JP7232261B2 - Wearable diagnostic device - Google Patents

Description

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

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

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

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

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

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

In some implementations, the action determines the second noninvasive diagnostic test to be performed by determining (i) a user pattern that associates the second noninvasive diagnostic test with the noninvasive diagnostic test, and (ii) the user operating a second set of one or more sensors based on the second noninvasive diagnostic test; operating the second set of one or more sensors obtaining a second predictive value of the second non-invasive diagnostic test based in part on a user profile; and the second predictive value and the received second Further comprising determining a second test result based on the signal data of the two.

In some implementations, obtaining the predictive value of the non-invasive diagnostic test includes using the second test result to determine the predictive value of the non-invasive diagnostic test.

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

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

In some implementations, the one or more sensors include one or more of wireless cardiac electrodes, piezoelectric vibration sensors, infrared sensors, temperature sensors, accelerometers, and microelectromechanical system (MEMS) sensors.

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

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

The above aspects and implementations described further herein provide several advantages. For example, a single wearable diagnostic device can perform multiple non-invasive diagnostic tests, including a non-invasive glucose test, a non-invasive cholesterol test, and a non-invasive hemoglobin test. Wearable diagnostic devices can also obtain electrocardiogram (EKG) data, or detection of Parkinson's disease symptoms such as involuntary movements of the user's hands. A wearable diagnostic device with wireless electrodes can record a user's history and medical conditions, and utilize predictive values, algorithms, and mapping databases to improve the accuracy and reliability of glucose, cholesterol, and/or hemoglobin tests. It can provide excellent results. Because the predicted value includes the user's clinical and demographic information, the calculation can more accurately take into account parameters such as skin color and age that can affect glucose or hemoglobin levels. The predictive value can also be used to correlate the predictive value with the user's susceptibility to a particular disease.

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

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

(Brief description of the drawing)
1-5 show exemplary implementations of wearable diagnostic devices.

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

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

FIG. 8 shows a flowchart of an exemplary method for determining glucose prediction values.

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

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

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

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

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

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

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

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

1-5, the wearable watch includes a casing 101, a display 102, removable wireless heart electrodes 103, 113, belts 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 layers 118, 120, insulated wire 119, accelerometer 121, power manager 122 , a system-on-chip (SoC) 123 , and a power switch 124 . A wearable watch can be worn on the left arm 150 of the user.

Casing 101 houses display 102, charging connector 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. may comprise or be coupled to Display 102 may 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 implementations, display 102 may be a touch screen, such as a capacitive touch screen.

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

The one or more processors within casing 101 can be implemented in 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 transceiver. For example, the processor can receive data indicative of user selections 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. You can generate commands to execute.

In some implementations, one or more processors can transmit and receive data using wireless transceivers. Data can be sent and received between the wearable diagnostic device and the secondary device. A secondary device may be a device selected by a user, a network server, or a device configured to communicate with a wearable watch. For example, the user can select another device that he owns to send data from the wearable diagnostic device to it. 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 user requests or predetermined schedules of data to be sent and received.

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. The one or more servers may be a set of servers running a network operating system. One or more servers may be used for and/or provide cloud and/or network computing.

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

In some implementations, the database may include a user database that can store user information such as alias name, medical history, and any medical conditions of the user. The user database can also store data related to licenses, permissions, and certificates for accessing various tools and software. In some cases, the user may select the option to anonymize the user data such that any information that can identify the user is anonymized and user identifying information is removed prior to storing the data in the user database. .

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

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

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

Casing 101 is connected to belts 104, 109, connector 107, and belt connector 114, which can be adjusted to secure the wearable diagnostic device around different sized wrists. For example, belts 104, 109 and belt connector 114 can be wrapped around a user's wrist, and belt fastener 115 can hold belts 104, 109 and belt connector 114 in place. A back cover 112 and a heart electrode 113 can be placed on the bottom of the casing 101 .

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

The wearable diagnostic device may also include one or more sensors such as cardiac 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 implementations, piezoelectric vibration sensor 105 may include a micro-electro-mechanical system (MEMS) sensor.

Wireless cardiac electrodes 103, 113 may comprise a metallic conductive material configured to detect cardiac potential waveforms, such as the voltages produced during contraction of the heart. Wireless cardiac electrodes 103 are disposed on an outer belt layer 118 corresponding to the outer circumference of the wearable diagnostic device. Wireless cardiac electrodes 113 are disposed on or integrated with the back cover 112 so that the wearable diagnostic device can contact the user's skin when secured around the user's arm 150 . The wireless cardiac electrodes 103, 113 can be removed and reattached to the wearable diagnostic device and can wirelessly communicate data to another electronic device.

IR/light/laser sensor 106 may comprise 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, 650-1400 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 infrared signals received from the user's body.

In some implementations, the IR/light/laser sensor 106 can detect pulsed signals and process the detected signals using pulse wave transit time (PWTT) in the Artery method. These pulse signals can be combined with data obtained by piezoelectric vibration sensor 105 to predict blood pressure non-invasively. The IR/light/laser sensor 106 can also be used to non-invasively determine or obtain data indicative of the oxygen saturation level ( _SpO2 ) in the user's blood.

