KR102563533B1 - wearable diagnostic device - Google Patents

Abstract

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

Description

wearable diagnostic device

The present invention relates to a wearable diagnostic device, methods, and systems for performing one or more medical diagnostic tests.

As interest in health of individuals increases, demand for receiving personal health information in a simple and convenient manner is increasing among users. Often users may be required 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.
(Patent Document 1) US 2017332980 A1
(Patent Document 2) US 2017055891 A1
(Patent Document 3) US 2016120460 A1

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 include one or more computer devices and one or more storage instructions that, when executed by the one or more computer devices, cause the one or more computer devices to perform operations. storage devices, wherein the operations include: receiving an input corresponding to a request for initiating a non-surgical diagnostic test to detect a medical condition of a user; 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 via the one or more sensors; obtaining a predictive value for the non-surgical diagnostic test based in part on a user profile; determining a test result based on the prediction 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, cholesterol test, hemoglobin test, oxygen saturation level test, and 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 determines the test result based on the determined values. includes deciding

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

The actions include: determining a second non-surgical diagnostic test, wherein the second non-surgical diagnostic test is i) a user pattern associating the second non-surgical diagnostic test with the non-surgical diagnostic test, and ii) performed based on the user's medical records; activating a second set of one or more sensors based on the second non-surgical diagnostic test; receiving second signal data via the second set of one or more sensors; obtaining a second predictive 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 predictive value for the non-surgical diagnostic test includes determining the predictive value for the non-surgical diagnostic test using the second test result.

In some implementations, the second non-surgical diagnostic test is a cholesterol test performed concurrently with the non-surgical diagnostic test that 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 cardiac electrode, a piezo vibration sensor, an infrared sensor, a temperature sensor, an accelerometer, and a MEMS.

According to aspects of the disclosed feature, a watch includes one or more computer devices and one or more computer devices that store instructions that, when executed by the one or more computer devices, cause the one or more computer devices to perform operations. The above storage devices, wherein 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 via the one or more sensors; obtaining a first predictive 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 prediction value and the received signal data; and outputting the glucose test result and the cholesterol test result through a 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 actions include: selecting a path based on raw data obtained from 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 determines the set of determined values based on the determined values. This includes determining the glucose test results.

The aspects described above and implementation examples further described herein have several advantages. For example, one wearable diagnostic device can perform multiple non-surgical diagnostic tests, including a non-surgical glucose test, a non-surgical cholesterol test, and a non-surgical hemoglobin test. Also, the wearable diagnostic device may acquire electrocardiogram (EKG) data or detect Parkinson's symptoms such as involuntary movement of a user's hand. A wearable diagnostic device with wireless electrodes can track user history and medical conditions, and use predictive values, algorithms, and mapping databases to provide more accurate and reliable results for glucose, cholesterol and/or hemoglobin tests. can provide Predictive values include the user's clinical and demographic information and therefore make calculations more accurate that take into account parameters such as skin color and age that can affect glucose or hemoglobin levels. Predictive values can also be used to associate predictive values with a user's susceptibility to a particular disease.

Other aspects include corresponding 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 description, drawings, and claims, other features, aspects, and advantages of the present invention will also become apparent.

1, 2, 3, 4, and 5 illustrate exemplary implementations of a wearable diagnostic device.
6 shows an exemplary system implemented in a wearable diagnostic device.
7 depicts a flow chart of an exemplary method for determining a user's glucose level.
8 shows a flow chart of an example method for determining a glucose predictive value.
9 shows a flow chart of an exemplary method for determining a user's hemoglobin level.
10 shows a flow chart of an example 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 flow chart of an example 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 provide real-time, non-surgical (or non-invasive), accurate, and continuous data to the user. It can provide data regarding heart rate, hemoglobin level, body temperature, oxygen level, glucose level, cholesterol, and blood pressure. Also in some implementations, the wearable diagnostic device can provide electrocardiogram (EKG) data and detection of Parkinson's symptoms, such as involuntary movements of a user's hand. A user may configure the wearable diagnostic device to provide information about one or more of the diagnostic tests described above. Implementations of the wearable diagnostic device are described below with reference to the drawings.

