JP2021516833A - Wearable diagnostic device - Google Patents

Abstract

A wearable diagnostic device worn on the user's body that can provide the user with various types of health-related information will be described. The wearable diagnostic device can provide real-time, non-invasive, accurate continuous data on the user's heart rate, hemoglobin level, body temperature, oxygen level, glucose level, and blood pressure. In some practices, the wearable diagnostic device can also provide electrocardiography (EKG) data or detection of Parkinson's disease symptoms such as involuntary movements of the user's hand. The user can configure the wearable diagnostic device to provide information about one, more, or all of the above health-related information. [Selection diagram] Fig. 6

Description

(background)
As individuals become more and more health conscious, there is an increasing demand among users for receiving personal health information in a simple and convenient way. In many cases, the user may need to wear multiple devices, each providing information about one aspect of the user's health. There is a need for equipment that can provide information on multiple aspects of a user's health in a simple, non-invasive, and convenient way.

(Overview)
In general, an innovative aspect of the subject describes a wearable diagnostic device and method and system for performing one or more medical diagnostic tests.

In some implementations, the system stores one or more computer devices and instructions that cause the one or more computer devices to perform an operation when executed by the one or more computer devices. Or includes a plurality of storage devices. The action is to receive an input corresponding to a request to start a non-invasive diagnostic test to detect the user's condition, to identify one or more sensors to perform the non-invasive diagnostic test, non-invasive. Activating the identified one or more sensors based on the diagnostic test, receiving signal data through the one or more sensors, the non-invasive diagnostic test based in part on the user profile. The present invention includes obtaining the predicted value of the above, determining the inspection result based on the predicted value and the received signal data, and outputting the inspection result via a display or a speaker.

Each implementation may optionally include one or more of the following functions: For example, in some practices, non-invasive diagnostic tests include one or more of glucose testing, cholesterol testing, hemoglobin testing, oxygen saturation level testing, and ECG monitoring testing.

In some practices, the operation further comprises selecting a route based on the raw data obtained from the received signal data, and obtaining a set of values determined based on the selected route. Determining the test result based on the predicted value and the received signal data includes determining the test result based on the determined value.

In some practices, the behavior is to obtain the user's clinical data and the user's demographic data from one or more databases, a clinical dataset based on the obtained user's clinical data and the user's demographic data. Further include determining the extent of the clinical data set and mapping the extent of the clinical dataset to the predicted values of non-invasive diagnostic tests.

In some practices, the action involves a second non-invasive diagnostic test to be performed, (i) a user pattern that associates the second non-invasive diagnostic test with a non-invasive diagnostic test, and (ii) a user. To determine based on the medical history of the patient; to activate a second set of one or more sensors based on the second non-invasive diagnostic test; to activate the second set of the one or more sensors. To receive the second signal data through; to obtain the second predicted value of the second non-invasive diagnostic test based in part based on the user profile; and the second predicted value and the received second. It further includes determining the second test result based on the signal data of 2.

In some practices, obtaining a predictive value for a non-invasive diagnostic test involves using a second test result to determine a predictive value for a non-invasive diagnostic test.

In some practices, the second non-invasive diagnostic test is a cholesterol test that is performed at the same time as the non-invasive diagnostic test, which is a glucose test.

In some practices, the non-invasive diagnostic test and the second non-invasive diagnostic test are performed by a clock with one or more computer devices.

In some implementations, one or more sensors include one or more wireless core electrodes, piezoelectric vibration sensors, infrared sensors, temperature sensors, accelerometers, and microelectromechanical systems (MEMS) sensors.

According to aspects of the disclosed subject matter, the clock stores one or more computer devices and instructions that cause the one or more computer devices to perform an operation when executed by the one or more computer devices. It is provided with one or more storage devices. This action is to select a glucose test and a cholesterol test based on the user profile, identify one or more sensors to perform the glucose test and the cholesterol test, the identified one or more. Activating sensors, receiving signal data through the one or more sensors, obtaining a first predicted value of the glucose test based in part on the user profile, said first predicted value. The glucose test result and the cholesterol test result are determined based on the received signal data, and the glucose test result and the cholesterol test result are output via the display or the speaker of the clock.

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

The above aspects and practices further described herein offer several advantages. For example, a single wearable diagnostic device can perform multiple non-invasive diagnostic tests, including a non-invasive glucose test, a non-invasive cholesterol test, and a non-invasive hemoglobin test. Wearable diagnostic devices can also obtain electrocardiographic (EKG) data or detection of Parkinson's disease symptoms such as involuntary movements of the user's hands. A wearable diagnostic device equipped with wireless electrodes can record the user's history and medical condition, and utilizes predictive values, algorithms, and mapping databases to ensure the accuracy and reliability of glucose, cholesterol, and / or hemoglobin tests. It is possible to provide highly sexual results. Since the predicted value includes the user's clinical information and demographic information, the calculation can be performed by more accurately considering parameters such as skin color and age that can affect glucose level or hemoglobin level. Predicted values can also be used to correlate the predicted values with the user's susceptibility to a particular disease.

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

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

(A brief description of the drawing)
1-5 show an exemplary implementation of a wearable diagnostic device.

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

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

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

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

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

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

FIG. 12 shows a flowchart of an exemplary method for determining a predicted blood pressure value.

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

Similar reference numbers and names in various drawings indicate similar elements.

(Detailed explanation)
The present disclosure generally relates to a wearable diagnostic device that can be worn on the user's body to provide the user with various types of health-related information. In some practices, the wearable diagnostic device performs one or more medical diagnostic tests and is real-time, non-invasive and accurate about the user's heart rate, hemoglobin level, temperature, oxygen level, glucose level, cholesterol, and blood pressure. Continuous data can be provided. In some practices, wearable diagnostic devices can also provide electrocardiographic (EKG) data and detection of Parkinson's disease symptoms such as involuntary movements of the user's hands. The user can configure the wearable diagnostic device to provide information about one or more of the diagnostic tests described above. Implementation of the wearable diagnostic device will be described below with reference to the figures.