A piezoelectric vibration sensor 105 and an 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. A temperature sensor 108 can be used to measure the user's temperature. Temperature sensor 108 can be implemented in a variety of suitable ways. For example, temperature sensor 108 can be a thermocouple, a silicon bandgap sensor, a thermometer, or a thermistor that includes one or more sense resistors. A change in electrical resistance in the sensing resistor may correspond to a change in body temperature. In some implementations, a temperature sensor may include active and passive components and resistors and semiconductor materials fabricated into a single IC package.

One or more measurements may be obtained using a sensor, as described in further detail below. The operations performed using the wearable diagnostic device to determine glucose levels, hemoglobin levels, blood pressure levels, EKG, heart rate levels, body temperature, and hand vibration are described in further detail below. This action involves the user placing the wearable diagnostic device on the user's left arm 150 and adjusting one or more of belts 104, 109, belt connector 114, belt fastener 115, and outer belt layers 118, 120 to Begin by securing the wearable diagnostic device around the user's left arm 150 .

When the wearable diagnostic device is secured to the user's left arm 150, the IR/light/laser sensor 106 may contact the bottom of the user's left wrist just above the user's radioulnar artery. As noted above, the IR/light/laser sensor 106 can include a signal generator for generating and transmitting infrared signals. A signal can be transmitted from the IR/light/laser sensor 106 towards the user's skin under the user's left wrist.

The IR/light/laser sensor 106 can detect signal reflections from the user's skin. A detected signal containing absorption spectrum data can then be converted to a digital signal using an analog-to-digital converter (ADC). The conversion results in raw digital data corresponding to the signals received from the user's skin. The raw digital data can be processed to determine absorption spectra 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 estimated.

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

In some implementations, the wearable diagnostic device can obtain information related to the user's health. The user's health-related information includes, but is not limited to, the user's residence, the user's physician, the user's pharmacist, the user's medical record owner, the user's the user's diet, an indication of the user's number of children, the user's medical history, one or more past or current medical conditions, such as the user's allergies, surgeries, genetic conditions, the user's one or Multiple health concerns and one or more blood content levels that the user is interested in obtaining details may be included. For example, a user can indicate that they are particularly interested in their glucose level or blood pressure level. The user can also indicate how often they would like information about their glucose level or blood pressure level.

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

In some implementations, a user may subscribe or choose to participate in a service that allows the wearable diagnostic device to receive and update the user's medical information in real time. In particular, wearable diagnostic devices can update test results, consultation results, diagnoses, or prescriptions. For example, a user may authorize the user's physician, pharmacist, or medical record owner 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 gather information from various sources to update user profiles. The wearable diagnostic device can receive updates regarding the user's medical information in real time, periodically, or upon the user's request.

In some implementations, a user can directly enter information regarding their health and background using the user interface of the wearable diagnostic device. The entered user medical information can then be stored remotely or stored on the wearable diagnostic device.

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

In some implementations, the wearable diagnostic device determines one of a glucose level, a hemoglobin level, or a blood pressure level for the user wearing the wearable diagnostic device based on received or obtained information 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 are further described in FIGS. 7-10.

Referring to Figures 7, 9 and 13, the wearable diagnostic device may receive an indication that a test should be performed (702, 902, 1302). For example, the wearable diagnostic device may receive an indication that a glucose test (702), hemoglobin test (902), or cholesterol test (1302) should be performed. An indication that a test should 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 according to a scheduled time. For example, a wearable diagnostic device can be programmed to provide blood pressure measurements at specific times or after a period of time. The wearable diagnostic device can then schedule a time and date to provide the user with a particular measurement, such as a blood pressure measurement, and initiate a method of measuring blood pressure and oxygen saturation levels at the scheduled time. can be done.

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

The activated sensor or sensors can obtain measurements characterizing the user (706, 906, 1306). For example, each of the one or more IR/light/laser sensors may be configured to emit an infrared signal or pulse signal for a short period of time, say 30 seconds, and detect the reflection of the emitted 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 circumstances such as different pulse powers or magnetic fields. For example, in the case of cholesterol measurements, a first current or infrared signal measurement can be obtained without an applied magnetic field, and one or more other current or infrared measurements can be taken with magnetic fields of various low power intensities applied for a period of time. For example after being generated for 10 seconds.

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

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

Table I

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

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

When a hemoglobin test is being performed, an exemplary mapping such as that shown in Table II can be used to select a particular pathway and corresponding prediction and slope regression values. Path A and corresponding hemoglobin (Hb) prediction value of 31.5 and Hb slope regression value of 0.114 are selected if the raw value is greater than or equal to 500 and less than or equal to 700. If the raw value is 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 value is 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 value is 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 value is greater than or equal to 1101, path E and corresponding Hb predicted value of 100.5 and Hb slope regression value of 0.114 are selected.

Table II

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

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

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

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

A predicted cholesterol level value is then obtained from the database (1310). The database may contain mappings of cholesterol levels to raw values and may be organized according to demographic class. A database can be constructed based on testing of multiple human subjects, as described below.