1 to 5 show the wearable diagnostic device from different sides. In some implementation examples, the wearable diagnostic device may be implemented in the form of a watch as shown in FIGS. 1 to 5 . 6 further depicts detailed components included in a wearable diagnostic device according to some implementation examples. In general, a wearable diagnostic device may come in a variety of suitable shapes, types, and sizes, and may be any electronic device capable of attaching to a user's left arm and obtaining multiple health diagnostic measurements of the user. .

1 to 5, the wearable watch includes a casing 101, a display 102, detachable wireless cardiac electrodes 103 and 113, 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 layer 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 left arm 150 of the user.

The casing 101 includes a display 102 (eg, a touch display), a charging connector 116, control buttons 110 and 111, and a processor, a printed circuit board (PCB), and an integrated circuit. : IC), SoC 123, memory, and one or more electronic components such as a wireless transceiver. The display 102 may be configured using any suitable display including, for example, a liquid crystal display (LCD), a light emitting diode (LED) display, or an organic light emitting diode display for displaying 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 and receive input from the user. The control buttons 110 and 111 in the wearable diagnostic device may be used by a user to navigate a user interface, make a selection, and execute one or more operations. In some implementations, display 102 can 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 may receive data from one or more processors and provide the data to display 102 for output to a user. For example, in some cases, a user may enter a request to provide the user's glucose levels via a user interface. After determining the user's glucose levels, information indicative of the user's glucose levels may be output via a user interface displayed on display 102 .

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

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

The one or more network servers provide 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 for or provide cloud and/or network computing.

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

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

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

The charging connector 116 may be a port configured to connect to a power cable such as a universal serial bus or conductive wire. Connected to the power cable, the charging connector 116 may serve as a power interface to charge the power source 117 in the wearable watch by providing power from an external source. Power source 117 may be any suitable battery and may provide power to any electronic component within the wearable diagnostic device.

Casing 101 also includes a power switch 124 . In some implementations, in response to selecting power switch 124 to place power switch 124 in a power off position, one or more processors send a command to power manager 122 to: Power supply to one or more components of the wearable diagnostic device, for example, the display 102 may be stopped. In some implementations, in response to selecting power switch 124 to place power switch 124 in a power on position, power manager 122 sends a command to power source 117 to place the wearable It may be configured to provide power to one or more components of the diagnostic device, for example the display 102 .

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

One or more insulated wires 119 may be integrated into the structure of the wearable diagnostic device and may provide electrical connections 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 the central axis of the wearable diagnostic device, between the power source 117 and the casing 101 and between the power source 117. An electrical connection may be provided between the and the infrared/light/laser sensor 106 .

In addition, the wearable diagnostic device may include one or more sensors such as cardiac electrodes 103 and 113, a piezo vibration sensor 105, an infrared/light/laser sensor 106, a temperature sensor 108, an accelerometer 121, and an impedance sensor. May contain sensors. In some implementations, the piezo vibration sensor 105 can include a micro-electro mechanical system (MEMS) sensor.

Wireless cardiac electrodes 103, 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 cardiac electrodes 113 may be disposed on or integrated into the back cover 112 to contact the user's skin when the wearable diagnostic device is secured to the user's arm 15 . The wireless heart electrodes 103 and 113 can be detached and reattached from the wearable diagnostic device, and can communicate data wirelessly with other electronic devices.

The infrared/light/laser sensor 106 may include a signal generator and a signal detector. The signal generator may generate and transmit infrared signals having a wavelength in the range of, for example, 650-1400 nanometers (nm). 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 sense pulse signals and process the sensed signals using Pulse Wave Transit Time (PWTT) in an Artery method. These pulse signals can be combined with data obtained by the piezo vibration sensor 105 to predict blood pressure non-invasively. Infrared/light/laser sensor 106 may also be used to non-surgically determine or obtain data indicative of oxygen saturation levels in the user's blood (SpO ₂ ).

The piezo vibration sensor 105 and accelerometer 121 can be used to measure vibrations of the user's hand. For example, accelerometer 121 can sense changes in direction, velocity, vibrations, and rotations. Temperature sensor 108 may be used to measure the 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 can include active and passive components and resistive and semiconducting materials fabricated within a single package of integrated circuits.