1 to 5 show different diagrams of the wearable diagnostic device. In some embodiments, the wearable diagnostic device can be implemented in the form of a watch as shown in FIGS. 1-5. FIG. 6 shows further details of the components included in the wearable diagnostic device with some implementations. In general, wearable diagnostic devices can be implemented in a variety of suitable shapes, forms, and sizes, and can be attached to the user's left arm to obtain multiple health diagnostic measurements for the user. It can be a device.

With reference to FIGS. 1-5, the wearable watch has a casing 101, a display 102, a removable wireless core electrode 103, 113, a belt 104, 109, a piezoelectric vibration sensor 105, an infrared (IR) / light / laser sensor 106, Connector 107, temperature sensor 108, control buttons 110, 111, back cover 112, belt connector 114, belt fastener 115, charging connector 116, power supply 117, outer belt layer 118, 120, insulated wire 119, accelerometer 121, power manager 122 , System-on-chip (SoC) 123, and power switch 124. The wearable watch can be worn on the user's left arm 150.

The casing 101 includes a display 102, charging connectors 116, control buttons 110, 111, and one or more electronic components such as a processor, printed circuit board (PCB), integrated circuit (IC), SoC 123, memory, and wireless transceiver. Or can be combined with these. The display 102 can be implemented using any suitable display, including, for example, a liquid crystal display (LCD), a light emitting diode (LED) display, or an organic LED display to display various data. In some embodiments, the display 102 can be a touch screen, such as a capacitive touch screen.

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

One or more processors in casing 101 can be implemented on a PCB or IC and can be electrically connected to other electronic components of the wearable diagnostic device such as memory, display 102, and wireless transceivers. For example, the processor can receive data indicating a user's selection from display 102, determines the type of information requested by the user, and performs one or more actions based on the information requested by the user. You can generate a command to execute.

In some practices, one or more processors may use wireless transceivers to send and receive data. Data can be sent and received between the wearable diagnostic device and the secondary device. The secondary device can be a device selected by the user, a network server, or a device configured to communicate with a wearable watch. For example, the user can select another device he owns and send data to it from the wearable diagnostic device. In another example, the wearable diagnostic device can be configured to send and receive data to and from one or more network servers according to a user request or a predetermined schedule of send and receive data.

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

Databases in the network can include cloud databases or databases managed by a database management system (DBMS). The DBMS can be implemented as an engine that controls the organization, storage, management, and retrieval of data in the database. DBMSs often have the ability to query, back up and duplicate, apply rules, provide security, perform calculations, modify and access logs, and optimize. Provides the ability to automate. The DBMS typically includes a modeling language, a data structure, a database query language, and a transaction mechanism. A modeling language is used to define the schema of each database in the DBMS according to the database model, which includes hierarchical models, network models, relational models, object models, or other applicable known or useful mechanisms. Can be included. Data structures can include fields, records, files, objects, and other applicable known or convenient structures for storing data. The DBMS can also contain metadata about the data to be stored.

In some practices, the database may include a user database that can store information about the user, such as alias names, medical history, and any medical condition of the user. User databases can also store data related to licenses, permits, and certificates for accessing various tools and software. In some cases, the user may choose to anonymize user data so that any information that can identify the user is anonymized and the user identification information is deleted before storing the data in the user database. ..

In general, various suitable wireless protocols can be used to communicate data with wearable diagnostic devices. For example, a wearable diagnostic device can communicate with one or more networks, devices, or servers using WiFi or Bluetooth communication. In general, it is possible to communicate on various types of networks and various communication protocols can be used.

The charging connector 116 can be a port configured to connect to a power cable such as a universal serial bus or conductor. When connected to the power cable, the charging connector 116 can act as a power interface to supply power from an external source to charge the power 117 of the wearable watch. The power supply 117 can be any suitable battery and can power any electrical component in the wearable diagnostic device.

Casing 101 also includes a power switch 124. In some implementations, one or more processors send commands to the power manager 122, depending on the choice of the power switch 124 such that the power switch 124 is in the power off position, for wearable diagnostics such as display 102. The power supply to one or more parts of the device can be stopped. In some implementations, depending on the choice of the power switch 124 such that the power switch 124 is in the power on position, the power manager 122 sends a command to the power 117 to send a command to one of the wearable diagnostic devices such as the display 102. Power can be supplied to one or more components.

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

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

The wearable diagnostic device shall also include one or more sensors such as core electrodes 103, 113, piezoelectric vibration sensor 105, infrared (IR) / light / laser sensor 106, temperature sensor 108, accelerometer 121, and impedance sensor. Can be done. In some embodiments, the piezoelectric vibration sensor 105 may include a microelectromechanical system (MEMS) sensor.

The radiocardiac electrodes 103, 113 may include a metal conductive material configured to detect a cardiac potential waveform such as a voltage generated during the contraction of the heart. The radiocentric electrode 103 is arranged on the outer belt layer 118 corresponding to the outer circumference of the wearable diagnostic device. The wireless heart electrode 113 is disposed of or integrated with the back cover 112 so that the wearable diagnostic device can come into contact with the user's skin when anchored around the user's arm 150. The wireless core electrodes 103, 113 can be removed from the wearable diagnostic device and reattached, and data can be wirelessly communicated to another electronic device.

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

In some practices, the IR / light / laser sensor 106 can detect the pulse signal and process the signal detected using the pulse wave velocity (PWTT) in the artery method. These pulse signals can be combined with the data obtained by the piezoelectric vibration sensor 105 to non-invasively predict blood pressure. The IR / light / laser sensor 106 can also be used to non-invasively determine or obtain data indicating _{the oxygen saturation level (SpO 2) in the user's blood.}

The piezoelectric vibration sensor 105 and the accelerometer 121 can be used to measure the vibration of the user's hand. For example, accelerometer 121 can detect changes in direction, velocity, vibration, and rotation. The temperature sensor 108 can be used to measure the user's body temperature. The temperature sensor 108 can be implemented in a variety of suitable ways. For example, the temperature sensor 108 can be a thermocouple, a silicon bandgap sensor, a thermometer, or a thermistor that includes one or more detection resistors. Changes in electrical resistance in the detection resistor can correspond to changes in body temperature. In some implementations, the temperature sensor may include active and passive elements manufactured in a single package of ICs as well as resistors and semiconductor materials.