First, a 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, skin color, marital status, and/or medical conditions. For example, a subject may have a demographic profile of 48 years old, white male, widowed, skin cancer. Invasive cholesterol tests can be performed on each subject using a variety of suitable methods, and test results can be added to the subject's profile.

Current and infrared signal measurements of the subject can then be obtained using resistive and infrared sensors, respectively, as described above. Current and infrared signal measurements can be taken before applying the magnetic field and after applying the magnetic field for a period of time. A derivative of the measured signal can be calculated and stored as a raw value. Raw values are mapped to cholesterol levels obtained from invasive cholesterol tests and saved in the subject's profile.

With a large sample size of subjects, hundreds of and thousands of subjects can be tested. In some implementations, statistical data can be extracted from the tests so that the mean, median, and mode of cholesterol levels and raw values can be determined by demographic class.

In general, different kinds of demographic classes can be formed. For example, in some cases, the demographic class can be restricted to people in the age group, eg, 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 estimated cholesterol levels based on raw values associated with particular demographic classes. It should be noted here that the database can be constructed anonymously so that the subject's personal information is never revealed or known.

Returning to FIG. 13, predicted cholesterol level values are obtained 1310 from the database based on the raw values obtained in operation 1308 . Specifically, the cholesterol levels mapped to the raw values obtained in operation 1308 and the demographic profile of the user wearing the wearable diagnostic device are obtained by referencing a database mapping table.

A 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 implementations, the predicted cholesterol level value can be compared to previously obtained cholesterol levels of the user. If the difference between the predicted cholesterol level and the user's last obtained cholesterol level is greater than a threshold value, eg, 3%, the wearable diagnostic device repeats operations 1302 through 1310 to obtain a new predicted cholesterol level. can be done. Such iterations may continue until the predicted cholesterol level value is within the user's last obtained cholesterol level threshold difference. Optionally, if three repetitions are performed without meeting the threshold difference, the predicted cholesterol level value obtained in the third repetition 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 exemplary implementation above, predictive values were utilized to determine glucose, hemoglobin, or cholesterol levels. Predictive value can be determined based on several factors using a variety of different methods. Methods for obtaining predictive values for cholesterol levels are described above. Methods for obtaining predictive values for glucose and hemoglobin levels are further described in connection with FIGS. 8 and 10. FIG.

Referring to FIG. 8, the wearable diagnostic device may obtain user profile information (802) to obtain a predicted glucose level for the user. User profile information may include the user's location, the user's physician, the user's pharmacist, the user's medical record owner, the user's demographic associations 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 current medical conditions, such as the user's allergies, surgeries, genetic conditions, the user's one or more health concerns, and the user's obtaining details; may include one or more of the one or more blood content levels of interest.

Using information from the user's profile, the wearable diagnostic device can determine a Clinical Correlation Information Value (cCIG) of glucose for the user based on the user's blood pressure and temperature (804). In some implementations, cCIG may be a matrix of values representing estimated blood pressure and estimated body temperature. In general, any suitable method may be used to obtain the user's blood pressure and temperature. In some implementations, 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. can. In some cases, the cCIG can be determined using clinical information obtained from a user profile, such as the user's medical history, which includes data such as physician reports, past or current medical conditions, clinical diagnoses, and the like.

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

After obtaining cCIG and cLDG, the wearable diagnostic device can determine the extent of the clinical data set based on cCIG and cLDG (810). The determined clinical data set range is mapped 812 to a glucose prediction value for the user's glucose level. A database storing various clinical data set ranges and glucose predictive values can be queried with the determined clinical data set range and provides glucose predictive values that map to the determined clinical data set range. can return. A predicted glucose value can then be provided and utilized in determining the user's glucose level (814).

Referring to FIG. 10, in order to obtain a predicted hemoglobin level for the user, the wearable diagnostic device may obtain user profile information (1002). User profile information may include the user's location, the user's physician, the user's pharmacist, the user's medical record owner, the user's demographic associations 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 current medical conditions, such as the user's allergies, surgeries, genetic conditions, the user's one or more health concerns, and the user's obtaining details; may include one or more of the one or more blood content levels of interest.

Using information from the user's profile, the wearable diagnostic device can determine the user's hemoglobin clinical correlation information value (cCIHb) (1004). The cCIHb can be determined using clinical information obtained from a user profile, such as the user's medical history, which includes data such as physician reports, past or current medical conditions, clinical diagnoses, and the like. In some implementations, cCIHb may be a matrix of values representing various aspects of the user's medical history if the user consents to provide the following personal information:

Next, the wearable diagnostic device can determine the user's clinically relevant demographic value of hemoglobin (cLDHb) by obtaining the user's oxygen saturation data and temperature data (1006). In some implementations, cLDHb may 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 implementations, 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 implementations, the cLDHb is based in part on information provided by the user profile, such as one or more user characteristics such as the user's demographic group, the user's age, and the user's other personal characteristics. can decide.