The above sensors may 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 operations may include a user positioning the wearable diagnostic device on the user's left arm 150, one or more belts 104, 109, belt connector 114, belt fastener 115, and outer belt layers. Begin by fixing the wearable diagnostic device to the user's left arm 150 by adjusting 118 and 120 .

When the wearable diagnostic device is fixed on 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 can be transmitted from the infrared/light/laser sensor 106 in the direction of the user's skin within the lower part of the user's left wrist.

The infrared/light/laser sensor 106 can detect the reflection of a signal from the user's skin. A detection signal including 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 created. 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 points in time. Different sets of raw digital data may be accumulated and averaged to yield one raw data set for the user.

In some implementations, the wearable diagnostic device may obtain information related to the user's health. Information associated with the user's health may include, but is not limited to, the user's residential location, 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, one or more past or current medical conditions of the user, such as allergies, which the user is interested in obtaining details of. fields, surgeries, 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 associated with a user's health may be used to create a user profile. The user profile may be stored within the wearable diagnostic device or may be stored in a database or server at a location remote 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, the user's identity is processed so that personally identifiable information about the user cannot be determined, or the user's geographic location, identity, or demographic background is processed so that certain details of the user cannot be determined. can be generalized to an extent. Thus, the user can control what information is collected and what information is used. To implement this, the user may be provided with controls through the wearable diagnostic device that allow the user to choose whether the systems, programs, or features described herein may collect or provide user information. .

In some implementations, a user may subscribe or choose to participate in a service in which the wearable diagnostic device may 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, diagnoses, 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 database that stores the user profile. This server or database can be managed through a subscription service that can collect information from various sources to update a user's profile. The wearable diagnosis device may receive updates on medical information of a user in real time, periodically, or according to a user's request.

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

The features described with respect to FIGS. 1-6 relate to a wearable diagnostic device worn on a user's left arm, but the wearable diagnostic device may have other variations and in some cases may be worn or applied to other parts of the user's body. It will be understood that 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, eg, corresponding to a user command, request, or feedback. can do.

The wearable diagnostic device includes one or more diagnostic tests for obtaining one or more glucose levels, hemoglobin levels, or blood pressure levels associated with a user wearing the wearable diagnostic device based on received or obtained information related to the user's health. may be further configured to execute These processes are further described with reference to FIGS. 7-12 .

Referring to FIGS. 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 is to be performed may include one or more of a selection made by a 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 readings at specific times or after specific time intervals. The wearable diagnostic device may then schedule the days and times at which a particular 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 the scheduled times.

After receiving the indication that a test is to 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 sensors of the determined type (704, 904, 1304). In the case of a glucose or hemoglobin test, the determined type of sensors 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 a sensor may include, but is not limited to, providing increased power to the sensor, preheating the sensor to emit or receive a signal, such as an infrared signal, configuring or calibrating the sensor. 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 a pulse signal, respectively, for a short period of time, for example, 30 seconds, and detect the reflected signal from the user's skin. can In some cases, reflection measurements and current measurements using an impedance sensor may be obtained repeatedly, or may be obtained under different circumstances, such as different pulsed power or magnetic fields. For example, in cholesterol measurements, a first current or infrared signal measurement may be obtained without applying a magnetic field, and then one or more magnetic fields of various low power intensities are generated for a period of time, for example, 10 seconds. Other current or infrared measurements may be obtained.

For glucose and hemoglobin tests, the measured sense signals are processed (processed) and a specific pathway is selected based on the raw data values (708, 908). 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 particular path and its predicted values and gradient regression values. If the raw values are greater than or equal to 500 and less than or equal to 700, path A and its glucose (G) predicted value of 917 and G slope regression value of 0.838 are selected. If the raw values are greater than or equal to 701 and less than or equal to 850, path B and the corresponding G predicted value 919 and G slope regression value 0.838 are selected. If the raw values are greater than or equal to 851 and less than or equal to 950, path C and its G predicted value of 1001 and G slope regression value of 0.838 are selected. If the raw values are greater than or equal to 951 and less than or equal to 1100, path D and its G predicted value of 1004 and G slope regression value of 0.838 are selected. If the raw values are greater than or equal to 1101, path E and its G predicted value 981 and G slope regression value 0.838 are selected.