As described in more detail below, the sensor can be used to obtain one or more measurements. The actions performed using a wearable diagnostic device to determine glucose levels, hemoglobin levels, blood pressure levels, EKG, heart rate levels, body temperature, and hand vibration are described in more detail below. In this operation, the user places a wearable diagnostic device on the user's left arm 150 and adjusts one or more of the belts 104, 109, belt connectors 114, belt fasteners 115, and outer belt layers 118, 120. The wearable diagnostic device is fixed around the user's left arm 150 before starting.

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

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

In some practices, multiple signal measurements can be obtained so that multiple sets of raw digital data can be obtained at different time points. Various sets of raw digital data can be accumulated and averaged to obtain a single set of raw digital data for the user.

In some practices, the wearable diagnostic device can obtain information related to the user's health. Information related to the user's health is not limited to, for example, the user's place of residence, the user's doctor, the user's pharmacist, the user's medical record holder, and the user's with one or more groups. Demographics, user's diet, indication of the number of children of the user, medical history of the user, one or more past or present medical conditions such as allergies of the user, surgery, genetic status, one or more of the users It may include multiple health concerns and one or more blood content levels for which the user is interested in obtaining details. For example, a user can indicate that he or she is particularly interested in his or her glucose or blood pressure level. Users can also indicate how often they want to get information about their glucose or blood pressure levels.

Information related to the user's health can be used to create a user profile. The user profile can be stored locally in the wearable diagnostic device or in a database or server located away from the wearable diagnostic device. In some practices, personally identifiable information is deleted because the user profile data can be processed in one or more ways before it is stored or used. For example, to generalize the user's geographic location, personal information, or demographic background to the extent that the user desires so that the user's personally identifiable information cannot be identified or the user's specific details cannot be identified. The personal information of the user can be processed. Therefore, the user can control what information is collected and how that information is used. To accomplish this, a wearable diagnostic device that allows the user to choose whether the systems, programs, or functions described herein can collect or provide user information, or when it can collect or provide it. It is possible to provide the user with control via.

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

In some practices, the user can use the wearable diagnostic device's user interface to directly enter information about the user's health and background. The entered user medical information can then be stored remotely or in a wearable diagnostic device.

Although the features described in relation to FIGS. 1-6 relate to a wearable diagnostic device worn on the user's left arm, the wearable diagnostic device can have various other forms and, in some cases, the wearable diagnostic device. It should be understood that it can be worn or applied to other parts of the user's body. In addition, one or more additional components may be included or combined in the wearable diagnostic device. For example, in some implementations, a wearable diagnostic device uses a speaker to output information using sound waves and one or more microphones to receive voice input corresponding to, for example, a user's command, request, or feedback. Can be prepared.

In some practices, the wearable diagnostic device is one of glucose, hemoglobin, or blood pressure levels for the user wearing the wearable diagnostic device, based on the information received or acquired related to the user's health. It can be further configured to perform one or more diagnostic tests to obtain one or more. These processes will be further described in FIGS. 7-10.

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

After receiving the instruction that the test should be performed (702, 902, 1302), one or more processors of the wearable diagnostic device determine the type of sensor used for the test and the determined type of sensor. The sensor may include one or more IR / light / laser sensors in the case of glucose or hemoglobin testing (704, 904). For cholesterol testing, the sensors activated may include one or more of IR / light / laser sensors, impedance sensors, and electromagnetic sensors. Activating a sensor is, but is not limited to, increasing power to the sensor, warming up the sensor to transmit or receive signals such as IR signals, or configuring or calibrating the sensor. Actions including that can be included.

One or more sensors activated can obtain measurements that characterize the user (706, 906, 1306). For example, one or more IR / light / laser sensors may each be configured to emit an infrared or pulsed signal for a short period of time, eg, 30 seconds, to detect reflections of the transmitted signal from the user's skin. Can be done. In some cases, reflection and current measurements using impedance sensors may be obtained repeatedly or under different environments such as different pulse powers or magnetic fields. For example, in the case of cholesterol measurement, the first current or infrared signal measurement can be obtained without applying a magnetic field, and one or more other current or infrared measurements can be made with various low power intensity magnetic fields for a period of time. For example, it can be done after being generated for 10 seconds.

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

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

Table I

The predicted and gradient regression values obtained can then be used to determine the user's glucose level (712). To determine glucose levels, wearable diagnostics apply generalizability theory and G-gradient regression to a set of raw data and use [Equation 1] to indicate glucose levels as described below. Can be determined.
Glucose value = predicted value ( _{G-clinical sampling regression} )-(G regression gradient) x (raw data) [Equation 1]

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

When the hemoglobin test is being performed, exemplary mappings such as those shown in Table II can be used to select specific pathways and corresponding predictive and gradient regression values. If the raw value is between 500 and 700, Path A and the corresponding 31.5 hemoglobin (Hb) predicted value and 0.114 Hb gradient regression value are selected. If the raw value is between 701 and 850, Path B and the corresponding 67 Hb predictions and 0.114 Hb gradient regression values are selected. If the raw value is between 851 and 950, Path C and the corresponding 81 Hb predictions and 0.114 Hb gradient regression values are selected. If the raw value is between 951 and 1100, Path D and the corresponding 98 Hb predictions and 0.114 Hb gradient regression values are selected. If the raw value is greater than or equal to 1101, path E and the corresponding Hb predicted value of 100.5 and Hb gradient regression value of 0.114 are selected.

Table II

The resulting predicted 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 can apply Hb gradient regression to the set of raw data and use [Equation 2] to determine the value indicating the hemoglobin level as described below. ..
Hb value = ((Hb regression gradient) x (raw data))-Predicted value ( _{Hb-clinical sampling regression} ) [Equation 2]

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

If the raw data value is less than 500 in the glucose or hemoglobin test, the wearable diagnostic device determines that the value is incorrect and another to obtain the raw data value by one or more IR / optical / laser sensors. Attempts can be initiated. If a value greater than 500 cannot be obtained after three attempts, the wearable diagnostic device can output an error message indicating to the user that a glucose or hemoglobin test cannot be performed at this time.

