CN111787856A - wearable diagnostic device - Google Patents

Abstract

A wearable diagnostic device worn on a user's body that is capable of providing various types of health-related information to the user is described. The wearable diagnostic device is capable of providing real-time, non-invasive, accurate, and continuous data about the user's heart rate, hemoglobin level, body temperature, oxygen level, glucose level, and blood pressure. In some embodiments, the wearable diagnostic device can also provide electrocardiogram (EKG) data or detection of Parkinson's symptoms, such as involuntary movements of the user's hands. The user can configure the wearable diagnostic device to provide information about one, more, or all of the health-related information shown above.

Description

wearable diagnostic device

background

As individuals become more health conscious, there is a growing need for users to receive personal health information in a simple and convenient way. Often, a user may have to wear multiple devices, each of which provides information about a single aspect of the user's health. There is a need for a device that can provide information about various aspects or a user's health in a simple, non-invasive and convenient manner.

Overview

In general, the innovative aspects of the present subject matter describe wearable diagnostic devices and methods and systems for performing one or more medical diagnostic tests.

In some embodiments, a system includes one or more computer devices and one or more storage devices storing instructions that, when executed by the one or more computer devices, cause the one or more computer devices to perform operations . The operations include receiving input corresponding to a request to initiate a non-invasive diagnostic test to detect a medical condition of a user; determining one or more sensors for performing the non-invasive diagnostic test; based on the non-invasive diagnostic test activating the determined one or more sensors; receiving signal data via the one or more sensors; obtaining a predicted value for the non-invasive diagnostic test based in part on user characteristics; determining a test based on the predicted value and the received signal data results; and outputting the test results through a display or speaker.

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

In some embodiments, the operations further include selecting a path based on raw data obtained from the received signal data, and obtaining a set of determined values based on the selected path. Determining 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 embodiments, the operations further include obtaining user clinical data and user demographic data from one or more databases; determining a clinical dataset scope based on the obtained user clinical data and user demographic data; and converting the clinical data Set ranges mapped to predicted values for non-invasive diagnostic tests.

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

In some embodiments, 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 embodiments, when the non-invasive diagnostic test is a glucose test, the second non-invasive diagnostic test is a cholesterol test performed concurrently therewith.

In some embodiments, the non-invasive diagnostic test and the second non-invasive diagnostic test are performed by a watch including one or more computer devices.

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

According to aspects of the disclosed subject matter, a watch includes one or more computer devices and one or more memory devices storing instructions that, when executed by the one or more computer devices, cause the one or more computer devices to The computer device performs the operation. The operations include selecting a glucose test and a cholesterol test based on user characteristics; determining one or more sensors for performing a glucose test and a cholesterol test; activating the determined one or more sensors; receiving signal data via the one or more sensors obtaining a first predicted value of a glucose test based in part on user characteristics; determining a glucose test result and a cholesterol test result based on the first predicted value and the received signal data; and outputting the glucose test result and the cholesterol test through a display or speaker of the watch result.

In some embodiments, the one or more sensors include infrared sensors and piezoelectric vibration sensors. Operations include selecting a path based on raw data obtained from received signal data, and obtaining a set of determined values based on the selected path. 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-described aspects and embodiments described further in this specification 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 symptoms, such as involuntary movements of the user's hands. Wearable diagnostic devices with wireless electrodes can record user history and medical conditions, and can utilize predictive values, algorithms and mapping databases to provide greater accuracy and reliable results for glucose, cholesterol and/or hemoglobin tests. The predicted value includes the user's clinical and demographic information, and thus can make calculations more accurate that take into account parameters such as skin color and age that may affect glucose or hemoglobin levels. The predicted value can also be used to correlate the predicted value with the user's susceptibility to certain diseases.

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

The details of one or more aspects described in this specification 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 description, drawings, and claims.

Brief Description of Drawings

1, 2, 3, 4 and 5 depict exemplary embodiments of wearable diagnostic devices.