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

By using predicted values to determine the user's glucose and hemoglobin levels in addition to using blood pressure data and temperature data, the user's determined glucose and hemoglobin levels can be obtained without using predicted values. It has much higher accuracy compared to methods that determine glucose and hemoglobin levels. Because the predicted value includes the user's clinical and demographic information, the calculation can more accurately take into account parameters such as skin color and age that can affect glucose or hemoglobin levels. can. The predictive value can also be used to relate the predictive value to a user's susceptibility to particular diseases so that the user can take preventative measures to minimize the chances of contracting these diseases and prevent the user from health and life expectancy can be improved. Moreover, these multiple tests can be performed and test results can be obtained on demand with a single device, providing a high degree of convenience to the user.

In addition to obtaining the user's glucose and hemoglobin levels, the wearable diagnostic device can determine the user's blood pressure level and oxygen saturation level. A flowchart of an exemplary method for determining a user's blood pressure level and oxygen saturation level is shown in FIG. Initially, the wearable diagnostic device may receive an indication that a blood pressure measurement should be taken (1102). The indication that a blood pressure measurement should be taken may include one or more selections made by a user to provide blood pressure measurements 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 specific times or after a period of time. The wearable diagnostic device can then schedule the days and times when the blood pressure measurements should be provided to the user, and at the scheduled time, the method of determining blood pressure and oxygen saturation levels can be initiated.

After receiving the indication that a blood pressure measurement should be performed (1102), the wearable diagnostic device can determine the type of sensor utilized for the blood pressure test, activate the determined type of sensor, and perform the blood pressure test. , the determined types of sensors may include piezoelectric vibration sensors and IR/light/laser sensors (1104). Activating the sensor may include a number of actions including, but not limited to, increasing power to the sensor and configuring or calibrating the sensor.

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

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 obtain infrared target detection (IRTD) values and piezoelectric vibration (PV) values. A value can be generated (1108). In general, various signal processing operations can be performed on signals detected by piezoelectric vibration sensors and IR/light/laser sensors. In some implementations, operations 1104-1108 can be repeated multiple times, and multiple IRTD and PV values can be averaged.

Processing may also include comparing the determined IRTD and PV values to predicted IRTD and PV values. This comparison produces two sets of blood pressure values. Each of the two sets of blood pressure values are averaged to obtain systolic and diastolic blood pressure values respectively (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) [Formula 3]

Generating predictions for blood pressure measurements is described below with reference to FIG. In some implementations, the oxygen saturation level of the user's blood may also be determined using [equation 4] (1110).
Oxygen saturation level = (( _CHbO2 )/( _CHbO2 + _CHb )) x 100 [Formula 4]

In Equation 4, _CHbO2 equals the concentration of oxygenated hemoglobin and _CHb equals the concentration of deoxygenated hemoglobin. The C _HbO2 and C _Hb values 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 implementations, blood pressure values and oxygen saturation values are displayed on the display. In some implementations, blood pressure values and oxygen saturation values are output from an audio speaker. In some implementations, blood pressure values and oxygen saturation values 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 prompted to confirm whether a message containing blood pressure and oxygen saturation values should be sent to another electronic device.

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

Using 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 a user profile, such as the user's medical history, which includes data such as physician reports, past or current medical conditions, and clinical diagnoses. EKG data can be obtained from a variety of suitable sources, such as the user's medical history.

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

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

In some implementations, the wearable diagnostic device may perform processes to obtain the EKG and heart rate levels of the user wearing the wearable diagnostic device.

When the wearable diagnostic device is secured to the user's left arm, the first cardiac electrode can be placed on or within the wearable diagnostic device's bottom surface and can contact the upper left wrist of the user. A second cardiac electrode is placed on or within the wearable diagnostic device without contacting the skin of the user's left arm. A first cardiac electrode can obtain a signal from the upper wrist of the user's left arm and a second cardiac electrode obtains two signals with the second cardiac electrode placed on the user's chest. be able to. The acquired signals may include electrocardiographic waveforms, such as the voltages produced during contraction of the heart. The data obtained from the three signals are converted into PQRST (Provocation, Quality, Radiation, Severity, Time) waves. The PQRST wave can be used to generate Eindhoven's triangle, EKG plots, and calculate additional information such as the user's heart rate. In some cases, the PQRST wave can be used to diagnose heart conditions.

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

In some implementations, the wearable diagnostic device can perform a process to obtain hand vibrations for the user wearing the wearable diagnostic device. The one or more processors may perform operations using timers and accelerometers or piezoelectric vibration sensors to obtain hand vibration measurements. For example, upon receiving an indication that the user is interested in seeing information about hand vibrations, the processor may set a timer to a specific period of time, say 60 seconds, and instruct the accelerometer to obtain vibration data over a period of time. Or it can be directed to a piezoelectric vibration sensor. An accelerometer or piezoelectric vibration sensor can sense the number and strength of vibrations of the user's hand over a period of time and provide data to the processor indicating the number of vibrations.