When a cholesterol test is being performed to determine cholesterol levels (eg, HDL, LDL, triglyceride molecules) for a user wearing a wearable diagnostic device, the lower part of the user's left wrist is IR / light. / Exposed to infrared signals transmitted from laser sensors. This area of the user's skin can also be exposed to a magnetic field for a short period of time, eg, 10 or 30 seconds, after which the blood particles, excluding cholesterol molecules, place themselves according to the positive and negative polariton. .. Before and after applying the magnetic field, data showing the infrared absorption spectrum can be obtained (1306), and the obtained data can be subsequently processed (1308). To process the resulting data, an analog-to-digital converter (ADC) is used to convert the data into a digital signal, and the difference between the absorption spectrum values before and after applying a magnetic field is calculated to obtain the raw value. To get can be included.

The predicted cholesterol level value is then obtained from the database (1310). The database can contain mappings of cholesterol levels to raw values and can be organized according to demographic classes. The database can be built on the basis of examinations of multiple human subjects, as described below.

First, you can record the demographic profile of the subject. The demographic profile may include various types of descriptive information about the subject, such as age, gender, ethnicity, color, marital status, and / or medical condition. For example, a subject may have a demographic profile of a 48-year-old Caucasian male, widows, and skin cancer. Invasive cholesterol testing can be performed on each subject using a variety of suitable methods, and the test results can be added to the subject's profile.

Next, as described above, the current and infrared signal measurement values of interest can be obtained using the resistance sensor and the infrared sensor, respectively. The current and infrared signals can be measured before the magnetic field is applied and after the magnetic field is applied for a certain period of time. The derivative of the measured signal can be calculated and saved as a raw value. Raw values are mapped to cholesterol levels obtained from an invasive cholesterol test and stored in the subject's profile.

Hundreds of subjects can use a large sample size to build a database that maps raw values to cholesterol levels according to one or more demographic classes (eg, gender, ethnicity, age, etc.). And thousands of subjects can be inspected. In some practices, statistical data can be extracted from the test so that the mean, median, and mode of cholesterol levels and raw values can be determined for each demographic class.

In general, various types of demographic classes can be formed. For example, in some cases, demographic classes can be limited to people of age, such as those in their 40s. In some cases, a demographic class may include multiple demographic characteristics such as age and ethnicity, or age and ethnicity and gender. Therefore, the database may include a mapping table that maps estimated cholesterol levels based on raw values associated with a particular demographic class. It should be noted here that the database can be constructed anonymously so that the personal information of the subject is not revealed or known.

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

The predicted cholesterol level value can then be determined to be the estimated cholesterol level of the user wearing the wearable diagnostic device (1312). In some practices, predicted cholesterol levels can be compared to previously obtained user cholesterol levels. If the difference between the predicted cholesterol level and the user's last obtained cholesterol level is greater than a threshold, eg 3%, the wearable diagnostic device repeats actions 1302 to 1310 to obtain a new predicted cholesterol level. Can be done. Such repetition can be continued until the predicted cholesterol level value is within the difference between the user's last obtained cholesterol level thresholds. In some cases, if three iterations are performed without satisfying the threshold difference, the predicted cholesterol level value obtained in the third iteration is determined as the estimated cholesterol level of the user wearing the wearable diagnostic device. can do. The user's determined estimated cholesterol level can then be displayed by the display 102 of the wearable diagnostic device.

In the above exemplary practices, predicted values were used to determine glucose, hemoglobin, or cholesterol levels. Predictions can be determined based on several factors using a variety of different methods. Methods for obtaining predicted cholesterol levels are described above. Methods for obtaining predicted glucose and hemoglobin levels will be further described in connection with FIGS. 8 and 10.

With reference to FIG. 8, the wearable diagnostic device can obtain user profile information in order to obtain a predicted value of the user's glucose level (802). User profile information includes the user's place of residence, the user's doctor, the user's pharmacist, the user's medical record holder, the user's demographics with one or more groups, the user's diet, the user's children's An indication of the number, the user's medical history, one or more past or present medical conditions, such as the user's allergies, surgery, genetic status, one or more of the user's health concerns, and the user's getting details. May include one or more blood content levels of interest to.

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

The wearable diagnostic device can then determine the clinically relevant demographic value (cLDG) of the user's glucose (806). The cLDG can be determined in part based on the characteristics of one or more users, such as the demographics of the user, the age of the user, and other personal characteristics of the user. As an example, if the raw glucose value described above is associated with a particular pathway, such as Path A, or if the user profile indicates a particular demographic origin or profile of the user, then the cLDG value is the cLDG value is the raw glucose value. Alternatively, it can be determined to correspond to the user's specific demographic origin or profile. In some practices, the cLDG can be a matrix of values representing various characteristics, such as the user's age, diet, and gender, if the user agrees to provide the following personal information.

After obtaining the cCIG and cLDG, the wearable diagnostic device can determine the scope of the clinical dataset based on the cCIG and cLDG (810). The range of determined clinical datasets is mapped to glucose predictions of the user's glucose levels (812). A database that stores a range of various clinical datasets and glucose predictors can query the range of determined clinical datasets and provides glucose predictors that map to the range of the determined clinical dataset. Can be returned. Glucose predictions can then be provided and the glucose predictions can be used to determine the user's glucose level (814).

With reference to FIG. 10, the wearable diagnostic device can obtain user profile information in order to obtain a predicted value of the user's hemoglobin level (1002). User profile information includes the user's place of residence, the user's doctor, the user's pharmacist, the user's medical record holder, the user's demographics with one or more groups, the user's diet, the user's children's An indication of the number, the user's medical history, one or more past or present medical conditions, such as the user's allergies, surgery, genetic status, one or more of the user's health concerns, and the user's getting details. May include one or more blood content levels of interest to.

Using information from the user's profile, the wearable diagnostic device can determine the clinical correlation information value (cCIHb) of the user's hemoglobin (1004). cCIHb can be determined using clinical information obtained from a user profile such as a physician's report, past or present medical condition, user's medical history including data such as clinical diagnosis. In some practices, cCIHb can be a matrix of values that represent various aspects of a user's medical history if the user agrees to provide the following personal information.