6 depicts an exemplary system implemented in a wearable diagnostic device.

7 depicts a flowchart of an exemplary method of determining a user's glucose level.

8 depicts a flowchart of an exemplary method of determining a predicted value of glucose.

9 depicts a flowchart of an exemplary method of determining a user's hemoglobin level.

10 depicts a flowchart of an exemplary method of determining a predicted value of hemoglobin.

11 depicts a flowchart of an exemplary method of determining a user's blood pressure level.

12 depicts a flowchart of an exemplary method of determining a blood pressure prediction value.

13 depicts a flowchart of an exemplary method of determining a user's cholesterol level.

The same reference numerals and numerals in the various figures denote the same elements.

detail

The present disclosure generally relates 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 embodiments, the wearable diagnostic device can perform one or more medical diagnostic tests and provide real-time, non-invasive information on the user's heart rate, hemoglobin level, body temperature, oxygen level, glucose level, cholesterol and blood pressure , accurate and continuous data. In some embodiments, the wearable diagnostic device may also provide electrocardiogram (EKG) data and detection of Parkinson's symptoms, such as involuntary movements of the user's hands. The wearable diagnostic device may be configured by a user to provide information regarding one or more of the aforementioned diagnostic tests. Embodiments of the wearable diagnostic device are described below with reference to the accompanying drawings.

1-5 show different views of the wearable diagnostic device. In some embodiments, the wearable diagnostic device may be implemented in the form of a watch, as shown in Figures 1-5. 6 shows additional details of components included in a wearable diagnostic device according to some embodiments. Generally, wearable diagnostic devices can be implemented in various suitable shapes, forms and sizes, and can be any electronic device that can be mounted to a user's left arm and that can obtain multiple health diagnostic measurements of the user.

1-5, a wearable watch may include a housing 101, a display 102, detachable wireless cardiac electrodes 103, 113, straps 104, 109, a piezoelectric vibration sensor 105, an infrared (IR)/light/laser sensor 106, a connection 107, temperature sensor 108, control buttons 110, 111, back cover 112, strap connector 114, strap fastener 115, charging connector 116, power supply 117, outer strap layers 118, 120, insulated wire 119, accelerometer 121 , a power manager 122 , a system on chip (SoC) 123 and a power switch 124 . The wearable watch can be worn on the user's left arm 150 .

Housing 101 may include or be coupled to 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. Display 102 may be implemented using any suitable display, including, for example, a liquid crystal display (LCD), light emitting diode (LED) display, or organic LED display, to display various data. In some implementations, the display 102 may be a touch screen, such as a capacitive touch screen.

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

One or more processors in housing 101 may be implemented in a PCB or IC, and may be electrically connected to other electronic components of the wearable diagnostic device, such as memory, display 102, and wireless transceivers. For example, the processor may receive data indicative of user selections from display 102, determine the type of information requested by the user, and generate instructions to perform one or more operations based on the information requested by the user.

In some implementations, one or more processors may transmit and receive data using a wireless transceiver. Data can be sent and received between wearable diagnostic devices and auxiliary devices. The auxiliary device may be a device selected by the user, a web server, or a device with which the wearable watch is configured to communicate. For example, the user may select another device owned by the user to send data to from the wearable diagnostic device. In another example, the wearable diagnostic device may be configured to transceive data to one or more network servers according to a user request or a predetermined transceiving data schedule.

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 series 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 can 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 in a database. DBMSs typically provide the ability to query, backup and replicate, enforce rules, provide security, perform calculations, make changes and access logs, and automate optimization. A DBMS typically includes modeling languages, data structures, database query languages, and transaction mechanisms. A modeling language can be used to define the schema for each database in the DBMS in terms of database models, which can include hierarchical models, network models, relational models, object models, or some other applicable known or convenient organization. Data structures may include fields, records, files, objects, and any other applicable known or convenient structure for storing data. The DBMS may also include metadata about the stored data.

