannot be determined, or a user's geographic location, identity, or demographic background may be determined by the user so that certain details of the user cannot be determined. Can be generalized to a degree. Thus, the user can control what information is collected and which information is used. To implement this, the user may be provided through the wearable diagnostic device with controls that allow the user to select whether the systems, programs, or features described herein can collect or provide user information. .

In some implementations, the user may subscribe or select to participate in 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 be updated with test results, doctor visit results, diagnosis, or prescriptions. For example, the user may authenticate the user's doctors, pharmacists, or medical record holders to release the user's medical information to a server or a database storing a user profile. This server or database can be managed through a subscription service that can collect information from various sources and update the user's profile. The wearable diagnosis apparatus may receive an update on the user's medical information in real time, periodically, or according to a user request.

The user may directly input information on the user's health and background using the user interface of the wearable diagnosis device. The input user medical information may be stored in a remote location or on the wearable diagnosis device.

The features described with reference to FIGS. 1 to 6 relate to a wearable diagnostic device worn on the user's left arm, but the wearable diagnostic device may have other changed shapes and may be worn or applied to other parts of the user's body in some cases. It will be understood that you can. Furthermore, one or more additional components may be included in or connected to the wearable diagnostic device. For example, in some implementations, the wearable diagnostic device includes one or more of a speaker that outputs information using sound waves, and a microphone that receives audio inputs corresponding to, for example, user commands, requests, or feedback. can do.

The wearable diagnostic device is one or more diagnostic tests to obtain one or more glucose levels, hemoglobin levels, or blood pressure levels associated with a user wearing the wearable diagnostic device based on information received or acquired in connection with the user's health. Can be further configured to run them. These processes are further described with reference to FIGS. 7-12.

7, 9, and 13, the wearable diagnostic device may receive an instruction (command) indicating that a test should 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 should be performed. The indication that the test should be performed may include one or more of a selection made by the user to perform the test, and a request made by one or more processors at a predetermined time. For example, the wearable diagnostic device may be programmed to provide blood pressure that is read at specific times or after specific time intervals. Then, the wearable diagnostic apparatus may schedule days and times at which a specific reading, such as a blood pressure reading, should be provided to the user, and may initiate a method of determining blood pressure and oxygen saturation levels at predetermined times.

After receiving an indication that the test should be performed (702, 902, 1302), one or more processors of the wearable diagnostic device may determine the type of sensor to be used for the test and activate the determined type of sensors (704, 904, 1304). In the case of a glucose or hemoglobin test, the types of sensors determined may include one or more infrared/light/laser sensors. In the case of a cholesterol test, the activated sensors may include one or more of an infrared/light/laser sensor, an impedance sensor, and an electromagnetic sensor. Activating the sensor includes, but is not limited to, providing increased power to the sensors, preheating the sensors to emit or receive signals such as infrared signals, configuring or calibrating the sensors. May include doing.

The activated one or more sensors may obtain measurements characterizing the user (706, 906, 1306). For example, one or more infrared/light/laser sensors may be configured to emit an infrared signal or pulse signal, respectively, for a short period of time, e.g. 30 seconds, and detect that the emitted signal is reflected from the user's skin Can In some cases, reflection measurements and current measurements using an impedance sensor may be acquired repeatedly, or may be acquired under different circumstances, such as different pulsed power or magnetic fields. For example, in cholesterol measurements, a first current or infrared signal measurement can be obtained without applying a magnetic field, and a magnetic field of various low power intensities can be generated for a certain period of time, e.g. 10 seconds, and then one or more Other current or infrared measurements can be obtained.

For glucose and hemoglobin tests, the measured sensed signals are processed (processed) and a specific pathway is selected (708, 908) based on the raw data values. In particular, each of the sensed signals includes absorption spectra data that can be converted into raw digital data using an analog-to-digital converter (ADC). The above processing may optionally include additional processing operations such as down conversion, filtering, convoluting, mixing, and any other suitable signal processing operation.