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

After obtaining the cCIHb and cLDHb, the wearable diagnostic device can determine the scope of the clinical dataset based on the cCIHb and cLDHb (1010). The range of clinical datasets determined is mapped to hemoglobin predictions at the user's hemoglobin level (1012). Databases that store various clinical dataset ranges and hemoglobin predictions can query the determined clinical dataset range and map hemoglobin predictions to the determined clinical dataset range. Can be returned. Hemoglobin predictions can then be provided and the hemoglobin predictions can be used to determine a user's hemoglobin level (1014).

By determining the user's glucose level and hemoglobin level using the predicted value in addition to utilizing the blood pressure data and the body temperature data, the user's determined glucose level and the hemoglobin level do not utilize the predicted value. It has much higher accuracy compared to methods for determining glucose and hemoglobin levels. Predictions include user clinical and demographic information, so calculations can be made with more accurate consideration of parameters such as skin color and age that can affect glucose or hemoglobin levels. it can. Predictive values can also be used to associate the predicted values with the user's susceptibility to a particular disease, thereby allowing the user to take precautionary measures to minimize the probability of contracting these diseases. Can improve health and life expectancy. Further, these plurality of tests can be performed, and the test results can be obtained by a single device on demand, providing a high degree of convenience to the user.

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

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

Activated piezoelectric vibration sensors and IR / light / laser sensors can obtain user measurements (1106). For example, a piezoelectric vibration sensor senses or detects one or more of the user's hand movements, positions, approaches, velocities, and directions to include one or more of the detected contact, vibration, and impact movements. It is configured to generate the corresponding electrical signal. The IR / light / laser sensor is configured to emit an infrared signal and detect the reflection of the transmitted infrared signal from the user's skin. In some practices, a preamplifier can be used to amplify the weak pulse signal obtained by the piezoelectric vibration sensor.

Signals detected by piezoelectric vibration sensors and IR / light / laser sensors are processed, for example, by performing analog-to-digital conversion (ADC) and filtering operations to produce infrared target detection (IRTD) values and piezoelectric vibration (PV). Values can be generated (1108). In general, various signal processing operations can be performed on the signals detected by the piezoelectric vibration sensor and the IR / light / laser sensor. In some practices, operations 1104-1108 can be repeated multiple times to determine the average of multiple IRTD and PV values.

The process may also include comparing the determined IRTD and PV values with the predicted IRTD and PV values. This comparison produces two sets of blood pressure values. The systolic and diastolic blood pressure values are obtained by averaging each of the two sets of blood pressure values (1112). Using the systolic and diastolic blood pressure values, the mean arterial pressure (MAP) can be determined using [Equation 3] below.
MAP = diastolic blood pressure + (1/3) (systolic blood pressure-diastolic blood pressure) [Equation 3]

The generation of predicted values for blood pressure measurements will be described below with reference to FIG. In some practices, the oxygen saturation level of the user's blood can also be determined using [Equation 4] (1110).
Oxygen saturation level = ((C _HbO2 ) / (C _HbO2 + C _Hb )) × 100 [Equation 4]

In [Equation 4], C _HbO2 is equal to the concentration of oxygenated hemoglobin, and C _Hb is equal to the concentration of oxygenated hemoglobin. _{The values of C Hb O2} and C Hb can be obtained by using an infrared sensor.

After actions 1110 and 1112, blood pressure and oxygen saturation values are output (1114). For example, in some practices, blood pressure and oxygen saturation values are displayed on the display. In some practices, blood pressure and oxygen saturation values are output from the audio speaker. In some practices, blood pressure and oxygen saturation levels can be communicated to another electronic device using messages such as email, SMS, or MMS. Messages can be automatically generated and added without user input. The user may be instructed to see if a message containing blood pressure and oxygen saturation values needs to be sent to other electronic devices.

In the above exemplary implementation, predicted values were used to determine a user's blood pressure. Predictions can be determined based on several factors using a variety of different methods. With reference to FIG. 12, in order to obtain a predicted value of the user's blood pressure, the wearable diagnostic device can obtain user profile information as described in Operations 802 and 1002 (1202). The wearable diagnostic device can obtain the user's clinical information and demographic information from the user profile.

Using the information from the user's profile, the wearable diagnostic device can determine the clinical correlation information value (cCIBP) of the user's blood pressure and obtain EKG data (1204). cCIBP can be determined using clinical information obtained from user profiles such as a physician's report, past or present medical condition, user's medical history including data such as clinical diagnosis. EKG data can be obtained from a variety of suitable sources, such as the user's medical history.

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

After obtaining the cCIBP and cLDBP, the wearable diagnostic device can determine the scope of the clinical dataset based on the cCIBP and cLDBP (1208). The range of determined clinical datasets is mapped to blood pressure predictions for the user's glucose levels (1210). A database that stores a range of various clinical datasets and blood pressure predictions can query the range of determined clinical datasets and provides blood pressure predictions that map to the range of the determined clinical dataset. Can be returned. Blood pressure predictions can then be provided and the blood pressure predictions can be used to determine a user's blood pressure level (1212).

In some practices, the wearable diagnostic device can perform a process for obtaining the EKG and heart rate levels of the user wearing the wearable diagnostic device.

When the wearable diagnostic device is fixed to the user's left arm, the first electrocardiogram can be placed on or inside the bottom surface of the wearable diagnostic device and can be brought into contact with the upper side of the user's left wrist. The second electrocardiogram is placed on or inside the wearable diagnostic device without contacting the skin of the user's left arm. The first heart electrode can get a signal from the upper part of the wrist of the user's left arm, and the second heart electrode gets two signals with the second heart electrode placed on the user's chest. be able to. The resulting signal may include an electrocardiographic waveform such as a voltage generated during the contraction of the heart. The data obtained from the three signals is converted into a PQRST (Provocation, Quality, Radiation, Severity, Time) wave. PQRST waves can be used to generate Eindhoven triangles, EKG plots and calculate additional information such as the user's heart rate. In some cases, PQRST waves can be used to diagnose the condition of the heart.