In some embodiments, the database may include a user database, which may store user information, such as the user's alias, medical history, and any medical conditions. User databases can also store data related to licenses, rights, and credentials used to access various tools and software. In some cases, the user may select an option to anonymize user data, such that any information that can identify the user is anonymized and user-identifying information is removed before storing the data in the user database.

In general, various suitable wireless protocols can be used for transmitting 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 communications. In general, various types of networks can be communicated and various communication protocols can be used.

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

The housing 101 also includes a power switch 124 . In some implementations, in response to selection of power switch 124 that places power switch 124 in the power-off position, one or more processors may send instructions to power manager 122 to stop supplying one or more of the wearable diagnostic devices Components such as display 102 provide power. In some implementations, in response to selection of power switch 124 that places power switch 124 in the power-on position, power manager 122 may send instructions to power source 117 to provide one or more components of the wearable diagnostic device, such as display 102 , electricity.

The housing 101 is connected to straps 104, 109, a connector 107, and a strap connector 114 that can be adjusted to secure the wearable diagnostic device around wrists of different sizes. For example, straps 104, 109 and strap connector 114 may wrap around the user's wrist, and strap fasteners 115 may hold straps 104, 109 and strap connector 114 in a fixed position. The back cover 112 and the cardiac electrodes 113 may be provided on the bottom of the housing 101 .

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

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

The wireless cardiac electrodes 103, 113 may comprise metallic conductive materials configured to detect cardiac electrical potential waveforms, such as the voltages generated during cardiac contractions. The wireless cardiac electrodes 103 are disposed on the outer tape layer 118 corresponding to the outer periphery of the wearable diagnostic device. The wireless cardiac electrodes 113 are disposed on or integrated in the back cover 112 so that when the wearable diagnostic device is fixed around the user's arm 150, it can contact the user's skin. The wireless cardiac electrodes 103, 113 can be detached and reconnected to the wearable diagnostic device and can transmit data wirelessly to another electronic device.

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

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

Piezoelectric vibration sensor 105 and accelerometer 121 may be used to measure the vibration of the user's hand. For example, the accelerometer 121 can detect changes in orientation, velocity, vibration, and rotation. The temperature sensor 108 may be used to measure the user's body temperature. The temperature sensor 108 may be implemented in various suitable manners. For example, temperature sensor 108 may be a thermocouple including one or more sensing resistors, a silicon bandgap sensor, a thermometer, or a thermistor. Changes in resistance in the sense resistor may correspond to changes in body temperature. In some embodiments, temperature sensors may include active and passive components as well as resistive and semiconductor materials fabricated in a single ICs package.

The sensor may be used to obtain one or more measurements as described in further detail below. Operations performed using the wearable diagnostic device to determine glucose levels, hemoglobin levels, blood pressure levels, EKG, heart rate levels, body temperature, and hand vibrations are described in further detail below. The wearable diagnostic device is placed by the user on the user's left arm 150 and adjusts one or more of the straps 104, 109, strap connectors 114, strap fasteners 115, and outer strap layers 118, 120 to place the wearable diagnostic device on the user's left arm 150. The type diagnostic device is fixed around the user's left arm 150 to start operation.

When the wearable diagnostic device is secured on the user's left arm 150, the IR/light/laser sensor 106 may be in contact with the lower part of the user's left wrist just above the user's radioulnar artery. As mentioned above, the IR/light/laser sensor 106 may include a signal generator to generate and transmit infrared signals. Signals may be transmitted from the IR/light/laser sensor 106 to the user's skin on the lower part of the user's left wrist.

The IR/light/laser sensor 106 can detect signal reflections from the user's skin. The detection signal, including the absorption spectrum data, can then be converted to a digital signal using an analog-to-digital converter (ADC). As a result of the conversion, raw digital data corresponding to the signal received from the user's skin is produced. 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, 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 embodiments, multiple signal measurements may be obtained to obtain multiple sets of raw digital data at various points in time. Different sets of raw digital data can be accumulated and averaged to generate a single set of raw data for the user.