The raw digital data corresponding to the signal received from the user's skin is used as a basis for selecting a specific path and corresponding predicted values and slope regression values (710, 910). For example, when a glucose test is performed, an exemplary mapping as shown in Table 1 can be used to select a specific path and corresponding predicted values and slope regression values. If the raw values are 500 or more and 700 or less, path A and corresponding glucose (G) predicted value 917 and G slope regression value 0.838 are selected. If the raw values are 701 or more and 850 or less, the path B and the corresponding G predicted value 919 and the G slope regression value 0.838 are selected. If the raw values are 851 or more and 950 or less, the path C and the corresponding G predicted value 1001 and the G slope regression value 0.838 are selected. If the raw values are 951 or more and 1100 or less, the path D and the corresponding G predicted value 1004 and the G slope regression value 0.838 are selected. If the raw values are greater than or equal to 1101, the path E and the corresponding G predicted value 981 and a G slope regression value of 0.838 are selected.

The obtained prediction and slope regression values may then be used to determine the user's glucose level (712). In order to determine the glucose level, the wearable diagnostic device may apply Generalizability Theory and G slope regression to a set of raw data and determine values representing the glucose level using Equation 1 below.

The determined glucose value may be output 714 through various suitable methods. For example, the determined glucose value may be output on a display of the wearable diagnosis device or may be output by a speaker of the wearable diagnosis device.

When a hemoglobin test is performed, an exemplary mapping as shown in Table 2 can be used to select a specific path and corresponding predicted value and slope regression value. If the raw values are 500 or more and 700 or less, path A and a corresponding hemoglobin (Hb) predicted value of 31.5 and an Hb slope regression value of 0.114 are selected. When the raw values are 701 or more and 850 or less, path B and the corresponding Hb predicted value 67 and Hb slope regression value 0.114 are selected. If the raw values are 851 or more and 950 or less, the path C and the corresponding Hb predicted value 81 and the Hb slope regression value 0.114 are selected. If the raw values are 951 or more and 1100 or less, path D and the corresponding Hb predicted value 98 and Hb slope regression value 0.114 are selected. If the raw values are greater than or equal to 1101, the path E and the corresponding Hb predicted value 100.5 and the Hb slope regression value 0.114 are selected.

The obtained prediction and slope regression values may then be used to determine the user's hemoglobin level (912). In order to determine the hemoglobin level, the wearable diagnostic apparatus may apply Hb gradient regression to a set of raw data and determine values representing the hemoglobin level using Equation 2 below.

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

For raw data values less than 500 in the glucose or hemoglobin test, the wearable diagnostic device determines that the values are erroneous and initiates another attempt to acquire raw data values through one or more infrared/light/laser sensors. can do. If a value greater than 500 cannot be obtained after three attempts, the wearable diagnostic device may output an error message indicating that the glucose or hemoglobin test cannot be performed to the user.

When a cholesterol test is performed to determine cholesterol levels (e.g., HDL, LDL, triglyceride molecules) associated with a user wearing a wearable diagnostic device, the inferior part of the user's left wrist is It is exposed to infrared signals emitted from infrared/light/laser sensors. In addition, this area of the user's skin may be exposed to a magnetic field for a short period of time, for example 10 or 30 seconds, after which blood particles excluding cholesterol molecules arrange themselves according to the positive and negative poles of the electromagnetic. do. Data indicative of the infrared absorption spectrum can be obtained before and after applying the magnetic field (1306), and the acquired data can then be processed (1308). Processing of the acquired data may include converting the data into a digital signal using an analog-to-digital converter (ADC) and calculating a raw value by calculating a difference between the absorption spectrum values before and after applying a magnetic field. .

Next, a predicted cholesterol level value is obtained from the database (1310). The database may contain a mapping of cholesterol levels to raw values, and may be organized according to demographic classes. The database can be created based on tests on multiple subjects as described below.

First, the subject's demographic profile can be recorded. The demographic profile may include various types of descriptive information about the subject, such as the subject's age, gender, ethnicity, skin color, marital status, and/or medical conditions. For example, a subject may have a demographic profile of a 48-year-old white male of a widow with skin cancer. For each subject, non-surgical cholesterol tests can be performed using a variety of suitable methods, and the test results can be added to the subject profile.

Next, current and infrared signal measurements for the subject may be obtained using resistance and infrared sensors, respectively, as described above. Current and infrared signal measurements may be performed before the magnetic field is applied and after the magnetic field is applied during a specific time period. Differential values of the measured signals can be calculated and stored as raw values. Raw values are mapped to cholesterol levels obtained from non-surgical cholesterol tests and stored in the subject profile.