The obtained prediction 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 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 via a variety of suitable methods. For example, the determined glucose value may be output on a display of the wearable diagnosis device or through 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 its predicted values and gradient regression values. If the raw values are greater than or equal to 500 and less than or equal to 700, path A and the corresponding hemoglobin (Hb) predicted value of 31.5 and Hb slope regression value of 0.114 are selected. If the raw values are greater than or equal to 701 and less than or equal to 850, path B and the corresponding Hb predicted value of 67 and Hb slope regression value of 0.114 are selected. If the raw values are greater than or equal to 851 and less than or equal to 950, path C and the corresponding Hb predicted value of 81 and Hb slope regression value of 0.114 are selected. If the raw values are greater than or equal to 951 and less than or equal to 1100, path D and the corresponding Hb predicted value of 98 and Hb slope regression value of 0.114 are selected. If the raw values are greater than or equal to 1101, path E and its Hb predicted value of 100.5 and Hb slope regression value of 0.114 are selected.

The obtained prediction and gradient regression values can then be used to determine the user's hemoglobin level (912). To determine the hemoglobin level, the wearable diagnostic device 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 914 through various suitable methods. For example, the determined hemoglobin value may be output on a display of the wearable diagnostic device or through a speaker of the wearable diagnostic 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 obtain the raw data values via one or more infrared/light/laser sensors. can do. If a value greater than 500 cannot be obtained even after three attempts, the wearable diagnostic device may output an error message indicating that the current glucose or hemoglobin test cannot be performed.

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 an infrared signal emitted from an infrared/light/laser sensor. Also, this area of the user's skin can be exposed to a magnetic field for a short period of time, for example 10 seconds or 30 seconds, after which the blood particles, except cholesterol molecules, arrange themselves according to the positive and negative poles of the electromagnetic field. do. Data representative of the infrared absorption spectrum before and after applying the magnetic field may be obtained (1306), and then the obtained data may be processed (1308). Processing of the obtained data may include converting the data into a digital signal using an analog-to-digital converter (ADC), and calculating a difference between absorption spectrum values before and after applying a magnetic field to calculate a raw value. .

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 may be created based on testing on multiple subjects as described below.

First, the subject's demographic profile can be recorded. A demographic profile may include various types of descriptive information about a subject, such as the subject's age, gender, ethnicity, color, marital status, and/or medical conditions. For example, a subject may have the demographic profile of a 48 year old Caucasian male, widowed with skin cancer. For each subject, a non-surgical cholesterol test 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 the 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 for a specific time period. Differential values of the measured signals may be calculated and stored as raw values. The raw values are mapped to cholesterol levels obtained from non-surgical cholesterol tests and stored in the subject profile.

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

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

Referring again to FIG. 13 , based on the raw values obtained in operation 1308, a predicted cholesterol level value 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 the 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. If the difference between the predicted cholesterol level and the recently obtained user's cholesterol level is greater than a threshold, eg 3%, the wearable diagnostic device may repeat operations 1302 to 1310 to obtain a new predicted cholesterol level. . This iteration may continue until the predicted cholesterol level value is within a threshold difference of the recently obtained cholesterol level. In some cases, if three iterations are executed without satisfying the above threshold difference, the predicted cholesterol level value obtained in the third iteration 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 example implementations described above, predictive values were used to determine glucose, hemoglobin, or cholesterol levels. Prediction values may be determined using a variety of different methods and based on several factors. A method of obtaining predictive values for cholesterol levels has been described above. A method of obtaining predictive values for glucose and hemoglobin levels is further described with respect to FIGS. 8 and 10 .

Referring to FIG. 8 , the wearable diagnostic device may acquire user profile information to obtain a predicted value for the user's glucose level (802). User profile information may include the user's region of residence, the user's doctor, the user's 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, medical record, one or more past or current medical conditions of the user, such as allergies, surgeries, genetic conditions, one or more health conditions of which the user is interested in obtaining details; concerns, and one or more blood content levels.