In some embodiments, the wearable diagnostic device can obtain information related to the user's health. Information related to the user's health may include, but is not limited to, for example, the user's residential location, the user's physician, the user's pharmacist, the user's medical record holder, the user's demographic association with one or more groups, User's diet, indication of multiple children the user has, user's medical history, one or more past or current medical conditions of the user (eg, allergies, surgery, genetic conditions), one or more of the user's health question and one or more blood levels for which the user is interested in obtaining detailed information. For example, the user may indicate that the user is particularly interested in the user's glucose or blood pressure levels. The user may also indicate how often the user would like to obtain information about the user's glucose or blood pressure levels.

Information related to user health can be used to create user profiles. User characteristics may be stored locally in the wearable diagnostic device, or in a database or server remote from the wearable diagnostic device. In some embodiments, the user profile data may be processed in one or more ways to remove personally identifiable information prior to storage or use. For example, the user's identity may be processed such that no personally identifiable information about the user can be determined, or the user's geographic location, identity, or demographic background may be generalized to the extent desired by the user such that specific details of the user cannot be determined. Therefore, users can control what information is collected and how it is used. To accomplish this, the user may be provided with controls through the wearable diagnostic device that allow the user to select whether or when the systems, procedures or features described herein may collect or provide user information.

In some embodiments, a user may subscribe or opt-in to a service through which the wearable diagnostic device may receive and update the user's medical information in real time. In particular, wearable diagnostic devices can update test results, doctor visits, diagnoses or prescriptions. For example, a user may authorize the user's doctor, pharmacist, or medical record holder to publish the user's medical information to a server or database that stores the user's characteristics. This server or database can be managed by a subscription service that can gather information from various sources to update the user's characteristics. The wearable diagnostic device may receive updates about the user's medical information in real time, periodically, or upon user request.

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

It should be understood that although the features described with respect to FIGS. 1-6 relate to a wearable diagnostic device worn on the left arm of a user, the wearable diagnostic device may take various other forms and, in some cases, may be worn or Apply 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 embodiments, a wearable diagnostic device may include one or more speakers to output information using sound waves and a microphone to receive audio input corresponding to, for example, user instructions, requests, or feedback.

In some embodiments, the wearable diagnostic device may also be configured to perform one or more diagnostic tests to obtain information related to the user wearing the wearable diagnostic device based on received or obtained information related to the user's health one or more of glucose levels, hemoglobin levels, or blood pressure levels. These processes are further described in Figures 7-12.

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), a hemoglobin test (902), or a cholesterol test (1302) should be performed. The indication that a test should be performed may include one or more of: a user's selection to perform the test; and a request by one or more processors based on a predetermined time. For example, a wearable diagnostic device can be programmed to provide blood pressure readings at certain times or after certain periods of time. The wearable diagnostic device can then schedule when a specific reading should be provided to the user, such as the date and time of a blood pressure reading, and can begin the method of determining blood pressure and oxygen saturation levels at a predetermined time.

After receiving an indication that a test should be performed (702, 902, 1302), the one or more processors of the wearable diagnostic device may determine the type of sensor for testing and activate the determined sensor type, which is in glucose or hemoglobin In the case of testing, one or more IR/light/laser sensors (704, 904) may be included. In the case of cholesterol testing, the activated sensors may include one or more of IR/light/laser sensors, impedance sensors, and electromagnetic sensors. Activating the sensor may include operations including, but not limited to, providing increased power to the sensor, preheating the sensor to transmit or receive signals (eg, IR signals), or configuring or calibrating the sensor.