Hundreds and thousands of subjects have been tested to create a database that maps raw values to cholesterol levels according to one or more demographic classes (e.g., gender, ethnicity, age, etc.) of a large sample size. It can be made available to subjects. In some implementations, statistical data can be extracted from the above tests so that mean, median, and mode values and raw values of cholesterol levels can be determined for each demographic class.

In general, various types of demographic classes can be formed. For example, in some cases, the demographic class may be limited to age, for example the age of people in their 40s. In some cases, a demographic class may include a number of demographic characteristics, such as age and ethnicity or age, ethnicity, and gender. Accordingly, the database may include a mapping table that likely maps cholesterol levels based on raw values associated with specific demographic classes. Here, the database can be created in an anonymous manner so that the identities of the subjects are not revealed or known.

Referring back to FIG. 13, a predicted cholesterol level value based on the raw values obtained in operation 1308 is obtained from the database (1310). In particular, the raw value obtained in step 1308 and the cholesterol level mapped to the demographic profile of the user wearing the wearable diagnostic device are obtained by referring to a mapping table in the database.

Next, the predicted cholesterol level value may be determined as a likely cholesterol level of the user wearing the wearable diagnostic device (1312). In some implementations, the predicted cholesterol level value can be compared to the user's previously obtained cholesterol levels. When the difference between the predicted cholesterol level and the recently acquired cholesterol level of the user is greater than a threshold value, for example 3%, the wearable diagnostic device may repeat operations 1302 to 1310 to obtain a newly predicted cholesterol level. . This repetition can be continued until the predicted cholesterol level value is within the critical difference of the recently obtained cholesterol level. In some cases, if three repetitions are performed without satisfying the above threshold difference, the predicted cholesterol level value obtained in the third repetition may be determined as the possible cholesterol level of the user wearing the wearable diagnostic device. The user's determined possible cholesterol level may then be displayed by the display 102 of the wearable diagnostic device.

In the exemplary embodiments described above, predictive values were used to determine glucose, hemoglobin, or cholesterol levels. The predicted values can be determined using a variety of different methods and based on several factors. The method of obtaining predicted values for cholesterol levels has been described above. A method of obtaining predicted values for glucose and hemoglobin levels is further described with reference to FIGS. 8 and 10.

Referring to FIG. 8, the wearable diagnosis apparatus may obtain user profile information in order to obtain a predicted value for the user's glucose level (802 ). User profile information includes the user's residence, the user's doctor, the user pharmacist, the user's medical record holder, the user's demographic associations with one or more groups, the user's diet, the number of children the user has, and the user's Medical history, one or more past or present medical conditions of the user, e.g. allergies, surgeries, genetic conditions, one or more health of the user, the user is interested in obtaining details Concerns, and one or more of one or more blood content levels

Using the information from the user's profile, the wearable diagnostic device may determine a clinical correlation information value for glucose (cCIG) of glucose for the user based on the user's blood pressure and temperature (804). In some implementations, cCIG may be a matrix (matrix) of values representing likely blood pressure and temperature. In general, various suitable methods can be used to obtain the user's blood pressure and temperature. In some implementations, the blood pressure may be obtained as described herein with reference to FIGS. 11-12, and the temperature may be obtained using a temperature sensor included in the wearable diagnostic device. In some cases, cCIG may be determined using clinical information obtained from a user profile, such as a user's medical record including data such as a doctor's report, past or present medical conditions, clinical diagnoses, and the like.

Next, the wearable diagnostic device may determine a clinical Linked Demographic Value for Glucose (cLDG) of glucose for the user (806). The cLDG may be determined based in part on one or more user characteristics, 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 value described above is associated with a specific route such as route A, or if the user profile represents a specific demographic origin or user's profile, then the cLDG value is the cLDG value is the raw glucose value or a specific It can be determined in a way that corresponds to a demographic source or user's profile. In some implementations, if the user agrees to provide personal information, the cLDG may be a matrix of values representing various characteristics such as the user's age, diet, and gender.