In some practices, the wearable diagnostic device can perform a process for obtaining body temperature for the user wearing the wearable diagnostic device. When the wearable diagnostic device is wrapped around the user's arm and comes into contact with the user's skin, the temperature sensor of the wearable diagnostic device obtains the user's body temperature based on the skin contact. For example, the temperature sensor can touch the wrist and obtain the user's temperature data for a certain period of time. The temperature sensor provides the sensor data to one or more processors, which converts the received data to Fahrenheit scale and averages the body temperature data obtained over one or more time periods to estimate the user. You can get body temperature.

In some practices, the wearable diagnostic device can perform a process for obtaining hand vibration for the user wearing the wearable diagnostic device. One or more processors can perform operations using timers and accelerometers or piezoelectric vibration sensors to obtain hand vibration measurements. For example, when a user is instructed to be interested in seeing information about hand vibration, the processor sets a timer for a specific period, eg 60 seconds, and an accelerometer to obtain vibration data over a period of time. Alternatively, it can be instructed to the piezoelectric vibration sensor. An accelerometer or piezoelectric vibration sensor can sense the number and intensity of vibrations of the user's hand over a period of time and provide the processor with data indicating the number of vibrations.

The processor can receive data indicating the number of vibrations and can determine the frequency of vibrations. If the vibration has a frequency of 3-7 hertz (Hz), the vibration can be classified as a vibration associated with the tremor of Parkinson's disease. The processor can also obtain amplitude information from accelerometers or piezoelectric vibration sensors to determine the intensity of any vibration associated with Parkinson's tremor. Data indicating the timing, intensity, and frequency of vibrations can be stored in the user profile in the storage device and / or presented to the user by the display.

The above process for obtaining information about a user's glucose level, hemoglobin level, blood pressure level, EKG, heart rate level, body temperature, hand vibration, and cholesterol level may be performed in parallel, simultaneously, or at different times. it can. The wearable diagnostic device has sufficient processing power to execute any of these processes at any time and at the request of the user.

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

In some practices, the user can enter a request to perform one type of diagnostic test, eg, a glucose test, but a wearable diagnostic device may be one or more to be performed with the diagnostic test requested by the user. Tests such as blood pressure tests can be determined. Additional tests, such as frequently selected tests performed simultaneously, sequentially or in pairs, by the user, by default settings, by manufacturer settings, at the recommendation of a physician, etc., are based on one or more criteria. Can be decided. In some practices, additional tests can be determined based on the user's medical history. For example, if a user has diabetes and hypertension, the wearable diagnostic device can also perform a glucose test each time the user chooses a blood pressure test. If the user chooses a glucose test, the wearable diagnostic device can also perform a blood pressure test.

The results obtained from the execution of one or more of the above processes can be stored in memory within the wearable diagnostic device or in a database or cloud account associated with the user. The results obtained from performing one or more of the above processes may also be shown on the display. In some practices, the wearable diagnostic device is used 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. It can output a visual alarm, a voice alarm, or an electronic alarm. For example, a wearable diagnostic device can output audio, such as sound waves, to advise the user to see a doctor, for example, if the EKG contains signs of abnormal heart movement or if the body temperature is above 102 degrees Fahrenheit. Can be generated.

The practices and / or operations described herein are digital electronic circuits, or computer software, firmware, or hardware, including the structures disclosed herein and their structural equivalents, or one or more of them. Please understand that it can be implemented in multiple combinations. The implementation is one or more of computer program instructions executed by one or more computer program products, such as a data processor, or encoded on a computer-readable medium for controlling the operation of the data processor. It can be implemented as a module of. The computer-readable medium can be a machine-readable storage device, a machine-readable storage board, a memory device, a composition of matter that generates a machine-readable propagation signal, or a combination thereof. The term "data processor" includes, by way of example, any device, device, and machine for processing data, including programmable processors, computers, or multiple processors or computers. In addition to the hardware, the device contains code that generates the execution environment for the computer program, such as processor firmware, protocol stack, database management system, operating system, or code that constitutes one or more combinations thereof. obtain. The propagating signal is an artificially generated signal, eg, a machine-generated electrical, optical, or electromagnetic signal generated to encode information for transmission to a suitable receiving 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, as stand-alone programs, or as modules. Can be deployed in any format, including as a component, subroutine, or other unit suitable for use in a computing environment. Computer programs do not always support files in the file system. A program may be part of a file that holds other programs or data (eg, one or more scripts stored in a markup language document), a single file dedicated to that program, or multiple federated files (eg, multiple federated files). For example, it can be saved in one or more modules, subprograms, or a file that saves part of the code). Computer programs can be run on one computer, or on multiple computers located at one site or distributed across multiple sites and interconnected by communication networks.

The process and logic flows described herein are programmable one or more to execute one or more computer programs to operate by operating on input data and producing output. It can be done by the processor. The process and logic flow may also be performed by dedicated logic circuits, such as FPGAs (Field Programmable Gate Arrays) or ASICs (Application Specific Integrated Circuits), or the equipment may be implemented as dedicated logic circuits.

Computer processors that execute computer programs include, for example, both general purpose and dedicated microprocessors, and any one or more processors of any type of digital computer. Generally, the processor receives instructions and data from read-only memory, random access memory, or both.

Although the present specification includes a number of specifics, these should not be construed as restrictions on the scope of disclosure or what can be claimed, but rather as an explanation of the characteristics specific to a particular embodiment. Is. The particular features described herein in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, the various features described in the context of a single embodiment can also be implemented individually in multiple embodiments or in any suitable partial combination. Further, the features may be as described above as acting in a particular combination and may even be claimed as such, but one or more features from the claimed combination may in some cases. , The combinations that may be removed from the combinations and that are claimed may be subject to partial combinations or variants of the partial combinations.

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

Therefore, specific implementations have been described. Other implementations are within the scope of the following claims. For example, the actions described in the claims can still achieve the desired result even if they are performed in different orders.