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

The above processes for obtaining information about the user's glucose level, hemoglobin level, blood pressure level, EKG, heart rate level, body temperature, hand vibrations, and cholesterol level can occur in parallel, simultaneously, or at different times. can. Wearable diagnostic devices have sufficient processing power to perform any of these processes at any time and upon user demand.

In some implementations, multiple diagnostic tests can be performed sequentially or simultaneously. For example, an EKG test can be performed before or during a blood pressure test. Oxygen saturation and temperature tests 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 also possible. As an example, if the user has a history of a particular condition, such as being diabetic, the wearable diagnostic device may periodically run a test, such as a glucose diagnostic test, that is most relevant to the user's condition, and the most appropriate test. Other tests, such as blood pressure tests, cholesterol tests, or oxygen saturation tests, can be performed before, after, or during the diagnostic test.

In some implementations, a user may enter a request to perform one type of diagnostic test, such as a glucose test, but the wearable diagnostic device may request one or more diagnostic tests to be performed along 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, by physician recommendation, etc., may be based on one or more criteria. can be determined by In some implementations, additional testing can be determined based on the user's medical history. For example, if the user has diabetes and hypertension, 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 can also perform a blood pressure test.

Results from performing one or more of the processes described above may be stored in memory within the wearable diagnostic device or stored in a database or cloud account associated with the user. Results from performing one or more of the above processes may also be displayed on a display. In some implementations, the wearable diagnostic device detects if one or more of the determined glucose level, hemoglobin level, blood pressure level, EKG, heart rate level, body temperature, and hand vibration is indicative of a serious medical condition. A visual alarm, an audio alarm, or an electronic alarm can be output. For example, a wearable diagnostic device may output audio, such as sound waves, advising a user to see a doctor if, for example, an EKG contains signs of abnormal heart activity or a body temperature is above 102 degrees Fahrenheit. can be generated.

The implementation and/or operations described herein may be implemented using digital electronic circuitry, or computer software, firmware, or hardware, including the structures disclosed herein and structural equivalents thereof, or any one or more of these. It should be appreciated that multiple combinations can be implemented. The implementation may be one or more computer program products, e.g., one or more computer program instructions encoded on a computer readable medium for execution by or for controlling the operation of a data processing apparatus. can be implemented as a module of A computer-readable medium may be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter that generates a machine-readable propagating signal, or a combination of one or more thereof. The term "data processing apparatus" encompasses all equipment, devices and machines for processing data including, by way of example, a programmable processor, computer, or multiple processors or computers. A device includes, in addition to hardware, code that creates an execution environment for the computer program, e.g., code that constitutes processor firmware, protocol stacks, database management systems, operating systems, or combinations of one or more thereof. obtain. Propagated signals are artificially generated signals, for example, machine-generated electrical, optical, or electromagnetic signals generated to encode information for transmission to appropriate receiving equipment.

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 written as a stand-alone program or as a module. , components, subroutines, or other units suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program may be part of a file holding other programs or data (e.g., one or more scripts stored in a markup language document), a single file dedicated to that program, or multiple associated files (such as For example, files that store one or more modules, subprograms, or portions of code). A computer program can be executed on one computer or on multiple computers located at one site or distributed across multiple sites and interconnected by a communication network.

The processes and logic flow described herein are based on one or more programmable programs that execute one or more computer programs to perform operations by operating on input data and generating output. It can be done by a 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), and the device may be implemented as dedicated logic circuits.

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

While this specification contains many specifics, these should not be construed as limitations on the scope of the disclosure or what may be claimed, but rather as a description of features specific to particular embodiments. is. Certain features that are described in this specification in the context of separate embodiments can 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. Further, while features may be described above as working in particular combinations, and may even be claimed as such, one or more features from the claimed combination may in some cases be , may be omitted from the combination, and a claimed combination may cover subcombinations or variations of subcombinations.

One or more clauses and at least one clause should be understood to include any and all combinations of the 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.