After acquiring cCIG and cLDG, the wearable diagnostic apparatus may determine a clinical dataset range based on the cCIG and cLDG (810). The determined clinical dataset range is mapped to a glucose predicted value for the user's glucose level (812). A database storing various clinical data set ranges and glucose prediction values may be inquired using the determined clinical data set range, and a glucose prediction value mapped to the determined clinical data set range may be returned. The glucose prediction value may then be provided and used to determine the user's glucose level (814).

Referring to FIG. 10, in order to obtain a predicted value for the user's hemoglobin level, the wearable diagnosis apparatus may obtain user profile information (1002). User profile information includes the user's residence, the user's doctor, the user pharmacist, the user's medical record holder, the user's demographic associations with one or more groups, the user's diet, the number of children the user has, and the user's Medical history, one or more past or present medical conditions of the user, e.g. allergies, surgeries, genetic conditions, one or more health of the user, the user is interested in obtaining details Concerns, and one or more of one or more blood content levels

Using the information from the user profile, the wearable diagnostic device may determine a clinical correlation information value for hemoglobin (cCIHb) of hemoglobin (1004). The cCIHb may be determined using clinical information obtained from a user profile, such as a user's medical record, including data such as a doctor's report, past or present health conditions, clinical diagnoses, and the like. In some implementations, if the user agrees to provide personal information, cCIHb may be a matrix of values representing various aspects of the user's medical record.

Next, the wearable diagnostic device may determine a clinical Linked Demographic value for Hemoglobin (cLDHb) of hemoglobin for the user by acquiring the user's oxygen saturation and temperature data (1006). In some implementations, cLDHb can be a matrix of values representing a user's likely oxygen saturation and temperature. In general, a variety of suitable methods can be used to obtain a user's oxygen saturation and temperature. In some implementation examples, 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 will be determined based in part on information provided by the 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. I can.

After acquiring cCIHb and cLDHb, the wearable diagnostic device may determine a clinical dataset range based on cCIHb and cLDHb (1010). The determined clinical dataset range is mapped to a predicted hemoglobin value for the user's hemoglobin level (1012). A database storing various clinical data set ranges and hemoglobin prediction values may be searched using the determined clinical data set range, and a predicted hemoglobin value mapped to the determined clinical data set range may be returned. The hemoglobin prediction value may then be provided and used to determine the user's hemoglobin level (1014).

By determining the user's glucose and hemoglobin levels using predicted values in addition to using blood pressure and temperature data, the determined user's glucose and hemoglobin levels are much higher compared to methods of determining glucose and hemoglobin levels without using predicted values. Have accuracy. The predicted values include the user's clinical and demographic information, thus allowing more accurate calculations to take into account parameters such as skin color and age that may affect glucose or hemoglobin levels. In addition, the predicted values can be used to correlate the predicted values with the user's susceptibility to specific diseases, so users can take precautions to minimize the likelihood of developing these diseases and improve the user's health and life expectancy. You can take them. Furthermore, multiple such tests can be performed, and results from these tests can be obtained through a single device in accordance with the desire to provide a high level of convenience to the user.

In addition to obtaining the user's glucose and hemoglobin levels, the wearable diagnostic device may determine the user's blood pressure and oxygen saturation levels. A flowchart of an exemplary method of determining a user's blood pressure and oxygen saturation levels is depicted in FIG. 11. First, the wearable diagnosis apparatus may receive an indication that blood pressure determination should be performed (1102). The indication that blood pressure determination should be performed may include one or more of a selection made by a user and a request made by one or more processors according to a predetermined time to provide a blood pressure reading. For example, the wearable diagnostic device may be programmed to provide a blood pressure reading at or after specific time intervals. The wearable diagnosis apparatus may schedule days and times for which blood pressure reading should be provided to the user afterwards, and may initiate the above method of determining blood pressure and oxygen saturation levels at the predetermined times.

After receiving an instruction that blood pressure measurement should be performed (1102), the wearable diagnostic device may determine the type of sensor used for the blood pressure test and activate the determined type of sensors. May include a /light/laser sensor (1104). Activating the sensors may include, but is not limited to, several actions including, but not limited to, providing increased power to the sensors and configuring or calibrating the sensors.