Using information from the user's profile, the wearable diagnostic device may determine a clinical correlation information value for glucose (cCIG) for the user based on the user's blood pressure and temperature (804). In some implementations, cCIG can be a matrix of values representing likely blood pressure and temperature. In general, various suitable methods may be used to obtain the user's blood pressure and temperature. In some implementations, blood pressure may be obtained as described herein with reference to FIGS. 11-12 and 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, which includes 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) for the user (806). 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 pathway, such as route A, or if the user profile represents a specific demographic origin or profile of the user, then the cLDG value is the raw glucose value or the specific user profile. It can be determined in a way that corresponds to the demographic origin or profile of the user. In some implementations, if the user agrees to provide personal information, cLDG can be a matrix of values representing various characteristics of the user, such as age, diet, and gender.

After acquiring cCIG and cLDG, the wearable diagnostic device may determine a clinical dataset range based on cCIG and cLDG (810). The determined clinical dataset ranges are mapped to glucose predictive values for the user's glucose level (812). A database storing various clinical dataset ranges and glucose prediction values may be queried using the determined clinical dataset range, and a glucose prediction value mapped to the determined clinical dataset 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 a user's hemoglobin level, the wearable diagnostic device may obtain user profile information (1002). User profile information may include the user's region of residence, the user's doctor, the user's 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, medical record, one or more past or current medical conditions of the user, such as allergies, surgeries, genetic conditions, one or more health conditions of which the user is interested in obtaining details; concerns, and 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) for the user (1004). cCIHb may be determined using clinical information obtained from a user profile, such as a user's medical record, which includes data such as a doctor's report, past or current health conditions, clinical diagnoses, and the like. In some implementations, if the user agrees to provide personal information, cCIHb can 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) for the user by acquiring oxygen saturation and temperature data of the user (1006). In some implementations, cLDHb can be a matrix of values representing the user's likely oxygen saturation and temperature. In general, a variety of suitable methods may be used to obtain the user's oxygen saturation and temperature. In some implementations, oxygen saturation may be obtained as described herein with reference to FIG. 11 and temperature may be obtained using a temperature sensor included in the wearable diagnostic device. In some implementations, cLDHb may be determined based, in part, 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. 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 ranges are mapped to hemoglobin predicted values for the user's hemoglobin level (1012). A database storing various clinical dataset ranges and hemoglobin predicted values may be queried using the determined clinical dataset range, and hemoglobin predicted values mapped to the determined clinical dataset range may be returned. The hemoglobin prediction value may then be provided and used to determine the user's hemoglobin level (1014).

By using predictive values to determine the user's glucose and hemoglobin levels in addition to using blood pressure and temperature data, the determined user's glucose and hemoglobin levels are much higher than methods of determining glucose and hemoglobin levels without using predictive values. have accuracy Predictive values include the user's clinical and demographic information, thus making calculations more accurate that take into account parameters such as skin color and age that can affect glucose or hemoglobin levels. Predictive values can also be used to associate the predictive values with a user's susceptibility to certain diseases, so that the user can take precautionary measures to minimize the likelihood of developing these diseases and improve the user's health and life expectancy. can take them Furthermore, multiple such tests can be performed, and results from these tests can be obtained through a single device as desired 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 for determining a user's blood pressure and oxygen saturation levels is depicted in FIG. 11 . First, the wearable diagnosis device may receive an instruction that blood pressure determination should be performed (1102). The indication that a blood pressure determination is to be performed may include one or more of a request made by one or more processors at a predetermined time and a selection made by the user to provide a blood pressure reading. For example, the wearable diagnostic device may be programmed to provide a blood pressure reading at certain times or after certain time intervals. The wearable diagnostic device may then schedule the days and times at which blood pressure readings should be provided to the user, and initiate the method of determining blood pressure and oxygen saturation levels at the scheduled times.