The activated sensor or sensors may obtain measurements that characterize the user (706, 906, 1306). For example, one or more IR/light/laser sensors, respectively, may be configured to emit an infrared signal or a pulsed signal for a short period of time, eg, 30 seconds, and detect the reflection of the emitted signal from the user's skin. In some cases, reflection measurements and current measurements using impedance sensors can be obtained iteratively, or can be obtained under different environments, such as different pulsed powers or magnetic fields. For example, for cholesterol measurements, a first current or infrared signal measurement can be obtained without the application of a magnetic field, and one or more of the measurements can be made after generating magnetic fields of various low power strengths for a period of time (eg, 10 seconds) Other current or infrared measurements.

In the case of glucose and hemoglobin tests, the measured detected signals are processed and a particular path is selected based on the raw data values (708, 908). In particular, each detected signal includes absorption spectral data, which 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, mixing, and any other suitable signal processing operations.

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

Table I

The obtained predicted value and slope regression value can then be used to determine the user's glucose level (712). To determine glucose levels, the wearable diagnostic device can apply generalization theory and G-slope regression to the raw data set and determine a value indicative of glucose levels using [Equation 1] as shown below.

Glucose value = predicted value _{(G - clinical sampling regression)} - (G regression slope) * (raw data) [Equation 1]

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

When performing a hemoglobin test, the examples shown in Table II can be used to select a specific path and corresponding predicted and slope regression values. If the raw value was equal to or greater than 500 and less than or equal to 700, Path A was chosen with a corresponding predicted value of hemoglobin (Hb) of 31.5 and a regression value of the slope of Hb of 0.114. If the raw value is equal to or greater than 701 and less than or equal to 850, then path B is chosen with the corresponding Hb predicted value of 67 and Hb slope regression value of 0.114. If the raw value is equal to or greater than 851 and less than or equal to 950, then path C is chosen with the corresponding Hb predicted value of 81 and Hb slope regression value of 0.114. If the raw value is equal to or greater than 951 and less than or equal to 1100, then path D is chosen with the corresponding Hb predicted value of 98 and Hb slope regression value of 0.114. If the raw value is greater than or equal to 1101, then path E is chosen with a corresponding Hb predicted value of 100.5 and a Hb slope regression value of 0.114.

Table II

The obtained predicted value and slope regression value can then be used to determine the user's hemoglobin level (912). To determine the hemoglobin level, the wearable diagnostic device can apply Hb slope regression to the raw data set and use [Equation 2] as shown below to determine a value indicative of the hemoglobin level.

Hb value = ((Hb regression slope) * (raw data)) - predicted value _{(Hb - clinical sampling regression)} [Equation 2]

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

For a raw data value less than 500 in a glucose or hemoglobin test, the wearable diagnostic device can determine that the value is wrong and initiate another attempt to obtain the raw data value via one or more IR/light/laser sensors. If after three attempts a value greater than 500 cannot be obtained, the wearable diagnostic device may output an error message indicating that the user is currently unable to perform a glucose or hemoglobin test.

When a cholesterol test is performed to determine cholesterol levels (eg, HDL, LDL, triglyceride molecules) associated with the user wearing the wearable diagnostic device, the lower portion of the user's left wrist is exposed to infrared emission from the IR/light/laser sensor Signal. This area of the user's skin can also be exposed to the magnetic field for a short period of time, such as 10 or 30 seconds, after which the blood particles, with the exception of cholesterol molecules, align themselves according to the electromagnetic positive and negative polarity. Data indicative of the infrared absorption spectrum can be obtained before and after the magnetic field is applied (1306), and the obtained data can be subsequently processed (1308). Processing of the acquired data may include 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 the application of the magnetic field to produce the raw values.

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

Initially, the demographic characteristics of the subject can be recorded. Demographic characteristics may include various types of descriptive information about the subject, such as the subject's age, gender, race, color, marital status, and/or medical condition. For example, a subject may have the following demographic characteristics: 48 years old, white, widowed, suffering from skin cancer. For each subject, an invasive cholesterol test can be performed using various suitable methods, and the test results can be added to the subject characteristics.