Therefore, specific implementations have been described. Other implementations are within the scope of the following claims. For example, the actions described in the claims can still achieve the desired result even if they are performed in different orders.
The present application provides the invention of the following aspects.
(Aspect 1)
A system comprising one or more computer devices and one or more storage devices, wherein the one or more storage devices are executed by the one or more computer devices. Next to computer equipment:
Receiving input corresponding to a request to initiate a non-invasive diagnostic test to detect a user's condition;
Identifying one or more sensors to perform the non-invasive diagnostic test;
Activating one or more of the identified sensors based on the non-invasive diagnostic test;
Receiving signal data through the one or more sensors;
Obtaining predictions for the non-invasive diagnostic test based in part on the user profile;
Determining test results based on the predicted values and the received signal data;
The system that stores an instruction to execute the output of the test result via a display or a speaker.
(Aspect 2)
The system according to aspect 1, wherein the non-invasive diagnostic test includes one or more of a glucose test, a cholesterol test, a hemoglobin test, an oxygen saturation level test, and an electrocardiogram monitoring test.
(Aspect 3)
The above operation is:
Choosing a route based on the raw data obtained from the received signal data;
Further including obtaining a set of values determined based on the selected route, including
The system according to aspect 1, wherein determining the test result based on the predicted value and the received signal data includes determining the test result based on the determined value.
(Aspect 4)
The above operation is:
Obtain user clinical data and user demographic data from one or more databases;
Determining the scope of the clinical dataset based on the obtained clinical data of the user and the demographic data of the user;
The system according to aspect 1, further comprising mapping the extent of the clinical data set to the predicted values of the non-invasive diagnostic test.
(Aspect 5)
The above operation is:
The second non-invasive diagnostic test to be performed is determined based on (i) the user pattern that associates the second non-invasive diagnostic test with the non-invasive diagnostic test, and (ii) the user's medical history. ;
Activating a second set of one or more sensors based on the second non-invasive diagnostic test;
Receiving the second signal data through the second set of the one or more sensors;
Obtaining a second prediction of the second non-invasive diagnostic test based in part on the user profile;
The system according to aspect 1, further comprising determining a second test result based on the second predicted value and the received second signal data.
(Aspect 6)
The system according to aspect 5, wherein obtaining a predicted value of the non-invasive diagnostic test comprises using the second test result to determine a predicted value of the non-invasive diagnostic test.
(Aspect 7)
The system according to aspect 5, wherein the second non-invasive diagnostic test is a cholesterol test performed at the same time as the non-invasive diagnostic test, which is a glucose test.
(Aspect 8)
The system according to aspect 5, wherein the non-invasive diagnostic test and the second non-invasive diagnostic test are performed by a clock having the one or more computer devices.
(Aspect 9)
The system according to aspect 1, wherein the one or more sensors comprises one or more wireless core electrodes, piezoelectric vibration sensors, infrared sensors, temperature sensors, accelerometers, and microelectromechanical systems (MEMS) sensors.
(Aspect 10)
Receiving input corresponding to a request to initiate a non-invasive diagnostic test to detect a user's condition;
Identifying one or more sensors for performing the non-invasive diagnostic test by one or more computer devices;
Activating one or more of the identified sensors based on the non-invasive diagnostic test;
Receiving signal data through the one or more sensors;
Obtaining the predicted value of the non-invasive diagnostic test based in part on the user profile by the one or more computer devices;
The one or more computer devices determine the test result based on the predicted value and the received signal data;
A computer practice method comprising outputting the test result via a display or a speaker by one or more computer devices.
(Aspect 11)
The computer-implemented method according to aspect 10, wherein the non-invasive diagnostic test includes one or more of a glucose test, a cholesterol test, a hemoglobin test, an oxygen saturation level test, and an electrocardiogram monitoring test.
(Aspect 12)
The above operation is:
Choosing a route based on the raw data obtained from the received signal data;
Further including obtaining a set of values determined based on the selected route, including
The computer implementation method according to aspect 10, wherein determining the test result based on the predicted value and the received signal data includes determining the test result based on the determined value.
(Aspect 13)
The above operation is:
Obtaining user clinical data and user demographic data from one or more databases;
Determining the scope of the clinical dataset based on the obtained clinical data of the user and the demographic data of the user;
10. The computer practice method according to aspect 10, further comprising mapping the extent of the clinical dataset to the predicted values of the non-invasive diagnostic test.
(Aspect 14)
The above operation is:
The second non-invasive diagnostic test to be performed is determined based on (i) the user pattern that associates the second non-invasive diagnostic test with the non-invasive diagnostic test, and (ii) the user's medical history. ;
Activating a second set of one or more sensors based on the second non-invasive diagnostic test;
Receiving the second signal data through the second set of the one or more sensors;
Obtaining a second prediction of a second non-invasive diagnostic test based in part on the user profile;
The computer implementation method according to aspect 10, further comprising determining the second test result based on the second predicted value and the received second signal data.
(Aspect 15)
The computer-implemented method of aspect 14, wherein obtaining a predicted value of the non-invasive diagnostic test comprises using the second test result to determine a predicted value of the non-invasive diagnostic test.
(Aspect 16)
The computer-implemented method according to aspect 14, wherein the second non-invasive diagnostic test is a cholesterol test performed at the same time as a non-invasive diagnostic test which is a glucose test.
(Aspect 17)
The computer implementation method according to aspect 14, wherein the non-invasive diagnostic test and the second non-invasive diagnostic test are performed by a clock equipped with the one or more computer devices.
(Aspect 18)
10. The computer embodiment according to aspect 10, wherein the one or more sensors comprises one or more of a radio center electrode, a piezoelectric vibration sensor, an infrared sensor, a temperature sensor, an accelerometer, and a microelectromechanical system (MEMS) sensor. ..
(Aspect 19)
A watch comprising one or more computer devices and one or more storage devices, the one or more of which is executed by the one or more computer devices. Next to computer equipment:
Select glucose and cholesterol tests based on user profile;
Identifying the glucose test and one or more sensors for performing the cholesterol test;
Activating the identified one or more sensors;
Receiving signal data through the one or more sensors;
Obtaining a first prediction of the glucose test based in part on the user profile;
Determining glucose and cholesterol test results based on the first predicted value and the received signal data;
The watch that stores an instruction to execute the glucose test result and the output of the cholesterol test result via a display or a speaker of the watch.
(Aspect 20)
The one or more sensors include an infrared sensor and a piezoelectric vibration sensor.
The above operation is:
Choosing a route based on the raw data obtained from the received signal data;
Further including obtaining a set of values determined based on the selected pathway;
The clock according to aspect 19, wherein determining the glucose test result based on the first predicted value and the received signal data includes determining the glucose test result based on the determined value.