Accordingly, specific implementations have been described. Other implementations are within the scope of the following claims. For example, the actions recited in the claims can be performed in a different order and still achieve desirable results.
The present application provides inventions of the following aspects.
(Aspect 1)
A system comprising one or more computing devices and one or more storage devices, wherein the one or more storage devices, when executed by the one or more computing devices The following actions on computer equipment:
receiving input corresponding to a request to initiate a non-invasive diagnostic test to detect a medical condition in a user;
identifying one or more sensors for performing the non-invasive diagnostic test;
activating the identified one or more sensors based on the non-invasive diagnostic test;
receiving signal data via the one or more sensors;
obtaining a predictive value of the non-invasive diagnostic test based in part on a user profile;
determining a test result based on the predicted value and the received signal data; and
outputting the test results via a display or a speaker.
(Aspect 2)
Aspect 1. The system of aspect 1, wherein the non-invasive diagnostic tests include one or more of a glucose test, a cholesterol test, a hemoglobin test, an oxygen saturation level test, and an electrocardiogram monitoring test.
(Aspect 3)
Said behavior is:
selecting a path based on raw data obtained from the received signal data; and
further comprising obtaining a set of values determined based on the selected path;
Aspect 1. The system of aspect 1, wherein determining a test result based on the predicted value and received signal data comprises determining the test result based on the determined value.
(Aspect 4)
Said behavior is:
Obtaining user clinical data and user demographic data from one or more databases;
determining the extent of a clinical data set based on the obtained user clinical data and the user demographic data; and
2. The system of embodiment 1, further comprising mapping a range of said clinical data set to a predictive value of said non-invasive diagnostic test.
(Aspect 5)
Said behavior is:
determining a second non-invasive diagnostic test to be performed based on (i) user patterns associating said second non-invasive diagnostic test with said non-invasive diagnostic test and (ii) the user's medical history. ;
activating a second set of one or more sensors based on the second non-invasive diagnostic test;
receiving second signal data via the second set of one or more sensors;
obtaining a second predictive value for the second non-invasive diagnostic test based in part on the user profile; and
Aspect 1. The system of aspect 1, further comprising determining a second test result based on the second predicted value and the received second signal data.
(Aspect 6)
6. The system of embodiment 5, wherein obtaining the predictive value of the non-invasive diagnostic test comprises using the second test result to determine the predictive value of the non-invasive diagnostic test.
(Aspect 7)
6. The system of embodiment 5, wherein the second non-invasive diagnostic test is a cholesterol test performed concurrently with the non-invasive diagnostic test that is a glucose test.
(Aspect 8)
6. The system of aspect 5, wherein the non-invasive diagnostic test and the second non-invasive diagnostic test are performed by a watch comprising the one or more computing devices.
(Aspect 9)
Aspect 1. The system of aspect 1, wherein the one or more sensors include one or more of wireless cardiac electrodes, piezoelectric vibration sensors, infrared sensors, temperature sensors, accelerometers, and microelectromechanical system (MEMS) sensors.
(Mode 10)
receiving input corresponding to a request to initiate a non-invasive diagnostic test to detect a medical condition in a user;
identifying, by one or more computing devices, one or more sensors for performing the non-invasive diagnostic test;
activating the identified one or more sensors based on the non-invasive diagnostic test;
receiving signal data via the one or more sensors;
obtaining, by the one or more computing devices, a predicted value of the non-invasive diagnostic test based in part on a user profile;
determining, by the one or more computing devices, a test result based on the predicted value and the received signal data; and
A computer-implemented method comprising outputting, by one or more computing devices, the test results via a display or speaker.
(Aspect 11)
11. The computer-implemented method of aspect 10, wherein the non-invasive diagnostic tests include one or more of glucose tests, cholesterol tests, hemoglobin tests, oxygen saturation level tests, electrocardiogram monitoring tests.
(Aspect 12)
Said behavior is:
selecting a route based on raw data obtained from the received signal data; and
further comprising obtaining a set of values determined based on the selected path;
11. The computer-implemented method of aspect 10, wherein determining a test result based on the predicted value and received signal data comprises determining the test result based on the determined value.
(Aspect 13)
Said behavior is:
Obtaining user clinical data and user demographic data from one or more databases;
determining the extent of a clinical data set based on the obtained user clinical data and the user demographic data; and
11. The computer-implemented method of embodiment 10, further comprising mapping ranges of said clinical data set to predictive value of said non-invasive diagnostic test.
(Aspect 14)
Said behavior is:
determining a second non-invasive diagnostic test to be performed based on (i) user patterns associating said second non-invasive diagnostic test with said non-invasive diagnostic test and (ii) the user's medical history. ;
activating a second set of one or more sensors based on the second non-invasive diagnostic test;
receiving second signal data via the second set of one or more sensors;
obtaining a second predictive value for a second non-invasive diagnostic test based in part on said user profile; and
11. The computer-implemented method of aspect 10, further comprising determining the second test result based on the second predicted value and the received second signal data.
(Aspect 15)
15. The computer-implemented method of aspect 14, wherein obtaining the predictive value of the non-invasive diagnostic test comprises using the second test result to determine the predictive value of the non-invasive diagnostic test.
(Aspect 16)
15. The computer-implemented method of embodiment 14, wherein the second non-invasive diagnostic test is a cholesterol test performed concurrently with a non-invasive diagnostic test that is a glucose test.
(Aspect 17)
15. The computer-implemented method of aspect 14, wherein the non-invasive diagnostic test and the second non-invasive diagnostic test are performed by a watch comprising the one or more computing devices.
(Aspect 18)
11. The computer-implemented method of aspect 10, wherein the one or more sensors include one or more of wireless cardiac electrodes, piezoelectric vibration sensors, infrared sensors, temperature sensors, accelerometers, and microelectromechanical system (MEMS) sensors. .
(Aspect 19)
A timepiece comprising one or more computing devices and one or more storage devices, the one or more storage devices, when executed by the one or more computing devices The following actions on computer equipment:
selecting glucose and cholesterol tests based on the user profile;
identifying one or more sensors for conducting said glucose test and said cholesterol test;
activating the identified sensor or sensors;
receiving signal data via the one or more sensors;
obtaining a first predicted value for the glucose test based in part on the user profile;
determining a glucose test result and a cholesterol test result based on the first predicted value and the received signal data; and
outputting the glucose test results and the cholesterol test results via a display or speaker of the watch.
(Aspect 20)
the one or more sensors include an infrared sensor and a piezoelectric vibration sensor;
Said behavior is:
selecting a route based on raw data obtained from the received signal data; and
further comprising obtaining a set of values determined based on the selected path;
20. The watch of aspect 19, wherein determining a glucose test result based on the first predicted value and received signal data comprises determining the glucose test result based on the determined value.