Next, the object's current and infrared signal measurements can be obtained using the resistive sensor and infrared sensor, respectively, as described above. Measurements of current and infrared signals can be made before and after the application of the magnetic field for a certain period of time. Differences in the measured signals can be calculated and saved as raw values. Raw values were mapped to cholesterol levels obtained from invasive cholesterol tests and stored in subject characteristics.

Hundreds and thousands of subjects may be tested so that a large sample size of subjects can be used to generate a database that maps raw values to cholesterol levels according to one or more demographic categories (eg, gender, race, age, etc.). In some embodiments, statistical data can be extracted from the test so that the mean, median, and mode values of cholesterol levels and raw values for each demographic category can be determined.

In general, various types of demographic categories can be formed. For example, in some cases demographic categories may be restricted by age, such as the age of people in their 40s. In some cases, a demographic category may include multiple demographic characteristics, such as age and ethnicity, or age, ethnicity, and gender. Thus, the database may include a mapping table that maps possible cholesterol levels based on raw values associated with particular demographic categories. It is important to note here that the database can be generated anonymously so that the identity of the object is not revealed or known.

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

Next, the predicted cholesterol level value may be determined as a likely cholesterol level for the user wearing the wearable diagnostic device (1312). In some embodiments, the predicted cholesterol level value may be compared to previously obtained cholesterol levels of the user. If the difference between the predicted user's cholesterol level and the last obtained user's cholesterol level is greater than a threshold, eg, 3%, the wearable diagnostic device may repeat operations 1302 to 1310 to obtain a new predicted cholesterol level. Such iterations may continue until the predicted cholesterol level value is within a threshold difference of the last obtained user's cholesterol level. In some cases, if three iterations are performed without meeting the threshold difference, the predicted cholesterol level value obtained at the third iteration may be determined as the likely cholesterol level of the user wearing the wearable diagnostic device. The determined probable cholesterol level of the user may then be displayed by the display 102 of the wearable diagnostic device.

In the above-described exemplary embodiments, predicted values are used to determine glucose, hemoglobin or cholesterol levels. The predicted value can be determined using a variety of different methods and based on several factors. Methods for obtaining predicted values for cholesterol levels are described above. Methods of obtaining predicted values for glucose and hemoglobin levels are further described with reference to FIGS. 8 and 10 .

8, in order to obtain a predicted value of the user's glucose level, the wearable diagnostic device may obtain user characteristic information (802). User characteristic information may include one or more of the following: the user's residential location, the user's physician, the user's pharmacist, the user's medical record holder, the user's demographic association with one or more groups, User's diet, indication of multiple children the user has, user's medical history, one or more past or current medical conditions of the user (eg, allergies, surgery, genetic conditions), one or more of the user's health question and one or more blood levels for which the user is interested in obtaining detailed information.

Using information from the user's characteristics, the wearable diagnostic device may determine a clinically relevant information value (cCIG) for the user's glucose based on the user's blood pressure and temperature (804). In some embodiments, the cCIG may be a matrix of values representing possible blood pressure and temperature. In general, various suitable methods can be used to obtain the user's blood pressure and temperature. In some embodiments, blood pressure may be obtained as described herein with reference to Figures 11 and 12, and temperature may be obtained using a temperature sensor included in the wearable diagnostic device. In some cases, cCIG may be determined using clinical information derived from user characteristics, such as the user's medical history, which includes data such as physician reports, past or current medical conditions, clinical diagnoses, and the like.

Next, the wearable diagnostic device may determine a clinically relevant demographic value (cLDG) for the user's glucose (806). The cLDG may be determined based in part on one or more user characteristics, such as the user's demographic group, the user's age, and other personal characteristics of the user. For example, if the raw glucose values described above are associated with a particular route, such as route A, or if the user characteristics are indicative of a particular demographic origin or characteristic of the user, the cLDG value may be correlated with the user's raw glucose value or a particular demographic The cLDG value is determined in a manner corresponding to the source or characteristic. In some implementations, if the user has agreed to provide this personal information, the cLDG may be a matrix of values representing various characteristics of the user, eg, age, diet, gender.