The activated piezo vibration sensor and infrared/light/laser sensor may acquire user measurements (1106). For example, the piezo vibration sensor senses or senses one or more of the user's hand movement, position, proximity, speed, and direction, and senses one or more of the touch, vibration, and shock movements. Are configured to generate electrical signals corresponding to them. The infrared/light/laser sensor is configured to emit an infrared signal and sense a reflection of the emitted infrared signal from the user's skin. In some implementations, a preamplifier may be used to amplify the weak pulse signals obtained through the piezo vibration sensor.

The signals detected by the piezo vibration sensor and the infrared/light/laser sensor are, for example, analog-to-digital conversion (IRTD) values and piezo vibration (PV) values. ADC) and filtering operations (1108). In general, various signal processing operations may be performed on signals detected by the piezo vibration sensor and the infrared/light/laser sensor. In some implementations, operations 1104-1108 may be repeated multiple times and average values of multiple IRTD and PV values may be determined.

The above processing may also include comparing the determined IRTD and PV values with predicted IRTD and PV values. This comparison produces two sets of blood pressure values. Each of the two sets of blood pressure values are averaged 1112 to yield systolic and diastolic pressure values respectively. The systolic and diastolic pressure values may be used to determine the mean arterial pressure (MAP) using Equation 3 described below.

The generation of predicted values for blood pressure measurements is described below with reference to FIG. 12. In some implementations, the oxygen saturation level of the user's blood may also be determined using Equation 4 (1110).

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

After operations 1110 and 1112, blood pressure and oxygen saturation values are output (1114). For example, in some implementations, blood pressure and oxygen saturation values are displayed by the display. In some implementations, blood pressure and oxygen saturation values are output through an audio speaker. In some implementations, blood pressure and oxygen saturation values may be communicated to another electronic device using a message such as email, SMS, or MMS. Messages can be automatically generated and transferred without any user input. The user may be induced to check whether the above message with blood pressure and oxygen saturation values should be transmitted to another electronic device.

In the example implementation examples described above, the predicted values were used to determine the user's blood pressure. The predicted values can be determined using a variety of different methods and based on several factors. Referring to FIG. 12, in order to obtain a predicted value for the user's blood pressure, the wearable diagnosis apparatus may obtain user profile information as described in operations 802 and 1002 (1202 ). The wearable diagnostic device may acquire clinical and demographic information of a user from a user profile.

Using the information from the user profile, the wearable diagnostic device may determine a clinical correlation information value for blood pressure (cCIBP) for the user and obtain EKG data (1204). The cCIBP may be determined using clinical information obtained from a user profile, such as a user's medical record, including data such as a doctor's report, past or present health conditions, clinical diagnoses, and the like. EKG data can be obtained from a variety of suitable sources, such as, for example, a user's medical record.

Next, the EKG data is processed to determine peak values, particularly the time difference and peak values between the R waves (1206). In addition, the wearable diagnosis apparatus may determine a clinical linked demographic value for blood pressure (cLDBP) of the user (1206). 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 detected peak values in a user's EKG. In some implementations, the cLDBP may be determined based in part 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, when the user has agreed to provide personal information, the cLDBP may be a matrix of values representing various characteristics such as the user's age, diet, and gender.

After acquiring cCIBP and cLDBP, the wearable diagnostic apparatus may determine a clinical dataset range based on cCIBP and cLDBP (1208). The determined clinical data set range is mapped to a blood pressure predicted value for the user's glucose level (1210). A database storing various clinical data set ranges and blood pressure prediction values may be searched using the determined clinical data set range, and may return a blood pressure prediction value mapped to the determined clinical data set range. The blood pressure prediction value may then be provided and used to determine the user's blood pressure level (1212).

In some implementation examples, the wearable diagnostic device may execute a process for acquiring an EKG and a heart rate level for a user wearing the wearable diagnostic device.

When the wearable diagnosis device is fixed to the user's left arm, the first heart electrode may be disposed on or inside the bottom surface of the wearable diagnosis device, and may contact the upper side of the user's left wrist. The second heart electrode is disposed on or inside the upper surface of the wearable diagnostic device without contacting the skin on the user's left arm. The first heart electrode may acquire a signal from the upper portion of the user's wrist of the left arm, and the second heart electrode may acquire two signals together with the second heart electrode located on the user's chest. The acquired signals may include cardiac electrical potential waveforms such as voltages generated during contraction of the heart. The data obtained from the three signals are converted into stimulus, quality, radiation, severity, and time (Provocation, Quality, Radiation, Severity, Time: PQRST) waves. PQRST waves can be used to generate Einthoven's triangles, EKG plots, and to calculate additional information such as the user's heart rate. In some cases, PQRST waves can be used to diagnose heart conditions.