Claims (16)

In the system:
a first set of one or more infrared laser sensors configured to detect one or more user characteristics;
coupled to the first set of one or more infrared laser sensors and configured to digitize one or more analog signals from the first set of one or more infrared laser sensors one or more analog-to-digital converters;
one or more computing devices ; and one or more storing instructions that, when executed by the one or more computing devices, cause the one or more computing devices to perform processes. Storage device;
with
The process is
receiving data indicating a request to perform a first non-invasive diagnostic test to detect a medical condition of the user;
communicating with one or more computer networks having one or more databases to obtain information related to the user and creating a user profile including demographic data for the user ;
controlling the first set of the one or more infrared laser sensors to respectively emit one or more infrared signals toward the user 's body part;
receiving one or more infrared signals reflected from a body part of the user using the first set of one or more infrared laser sensors ;
digitizing the received one or more infrared signals using the one or more analog-to-digital converters to generate first digitized raw data;
selecting, from among a plurality of data sets stored in the one or more storage devices, a first particular data set corresponding to the first digitized raw data , the plurality of each of the data sets associates a predetermined range of the first digitized raw data with a first predictive value and a first slope regression value of the first non-invasive diagnostic test; said step ;
communicating with the one or more computer networks to obtain the first predictive value of the first non-invasive diagnostic test of the user , wherein the first predictive value corresponds to the demographic data ; corresponding to the possible values of the first non-invasive diagnostic test of
determining the value of the first non-invasive diagnostic test for the user based on the first predictive value and the first slope regression value of the first specified data set; and displaying. or outputting the value of the first non-invasive diagnostic test via a speaker;
The system , comprising: 2. The system of claim 1 , wherein the first non-invasive diagnostic test comprises one or more of a glucose test, a cholesterol test, a hemoglobin test, and an oxygen saturation level test. Said processing is:
obtaining the user's clinical data from said one or more databases;
determining a range of a clinical data set based on the obtained user clinical data and the user demographic data; and mapping the range of the clinical data set to the plurality of data sets ; The system of Claim 1. Said processing is:
determining a second non-invasive diagnostic test to be performed based on the user's medical history;
activating the second set of one or more infrared laser sensors based on the second non-invasive diagnostic test;
receiving one or more infrared signals reflected from a body part of the user using the second set of one or more infrared laser sensors ;
digitizing the received one or more infrared signals using the one or more analog-to-digital converters to produce second digitized raw data;
selecting, from among the plurality of data sets stored in the one or more storage devices, a second particular data set corresponding to the second digitized raw data; Each of the plurality of data sets associates a predetermined range of the second digitized raw data with a second predictive value and a second slope regression value of the second noninvasive diagnostic test. , said step ;
communicating with said one or more computer networks to obtain said second predictive value of said second non-invasive diagnostic test of said user, said second predictive value being based on said demographic data; corresponding to the possible value of said second non-invasive diagnostic test of;
determining the value of the second non-invasive diagnostic test for the user based on the second predictive value and the second slope regression value of the second specific data set; 3. The system of claim 1, comprising: Obtaining the first predicted value of the first non-invasive diagnostic test of the user comprises performing the first non-invasive diagnostic test based on the value of the second non-invasive diagnostic test of the user. 5. The system of claim 4 , comprising determining the first predicted value of . said first non-invasive diagnostic test is a glucose test;
5. The system of claim 4 , wherein said second non-invasive diagnostic test is a cholesterol test performed sequentially with said first non-invasive diagnostic test. The system of claim 1, wherein the system includes a clock configured to perform the first non-invasive diagnostic test and the second non-invasive diagnostic test, the clock comprising the one or more computing devices. Item 4. The system according to item 4 . A computer-implemented method comprising:
receiving data indicating a request to perform a first non-invasive diagnostic test to detect a medical condition of the user at a device configured to be worn by the user;
a user, by means of one or more computer devices at the device , communicating with one or more computer networks having one or more databases to obtain information relating to the user, including demographic data of the user; creating a profile ;
controlling , by the one or more computer devices in the device, a first set of one or more infrared laser sensors to respectively emit one or more infrared signals toward the user's body part; ;
receiving one or more infrared signals reflected from a body part of the user using the first set of one or more infrared laser sensors;
digitizing the received one or more infrared signals using one or more analog-to-digital converters in the device to generate first digitized raw data;
selecting, by the one or more computing devices , a first specific data set corresponding to the first digitized raw data from among a plurality of data sets stored in one or more storage devices; selecting, each of said data sets comprising a predetermined range of said first digitized raw data and a first predictive value of said first non-invasive diagnostic test and a first slope regression; the step of associating a value with ;
communicating by the one or more computing devices with the one or more computer networks to obtain the first predictive value of the first non-invasive diagnostic test of the user ; said step , wherein said first predictive value corresponds to a possible value of said first non-invasive diagnostic test of said demographic data ;