Claims (20)

A system comprising one or more computer devices and one or more storage devices, wherein the one or more storage devices are executed by the one or more computer devices. Next to computer equipment:
Receiving input corresponding to a request to initiate a non-invasive diagnostic test to detect a user's condition;
Identifying one or more sensors to perform the non-invasive diagnostic test;
Activating one or more of the identified sensors based on the non-invasive diagnostic test;
Receiving signal data through the one or more sensors;
Obtaining predictions for the non-invasive diagnostic test based in part on the user profile;
The system that stores an instruction to determine a test result based on the predicted value and the received signal data; and output the test result via a display or a speaker. The system according to claim 1, wherein the non-invasive diagnostic test includes one or more of a glucose test, a cholesterol test, a hemoglobin test, an oxygen saturation level test, and an electrocardiogram monitoring test. The above operation is:
It further includes selecting a route based on the raw data obtained from the received signal data; and obtaining a set of values determined based on the selected route.
The system according to claim 1, wherein determining the inspection result based on the predicted value and the received signal data includes determining the inspection result based on the determined value. The above operation is:
Obtain user clinical data and user demographic data from one or more databases;
Determining the extent of the clinical dataset based on the obtained clinical data of the user and the demographic data of the user; and further mapping the scope of the clinical dataset to the predicted values of the non-invasive diagnostic test. The system according to claim 1, including. The above operation is:
The second non-invasive diagnostic test to be performed is determined based on (i) the user pattern that associates the second non-invasive diagnostic test with the non-invasive diagnostic test, and (ii) the user's medical history. ;
Activating a second set of one or more sensors based on the second non-invasive diagnostic test;
Receiving the second signal data through the second set of the one or more sensors;
Obtaining a second predicted value of the second non-invasive diagnostic test based in part on the user profile; and a second based on the second predicted value and the received second signal data. The system of claim 1, further comprising determining test results. The system according to claim 5, wherein obtaining the predicted value of the non-invasive diagnostic test includes determining the predicted value of the non-invasive diagnostic test using the second test result. The system according to claim 5, wherein the second non-invasive diagnostic test is a cholesterol test performed at the same time as the non-invasive diagnostic test, which is a glucose test. The system according to claim 5, wherein the non-invasive diagnostic test and the second non-invasive diagnostic test are performed by a clock having the one or more computer devices. The system according to claim 1, wherein the one or more sensors includes one or more wireless core electrodes, piezoelectric vibration sensors, infrared sensors, temperature sensors, accelerometers, and microelectromechanical systems (MEMS) sensors. Receiving input corresponding to a request to initiate a non-invasive diagnostic test to detect a user's condition;
Identifying one or more sensors for performing the non-invasive diagnostic test by one or more computer devices;
Activating one or more of the identified sensors based on the non-invasive diagnostic test;
Receiving signal data through the one or more sensors;
Obtaining the predicted value of the non-invasive diagnostic test based in part on the user profile by the one or more computer devices;
The one or more computer devices determine the test result based on the predicted value and the received signal data;
A computer practice method comprising outputting the test result via a display or a speaker by one or more computer devices. The computer-implemented method of claim 10, wherein the non-invasive diagnostic test includes one or more of a glucose test, a cholesterol test, a hemoglobin test, an oxygen saturation level test, and an electrocardiogram monitoring test. The above operation is:
It further includes selecting a route based on the raw data obtained from the received signal data; and obtaining a set of values determined based on the selected route.
The computer implementation method according to claim 10, wherein determining the inspection result based on the predicted value and the received signal data includes determining the inspection result based on the determined value. The above operation is:
Obtaining user clinical data and user demographic data from one or more databases;
Determining the scope of the clinical dataset based on the obtained clinical data of the user and the demographic data of the user; and mapping the scope of the clinical dataset to the predicted values of the non-invasive diagnostic test. The computer implementation method of claim 10, further comprising. The above operation is:
The second non-invasive diagnostic test to be performed is determined based on (i) the user pattern that associates the second non-invasive diagnostic test with the non-invasive diagnostic test, and (ii) the user's medical history. ;
Activating a second set of one or more sensors based on the second non-invasive diagnostic test;
Receiving the second signal data through the second set of the one or more sensors;
Obtaining a second predicted value of the second non-invasive diagnostic test based in part on the user profile; and the second predicted value based on the second predicted value and the received second signal data. The computer implementation method of claim 10, further comprising determining test results. 14. The computer-implemented method of claim 14, wherein obtaining a predicted value of the non-invasive diagnostic test comprises using the second test result to determine a predicted value of the non-invasive diagnostic test. The computer-implemented method according to claim 14, wherein the second non-invasive diagnostic test is a cholesterol test performed at the same time as a non-invasive diagnostic test which is a glucose test. 14. The computer-implemented method of claim 14, wherein the non-invasive diagnostic test and the second non-invasive diagnostic test are performed by a clock equipped with the one or more computer devices. 10. The computer embodiment of claim 10, wherein the one or more sensors includes one or more wireless core electrodes, piezoelectric vibration sensors, infrared sensors, temperature sensors, accelerometers, and microelectromechanical system (MEMS) sensors. Method. A watch comprising one or more computer devices and one or more storage devices, the one or more of which is executed by the one or more computer devices. Next to computer equipment:
Select glucose and cholesterol tests based on user profile;
Identifying the glucose test and one or more sensors for performing the cholesterol test;
Activating the identified one or more sensors;
Receiving signal data through the one or more sensors;
Obtaining a first prediction of the glucose test based in part on the user profile;
To determine the glucose test result and the cholesterol test result based on the first predicted value and the received signal data; and to output the glucose test result and the cholesterol test result via the display or the speaker of the clock. The clock that stores the command to execute. The one or more sensors include an infrared sensor and a piezoelectric vibration sensor.
The above operation is:
Further including selecting a route based on the raw data obtained from the received signal data; and obtaining a set of values determined based on the selected route;
The clock according to claim 19, wherein determining a glucose test result based on the first predicted value and received signal data includes determining the glucose test result based on the determined value.

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