In some implementations, the wearable diagnostic device may execute a process for obtaining body temperature associated with a user wearing the wearable diagnostic device. After the wearable diagnostic device covers the user's arm and contacts the user's skin, the temperature sensor in the wearable diagnostic device will acquire the user's temperature based on the skin contact. For example, the temperature sensor may contact the wrist to obtain the user's body temperature data over a specific time period. The temperature sensor provides sensor data to one or more processors, converts the received data to a Fahrenheit scale, and averages the temperature data acquired over one or more cycles to allow the user's likely body temperature. Can be calculated.

In some implementation examples, the wearable diagnostic device may execute a process for acquiring a hand vibration (hand vibration) associated with a user wearing the wearable diagnostic device. One or more processors may perform operations with a timer and accelerometer or piezo vibration sensor to obtain hand vibration measurements. For example, after receiving an indication (command) that the user is interested in viewing information about hand vibration, the processor sets a timer for a specific time period, e.g. 60 seconds, and the accelerometer or piezo vibration sensor. It can be instructed to obtain vibration data for a specific time period. The accelerometer or piezo vibration sensor may sense the number and intensity of hand vibrations of the user in the above specific time interval and provide data representing the number of vibrations to the processor.

The processor may receive data representing the number of vibrations and determine the frequency of the vibrations. If the vibrations have a frequency between 3-7 hertz (Hz), the vibrations can be classified as vibrations associated with Parkinson's tremors. The processor may also obtain amplitude information from an accelerometer or piezo vibration sensor to determine the intensity of any vibrations associated with Parkinson's tremors. Data indicative of the timing, intensity, and frequency of vibrations may be stored in a user profile of the storage device and provided to the user through a display.

The processes described above for obtaining information about the user's glucose levels, hemoglobin levels, blood pressure levels, EKG, heart rate levels, body temperature, hand vibrations, and cholesterol levels can be performed in parallel, simultaneously, or at different times. Can be run on The wearable diagnostic device has sufficient processing power to execute these processes at any time in response to a user request.

In some implementations, multiple diagnostic tests may be performed sequentially or simultaneously. For example, EKG tests can be performed before or during a blood pressure test. The oxygen saturation test and body temperature test may be performed before or during the hemoglobin test. The blood pressure test and body temperature test can be performed before or during the glucose test. Other variations and combinations are possible. For example, if the user has a history of certain conditions such as diabetes, the wearable diagnostic device periodically performs a test most relevant to the user's condition, such as a glucose diagnosis test, and before, after, or after the most relevant test. Meanwhile, other tests such as a blood pressure test, a cholesterol test, or an oxygen saturation test may be performed.

In some implementations, the user may enter a request to perform one type of diagnostic test, e.g., a glucose test, but the wearable diagnostic device may perform one or more tests, e.g., to be performed in conjunction with the diagnostic test requested by the user. Listen to the blood pressure test can be determined. One such as tests that are frequently selected to be performed simultaneously, sequentially, or in pairs, for example by the user, by default settings, by manufacturer's settings, by doctor's recommendation. Alternatively, the above additional tests may be determined based on more criteria. In some implementations, additional tests can be determined based on the user's medical history. For example, if the user suffers from diabetes and high blood pressure, the wearable diagnostic device may also perform a glucose test whenever the user selects a blood pressure test. If the user selects a glucose test, the wearable diagnostic device may also perform a blood pressure test.

Results obtained from executing one or more of the above-described processes may be stored in a memory of the wearable diagnostic device or in a database or cloud account associated with the user. Also, results obtained from executing one or more of the processes described above may be displayed through a display. In some implementations, the wearable diagnostic device may be visual, acoustic, or if one or more of the determined glucose levels, hemoglobin levels, blood pressure levels, EKG, heart rate levels, body temperature, and hand vibrations indicate a serious medical condition. Alternatively, an electronic alarm can be output. If, for example, the EKG contains abnormal signs of heart movement or the body temperature is over 102 degrees Fahrenheit, for example, the wearable diagnostic device may generate audio output such as sound waves (sound waves) while recommending to go to the doctor.