performing, by the one or more computing devices, the first non-invasive diagnostic test of the user based on the first predictive value and the first slope regression value of the first specified data set; and outputting , by the one or more computing devices, the value of the first non-invasive diagnostic test via a display or speaker on the device. . 9. The computer-implemented method of Claim 8 , wherein the first non-invasive diagnostic test comprises one or more of a glucose test, a cholesterol test, a hemoglobin test, and an oxygen saturation level test. obtaining the user's clinical data from the one or more databases;
determining a range of a clinical data set based on the obtained clinical data of the user and the demographic data of the user; and mapping the range of the clinical data set to the plurality of data sets . 9. The computer-implemented method of claim 8 . determining a second non-invasive diagnostic test to be performed based on the user's medical history;
activating the second set of one or more infrared laser sensors based on the second non-invasive diagnostic test;
receiving one or more infrared signals reflected from a body part of the user using the second set of one or more infrared laser sensors;
digitizing the received one or more infrared signals using the one or more analog-to-digital converters to produce second digitized raw data;
selecting, from among the plurality of data sets stored in the one or more storage devices, a second particular data set corresponding to the second digitized raw data; Each of the plurality of data sets associates a predetermined range of the second digitized raw data with a second predictive value and a second slope regression value of the second noninvasive diagnostic test. , said step ;
communicating with said one or more computer networks to obtain said second predictive value of said second non-invasive diagnostic test of said user, said second predictive value being based on said demographic data; corresponding to the possible value of said second non-invasive diagnostic test of ;
determining the value of the second non-invasive diagnostic test for the user based on the second predictive value and the second slope regression value of the second specific data set; 9. The computer-implemented method of claim 8 , comprising: Obtaining the first predicted value of the first non-invasive diagnostic test of the user comprises performing the first non-invasive diagnostic test based on the value of the second non-invasive diagnostic test of the user. 12. The computer-implemented method of claim 11 , comprising determining the first predicted value of . said first non-invasive diagnostic test is a glucose test;
12. The computer-implemented method of claim 11, wherein said second non-invasive diagnostic test is a cholesterol test performed sequentially with said first non-invasive diagnostic test. 12. The computer-implemented method of claim 11 , wherein the first non-invasive diagnostic test and the second non-invasive diagnostic test are performed by a watch comprising the one or more computing devices. A non-transitory computer-readable storage medium that stores instructions that, when executed by one or more computers, cause the one or more computers to perform processes,
The process is
receiving data indicating a request to perform a first non-invasive diagnostic test to detect a medical condition of the user at a device configured to be worn by the user;
communicating with one or more computer networks having one or more databases to obtain information related to the user and creating a user profile including demographic data for the user ;
controlling a first set of one or more infrared laser sensors of the device to emit one or more infrared signals respectively toward the user 's body part;
receiving one or more infrared signals reflected from a body part of the user using the first set of one or more infrared laser sensors;
digitizing the received one or more infrared signals using one or more analog-to-digital converters in the device to generate first digitized raw data;
selecting, from among a plurality of data sets stored in the one or more storage devices, a first particular data set corresponding to the first digitized raw data, the plurality of each of the data sets associates a predetermined range of the first digitized raw data with a first predictive value and a first slope regression value of the first non-invasive diagnostic test; said step ;
communicating with the one or more computer networks to obtain the first predictive value of the first non-invasive diagnostic test of the user, wherein the first predictive value corresponds to the demographic data; corresponding to the possible values of the first non-invasive diagnostic test of
determining a value of said first non-invasive diagnostic test for said user based on said first predicted value and said first slope regression value of said first particular data set; and said apparatus; outputting the value of the first non-invasive diagnostic test by a display or speaker in
the non-transitory computer-readable storage medium comprising: Said processing is:
determining a second non-invasive diagnostic test to be performed based on the user's medical history;
activating the second set of one or more infrared laser sensors based on the second non-invasive diagnostic test;
receiving one or more infrared signals reflected from a body part of the user using the second set of one or more infrared laser sensors ;
digitizing the received one or more infrared signals using the one or more analog-to-digital converters to produce second digitized raw data;
selecting, from among the plurality of data sets stored in the one or more storage devices, a second particular data set corresponding to the second digitized raw data; Each of the plurality of data sets associates a predetermined range of the second digitized raw data with a second predictive value and a second slope regression value of the second noninvasive diagnostic test. , said step ;
communicating with said one or more computer networks to obtain said second predictive value of said second non-invasive diagnostic test of said user, said second predictive value being based on said demographic data; corresponding to the possible value of said second non-invasive diagnostic test of ;
determining the value of the second non-invasive diagnostic test for the user based on the second predictive value and the second slope regression value of the second specific data set; 16. The non-transitory computer-readable storage medium of claim 15 , comprising:

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