After obtaining the cCIG and cLDG, the wearable diagnostic device may determine the clinical dataset extent based on the cCIG and cLDG (810). The determined clinical dataset range is mapped to a glucose predicted value for the user's glucose level (812). A database storing various clinical dataset ranges and glucose predicted values can be queried with the determined clinical dataset ranges, and glucose predicted values mapped to the determined clinical dataset ranges can be returned. The predicted glucose value can then be provided and used to determine the user's glucose level (814).

10, in order to obtain a predicted value of the user's hemoglobin level, the wearable diagnostic device may obtain user characteristic information (1002). User characteristic information may include one or more of the following: the user's residential location, the user's physician, the user's pharmacist, the user's medical record holder, the user's demographic association with one or more groups, User's diet, indication of multiple children the user has, user's medical history, one or more past or current medical conditions of the user (eg, allergies, surgery, genetic conditions), one or more of the user's health question and one or more blood levels for which the user is interested in obtaining detailed information.

Using information from the user's characteristics, the wearable diagnostic device can determine a clinically relevant information value (cCIHb) for the user's hemoglobin (1004). The cCIHb may be determined using clinical information obtained from user characteristics, 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, if the user has agreed to provide this personal information, the cCIHb may be a matrix of values representing various aspects of the user's medical history.

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

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

By utilizing the predicted values in addition to the blood pressure and temperature data to determine the user's glucose and hemoglobin levels, the user's glucose and hemoglobin levels are determined to be much higher relative to methods that do not utilize the predicted values to determine the glucose and hemoglobin levels. Accuracy. The predicted value includes the user's clinical and demographic information, and thus can make calculations more accurate that take into account parameters such as skin color and age that may affect glucose or hemoglobin levels. The predicted value can also be used to correlate the predicted value with the user's susceptibility to certain diseases, and the user can thus take preventive steps to minimize the likelihood of acquiring these diseases and improve the user's health status and life expectancy. Furthermore, these multiple tests can be performed as required by a single device and the results can be obtained from the tests, thereby 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 also determine the user's blood pressure and oxygen saturation levels. A flowchart of an exemplary method of determining a user's blood pressure and oxygen saturation levels is depicted in FIG. 11 . Initially, the wearable diagnostic device may receive an indication that a blood pressure measurement should be performed (1102). The indication that a blood pressure measurement should be taken may include one or more of a selection by the user to provide a blood pressure reading and a request by one or more processors based on a predetermined time. For example, a wearable diagnostic device can be programmed to provide blood pressure readings at certain times or after certain periods of time. The wearable diagnostic device can then schedule a date and time when a blood pressure reading should be provided to the user, and the method of determining blood pressure and oxygen saturation levels can begin at a predetermined time.

Upon receiving the indication (1102) that a blood pressure measurement should be performed, the wearable diagnostic device may determine the type of sensor used for the blood pressure test, and activate the determined sensor type, which in the case of the blood pressure test, may include piezoelectric vibration Sensors and IR/Light/Laser Sensors (1104). Activating a sensor may include several operations including, but not limited to, providing increased power to the sensor and configuring or calibrating the sensor.

Activated piezoelectric vibration sensors and IR/light/laser sensors may obtain user measurements (1106). For example, piezoelectric vibration sensors are configured to sense or detect movement, position, proximity, speed, and direction of a user's hand and generate electrical voltages corresponding to one or more of detected touch, vibration, and shock movements Signal. The IR/light/laser sensor is configured to emit an infrared signal and detect the reflection of the emitted infrared signal from the user's skin. In some embodiments, 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 can be processed, for example, by performing analog-to-digital conversion (ADC) and filtering operations to generate infrared target detection (IRTD) values and piezoelectric vibration (PV) values ( 1108). In general, various signal processing operations can be performed on signals detected by piezoelectric vibration sensors and IR/light/laser sensors. In some embodiments, operations 1104-1108 may be repeated multiple times, and an average of multiple IRTD and PV values may be determined.