After receiving the indication that a blood pressure measurement is to be performed (1102), the wearable diagnostic device may determine the type of sensor used for the blood pressure test and activate sensors of the determined type, which in the case of the blood pressure test may include a piezo vibration sensor and an infrared sensor. /light/laser sensor may be included (1104). Activating the sensors may include 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 obtain user measurements (1106). For example, the piezo vibration sensor senses or detects one or more of the movement, position, proximity, speed, and direction of the user's hand, and detects one or more touch, vibration, and shock movements. configured to generate electrical signals corresponding to the The infrared/light/laser sensor is configured to emit an infrared signal and detect reflection of the emitted infrared signal from a user's skin. In some implementations, a preamplifier can 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 converted to analog to digital, for example, to generate an infrared target detection (IRTD) value and a piezo vibration (PV) value. ADC) and filtering operations (1108). In general, various signal processing operations may be performed on signals sensed by the piezo vibration sensor and the infrared/light/laser sensor. In some implementations, operations 1104 - 1108 can be repeated multiple times and average values of multiple IRTD and PV values can be determined.

The above process 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 are averaged 1112 to yield systolic and diastolic pressure values, respectively. The systolic and diastolic pressure values can be used to determine the mean arterial pressure (MAP) using Equation 3 described below.

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 a 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 can be communicated to another electronic device using messages such as email, SMS or MMS. Messages can be created and forwarded automatically without any user input. The user may be prompted to confirm whether the above message with blood pressure and oxygen saturation values should be sent to another electronic device.

In the example implementations described above, the predicted values were used to determine the user's blood pressure. Prediction values may 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 a user's blood pressure, the wearable diagnostic device may obtain user profile information as described in operations 802 and 1002 ( 1202 ). A wearable diagnostic device may obtain 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). 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 current health conditions, clinical diagnoses, and the like. EKG data may be obtained from a variety of suitable sources, such as, for example, the user's medical records.

Next, the EKG data is processed to determine peak values, in particular the time difference between R waves and peak values (1206). Also, the wearable diagnostic device 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 that detect peak values and time differences between sensed peak values in the user's EKG. In some implementations, cLDBP can 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, where the user agrees to provide personal information, cLDBP can be a matrix of values representing various characteristics of the user, such as age, diet, and gender.

After obtaining cCIBP and cLDBP, the wearable diagnostic device may determine a clinical dataset range based on cCIBP and cLDBP (1208). The determined clinical dataset ranges are mapped to blood pressure predictive values for the user's glucose level (1210). A database storing various clinical dataset ranges and blood pressure prediction values may be queried using the determined clinical dataset range, and blood pressure prediction values mapped to the determined clinical dataset range may be returned. The blood pressure prediction value may then be provided and used to determine the user's blood pressure level (1212).

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

When the wearable diagnostic device is fixed to the user's left arm, the first cardiac electrode may be disposed on or inside the bottom surface of the wearable diagnostic device, and may contact the upper side of the user's left wrist. The second cardiac 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 can acquire a signal from the upper part of the user's wrist on the left arm, and the second heart electrode can acquire two signals together with the second heart electrode located on the user's chest. The obtained signals may include cardiac electrical potential waveforms, such as voltages generated during contraction of the heart. Data obtained from the three signals are converted into Provocation, Quality, Radiation, Severity, Time (PQRST) waves. The PQRST waves can be used to generate Einthoven's triangle, EKG plots and compute 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 can execute a process to obtain a body temperature associated with a user wearing the wearable diagnostic device. After the wearable diagnostic device wraps around the user's arm and contacts the user's skin, a temperature sensor in the wearable diagnostic device acquires the user's temperature based on the skin contact. For example, the temperature sensor may touch the wrist and acquire body temperature data of the user over a specific time period. A temperature sensor provides sensor data to one or more processors, converts the received data to a Fahrenheit scale, and averages the temperature data obtained over one or more cycles to determine the user's likely body temperature. can be calculated.

In some implementations, the wearable diagnostic device may execute a process to obtain hand vibration associated with a user wearing the wearable diagnostic device. One or more processors may execute operations in conjunction with a timer and an 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 interval, for example 60 seconds, and locates the accelerometer or piezo vibration sensor. Vibration data may be instructed to be obtained during 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 period, and provide data indicating the number of vibrations to the processor.