Implementations and/or operations described in this specification may be implemented in digital electronic circuitry, including the structures described in this specification and structural equivalents thereof, or in computer software, firmware, or hardware, or one or more combinations thereof. It should be understood that it can. Implementation examples may be implemented as one or more modules of computer program instructions executed by one or more computer program products, for example, computer program instructions executed by a data processing apparatus or encoded in a computer readable medium for controlling its operation. Computer-readable media is a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of substances that affect a machine-readable propagated signal, or a combination of one or more of them. Can be The term “data processing device” includes all devices and machines for processing data, including, for example, a programmable processor, a computer, or multiple processors or computers. The above device may include, in addition to hardware, code that creates an execution environment for a computer program, for example, a processor firmware, a protocol stack, a database management system, an operating system, or a code constituting one or more combinations thereof. have. The radio signal may be an artificially generated signal, for example an electronic, optical, or electromagnetic signal generated by a machine that is generated to encode information for transmission to a suitable receiver device.

Computer programs (also known as programs, software, software applications, scripts, or code) can be written in any form of programming language, including compiled or interpreted languages, which can be used as standalone programs or computing environments. It can be arranged in any form including modules, components, subroutines, or other units suitable for use in Computer programs do not necessarily correspond to files in the file system. A program is a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), a single file dedicated to the program in question, or a plurality of coordinated files. ) Files (eg, files that store one or more modules, sub programs or portions of code). The computer program may be located at one site or may be distributed over several sites and executed on one computer or multiple computers 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 an operation by operating on input data and generating output. In addition, processes and logic flows can be performed and implemented by special purpose logic circuits such as field programmable gate array (FPGA) or application specific integrated circuit (ASIC). I can.

Processors of computers executing a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. In general, the processor will receive instructions and data from read-only memory or random access memory or both.

Although this specification includes many details, these should not be construed as limitations on the scope of the claims or the scope of the present disclosure, but as descriptions of features specific to the specific embodiments. Certain features described herein within the context of separate embodiments may be implemented in combination in a single embodiment. Conversely, various features described within the context of a single embodiment may also be implemented separately or in any suitable sub-combination within multiple embodiments. Furthermore, although features have been described above as operating within certain combinations and may be claimed as such, one or more features from a claimed combination may in some cases be excluded from the combination, and the claimed combination may be subordinate to It may correspond to variations of combinations or subcombinations.

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

Specific implementation examples are described here. Other implementation examples are also within the scope of the claims that follow. For example, the actions (actions) included in the claims may be performed in a different order and still achieve the desired results.

Claims (20)