A processor may receive data representing the number of oscillations and may determine a frequency of the oscillations. Vibrations can be classified as those associated with Parkinson's tremors if they have a frequency between 3 and 7 hertz (Hz). 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 the vibrations may be stored in a user profile on a storage device and presented to the user via a display.

The above-described processes for obtaining information about a user's glucose levels, hemoglobin levels, blood pressure level, EKG, heart rate levels, body temperature, hand vibrations, and cholesterol levels may occur in parallel, simultaneously, or at different times. can be run in 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 concurrently. For example, EKG tests may 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. A blood pressure test and temperature test may be performed before or during a glucose test. Other variations and combinations are also possible. For example, if a 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 performs tests before, after, or above the most relevant test. During this time, other tests such as a blood pressure test, cholesterol test, or oxygen saturation test may be performed.

In some implementations, a user may enter a request to perform one type of diagnostic test, eg a glucose test, but the wearable diagnostic device may provide one or more tests, eg, to be performed along with the diagnostic test requested by the user. For example, a blood pressure test can be determined. One such as tests that are frequently selected to be performed simultaneously, sequentially, or in pairs, e.g. by the user, by default settings, by manufacturer settings, or by physician recommendation. Or, the additional tests may be determined based on more criteria. In some implementations, additional tests may 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 processes described above may be stored in the memory of the wearable diagnostic device or stored 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 provides a visual, acoustic, Alternatively, an electronic alarm may be output. If, for example, the EKG contains signs of abnormal heart movement or the body temperature is above 102 degrees Fahrenheit, for example, the wearable diagnostic device may generate audio output, such as sonic waves (sound waves), recommending going to the doctor.

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

A computer program (also known as a program, software, software application, script, or code) may be written in any form of programming language, including compiled or interpreted languages, and may be a standalone program or computing environment. may 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 a file system. A program may be part of a file holding 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 (eg, files that store one or more modules, sub programs, or portions of code). A computer program may be executed on one computer or on multiple computers located at one site or distributed across several sites and interconnected by a communication network.

The processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform actions by operating on input data and generating output. Also, processes and logic flows may be performed by and implemented by a special purpose logic circuit such as a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC). can

Processors in computers that execute computer programs include, 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 will receive instructions and data from read only memory or random access memory or both.

Although this specification contains many details, they should not be construed as limitations on the scope of rights or claims of the present disclosure, but rather as descriptions of features specific to particular embodiments. Certain features that are described in this specification within the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Moreover, although features have been described above as acting in particular 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 It can correspond to variations of combinations or subcombinations.

It should 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 include A, B, or both A and B. Similarly, at least one of the phrases A and B includes A, B, or both A and B.

Specific implementation examples are described here. Other examples of implementation are within the scope of the following claims. For example, the actions (actions) recited in the claims can be performed in a different order and still achieve desired results.

Claims (20)