One or more computer devices, and one or more storage devices storing instructions that, when executed by the one or more computer devices, cause the one or more computer devices to perform operations, the The actions are:
Receiving an input corresponding to a request for detecting a medical condition of a user by starting a non-surgical diagnostic test;
Identifying one or more sensors for performing the non-surgical diagnostic test;
Activating the identified one or more sensors based on the non-surgical diagnostic test;
Receiving signal data through the one or more sensors;
Obtaining a predicted value for the non-surgical diagnostic test based in part on a user profile;
Determining a test result based on the predicted value and the received signal data; And
And outputting the test results through a display or speaker. The method of claim 1,
The non-surgical 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. The method of claim 1,
The above actions are:
Selecting a path based on raw data obtained from the received signal data; And
Further comprising obtaining one set of determined values based on the selected path,
Determining the test result based on the predicted value and the received signal data comprises determining the test result based on the determined values. The method of claim 1,
The above actions are:
Obtaining user clinical data and user demographic data from one or more databases;
Determining a range of a clinical dataset based on the acquired user clinical data and the user demographic data; And
The system further comprising mapping the clinical dataset range to the predicted value for the non-surgical diagnostic test. The method of claim 1,
The above actions are:
Determine a second non-surgical diagnostic test, wherein the second non-surgical diagnostic test comprises i) a user pattern associating the second non-surgical diagnostic test with the non-surgical diagnostic test, and ii) the user's medical record Performed on the basis of;
Activating one or more sensors of a second set based on the second non-surgical diagnostic test;
Receiving second signal data through one or more sensors of the second set;
Obtaining a second predicted value for the second non-surgical diagnostic test based in part on the user profile; And
And determining a second test result based on the second predicted value and the received second signal data. The method of claim 5,
The system for obtaining the predicted value for the non-surgical diagnostic test includes determining the predicted value for the non-surgical diagnostic test using the second test result. The method of claim 5,
The second non-surgical diagnostic test is a cholesterol test performed simultaneously with the non-surgical diagnostic test, which is a glucose test. The method of claim 5,
The non-surgical diagnostic test and the second non-surgical diagnostic test are performed by a watch comprising the one or more computer devices. The method of claim 1,
The one or more sensors include one or more of a wireless heart electrode, a piezo vibration sensor, an infrared sensor, a temperature sensor, an accelerometer, and a micro-electro mechanical system (MEMS). Receiving an input corresponding to a request for detecting a medical condition of a user by starting a non-surgical diagnostic test;
Identifying, by one or more computer devices, one or more sensors for performing the non-surgical diagnostic test;
Activating the identified one or more sensors based on the non-surgical diagnostic test;
Receiving signal data through the one or more sensors;
Obtaining, by the one or more computer devices, a predicted value for the non-surgical diagnostic test based in part on a user profile;
Determining, by the one or more computer devices, a test result based on the predicted value and the received signal data; And
And outputting the test results via a display or speaker, by the one or more computer devices. The method of claim 10,
The non-surgical diagnostic test is a computer-implemented method comprising one or more of a glucose test, a cholesterol test, a hemoglobin test, an oxygen saturation level test, and an electrocardiogram monitoring test. The method of claim 10,
Selecting a path based on raw data obtained from the received signal data; And
Further comprising obtaining a set of determined values based on the selected path,
Determining the test result based on the predicted value and the received signal data comprises determining the test result based on the determined values. The method of claim 10,
Obtaining user clinical data and user demographic data from one or more databases;
Determining a range of a clinical dataset based on the user clinical data and the user demographic data; And
And mapping the clinical dataset range to the predicted value for the non-surgical diagnostic test. The method of claim 10,
Determine a second non-surgical diagnostic test, wherein the second non-surgical diagnostic test comprises i) a user pattern associating the second non-surgical diagnostic test with the non-surgical diagnostic test, and ii) the user's medical record Performed on the basis of;
Activating one or more sensors of a second set based on the second non-surgical diagnostic test;
Receiving second signal data through one or more sensors of the second set;
Obtaining a second predicted value for the second non-surgical diagnostic test based in part on the user profile; And
The method further comprising determining a second test result based on the second predicted value and the received second signal data. The method of claim 14,
The method of obtaining the predicted value for the non-surgical diagnostic test comprises determining the predicted value for the non-surgical diagnostic test using the second test result. The method of claim 14,
The second non-surgical diagnostic test is a method implemented by a computer, which is a cholesterol test performed simultaneously with the non-surgical diagnostic test, which is a glucose test. The method of claim 14,
The method implemented by a computer, wherein the non-surgical diagnostic test and the second non-surgical diagnostic test are performed by a watch comprising the one or more computer devices. The method of claim 10,
The one or more sensors are computer-implemented method comprising one or more of a wireless heart electrode, piezo vibration sensor, infrared sensor, temperature sensor, accelerometer, and MEMS. One or more computer devices and one or more storage devices storing instructions that when executed by the one or more computer devices cause the one or more computer devices to perform operations, the operations :
Selecting a glucose test and a cholesterol test based on the user profile;
Identifying one or more sensors for performing the glucose test and cholesterol test;
Activating the identified one or more sensors;
Receiving signal data through the one or more sensors;
Obtaining a first predicted value for 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
And outputting the glucose test result and the cholesterol test result through a display or a speaker of the watch. The method of claim 19,
The one or more sensors include an infrared sensor and a piezo vibration sensor,
The above actions are:
Selecting a path based on raw data obtained from the received signal data; And
Further comprising obtaining a set of determined values based on the selected path,
Determining the glucose test result based on the first predicted value and the received signal data comprises determining the glucose test result based on the determined values.

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