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; Actions are:
receiving an input corresponding to a request for initiating a non-surgical diagnostic test to detect a medical condition of a user;
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 via the one or more sensors;
obtaining a predictive value for the non-surgical diagnostic test based in part on a user profile;
determining a test result based on the prediction value and the received signal data; and
Including outputting the test result through a display or speaker,
The one or more computer devices include a wearable diagnostic device providing information about the non-surgical diagnostic test,
wherein the one or more sensors include a pair of detachable first and second wireless cardiac electrodes;
The first wireless cardiac electrode is disposed on an outer belt layer of the wearable diagnostic device, and the second wireless cardiac electrode is disposed on a rear cover of the wearable diagnostic device to directly contact the user's skin. According to claim 1,
wherein the non-surgical diagnostic test comprises one or more of a glucose test, a cholesterol test, a hemoglobin test, an oxygen saturation level test, and an electrocardiogram monitoring test. According to claim 1,
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;
and determining the test result based on the predicted value and the received signal data comprises determining the test result based on the determined values. According to claim 1,
The above actions are:
obtaining user clinical data and user demographic data from one or more databases;
determining a clinical dataset range based on the obtained user clinical data and the user demographic data; and
The system further comprising mapping the clinical dataset ranges to the predictive values for the non-surgical diagnostic test. According to claim 1,
The above actions are:
Determine a second non-surgical diagnostic test comprising: i) a user pattern associating the second non-surgical diagnostic test with the non-surgical diagnostic test, and ii) a medical record of the user performed based on;
activating a second set of one or more sensors based on the second non-surgical diagnostic test;
receiving second signal data via the second set of one or more sensors;
obtaining a second predictive 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. According to claim 5,
The system of claim 1 , wherein obtaining the predictive value for the non-surgical diagnostic test comprises determining the predictive value for the non-surgical diagnostic test using the second test result. According to claim 5,
The system of claim 1 , wherein the second non-surgical diagnostic test is a cholesterol test performed simultaneously with the non-surgical diagnostic test, which is a glucose test. According to claim 5,
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. According to claim 1,
The system of claim 1 , wherein the one or more sensors further include one or more of a piezo vibration sensor, an infrared sensor, a temperature sensor, an accelerometer, and a micro-electro mechanical system (MEMS). receiving, by one or more sensors, an input corresponding to a request for starting a non-surgical diagnostic test and detecting a medical condition of a user;
identifying, by one or more computer devices, the one or more sensors for performing the non-surgical diagnostic test;
activating, by the one or more computer devices, 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 predictive 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
outputting the test result by the one or more computer devices through a display or a speaker;
The one or more computer devices include a wearable diagnostic device providing information about the non-surgical diagnostic test,
wherein the one or more sensors include a pair of detachable first and second wireless cardiac electrodes;
The first wireless cardiac electrode is disposed on an outer belt layer of the wearable diagnostic device, and the second wireless cardiac electrode is disposed on a rear cover of the wearable diagnostic device to directly contact the user's skin. . According to claim 10,
The computer implemented method of claim 1 , wherein the non-surgical diagnostic test comprises one or more of a glucose test, a cholesterol test, a hemoglobin test, an oxygen saturation level test, and an electrocardiogram monitoring test. According to 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;
wherein determining the test result based on the predicted value and the received signal data comprises determining the test result based on the determined values. According to claim 10,
obtaining user clinical data and user demographic data from one or more databases;
determining a clinical dataset range based on the user clinical data and the user demographic data; and
The computer-implemented method further comprising mapping the clinical dataset ranges to the predictive values for the non-surgical diagnostic test. According to claim 10,
Determine a second non-surgical diagnostic test comprising: i) a user pattern associating the second non-surgical diagnostic test with the non-surgical diagnostic test, and ii) a medical record of the user Performed based on, step;
activating a second set of one or more sensors based on the second non-surgical diagnostic test;
receiving second signal data via the second set of one or more sensors;
obtaining a second predictive 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 prediction value and the received second signal data. 15. The method of claim 14,
The computer implemented method of claim 1 , wherein obtaining the predictive value for the non-surgical diagnostic test comprises determining the predictive value for the non-surgical diagnostic test using the second test result. 15. The method of claim 14,
The second non-surgical diagnostic test is a cholesterol test performed simultaneously with the non-surgical diagnostic test, which is a glucose test, a computer-implemented method. 15. The method of claim 14,
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. According to claim 10,
wherein the one or more sensors include one or more of a wireless cardiac electrode, a piezo vibration sensor, an infrared sensor, a temperature sensor, an accelerometer, and a MEMS. a wearable diagnostic watch and one or more storage devices storing instructions that, when executed by the wearable diagnostic watch, cause the wearable diagnostic watch to perform operations, the operations comprising:
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 via the one or more sensors;
obtaining a first predictive 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 prediction value and the received signal data; and
Outputting the glucose test result and the cholesterol test result through a display or speaker of the wearable diagnostic watch,
wherein the one or more sensors include a pair of detachable first and second wireless cardiac electrodes;
The first wireless heart electrode is disposed on an outer belt layer of the wearable diagnostic watch, and the second wireless heart electrode is disposed on a rear cover of the wearable diagnostic watch to directly contact a user's skin. According to claim 19,
wherein 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;
Wherein determining the glucose test result based on the first prediction value and the received signal data comprises determining the glucose test result based on the determined values.