Processing may also include comparing the determined IRTD and PV values to predicted IRTD and PV values. The comparison yields two sets of blood pressure values. Each of the two sets of blood pressure values is averaged to generate systolic and diastolic blood pressure values, respectively (1112). Using [Equation 3] shown below, systolic and diastolic blood pressure values can be used to determine mean arterial pressure (MAP).

MAP = diastolic blood pressure + (1/3) (systolic blood pressure – diastolic blood pressure) [equation 3]

The generation of predicted values for blood pressure measurement is explained below with reference to FIG. 12 . In some embodiments, [Equation 4] may also be used to determine the oxygen saturation level of the user's blood (1110).

Oxygen Saturation Level = (( _CHbO2 )/( _CHbO2 + _CHb ))*100 [Equation 4]

In [Equation 4], _CHbO2 is equal to the concentration of oxidized _hemoglobin , and CHb is equal to the concentration of deoxygenated hemoglobin. The values of _CHbO2 and _CHb can be obtained by using an infrared sensor.

After operations 1110 and 1112, blood pressure and oxygen saturation values are output (1114). For example, in some embodiments, blood pressure and oxygen saturation values are displayed via a display. In some embodiments, the blood pressure and oxygen saturation values are output through a speaker. In some embodiments, the blood pressure and oxygen saturation values may be communicated to another electronic device using a message, such as email, SMS, or MMS. Information can be automatically generated and populated without any user input. The user may be prompted to confirm whether information on blood pressure and oxygen saturation values should be transmitted to other electronic devices.

In the above-described exemplary embodiment, the predicted value is used to determine the user's blood pressure. Predictive values can be determined using a variety of different methods and based on several factors. 12 , in order to obtain the predicted value of the user's blood pressure, the wearable diagnostic device may obtain user characteristic information ( 1202 ), as described in operations 802 and 1002 . The wearable diagnostic device can obtain the clinical and demographic information of the user from the user characteristics.

Using information from the user's characteristics, the wearable diagnostic device may determine the user's blood pressure clinically correlated information value (cCIBP) and obtain EKG data (1204). The cCIBP can be determined using clinical information obtained from user characteristics (the user's medical history), which includes data such as doctor's reports, past or current medical conditions, clinical diagnoses, and the like. EKG data may be obtained from various suitable sources, such as, for example, the user's medical history.

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

After obtaining the cCIBP and cLDBP, the wearable diagnostic device may determine the clinical dataset extent based on the cCIBP and cLDBP (1208). The determined clinical dataset ranges are mapped to blood pressure predictions for the user's glucose level (1210). A database storing various clinical data set ranges and blood pressure predicted values can be queried with the determined clinical data set ranges, and blood pressure predicted values mapped to the determined clinical data set ranges can be returned. Blood pressure predictions may then be provided and used to determine the user's blood pressure level (1212).

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

When the wearable diagnostic device is fixed on the user's left arm, the first cardiac electrode may be disposed on or in the bottom surface of the wearable diagnostic device, and may be in contact with the upper side of the user's left wrist. The second cardiac electrode is disposed on or within the upper surface of the wearable diagnostic device without touching the skin on the user's left arm. The first cardiac electrode may acquire signals from the upper wrist on the user's left arm, and the second cardiac electrode may acquire both signals using a second cardiac electrode placed on the user's chest. The acquired signals may include cardiac potential waveforms, such as the voltages generated during systole. The data obtained from these three signals are converted into Provocation, Quality, Radiation, Severity, Time (PQRST) waves. PQRST waves can be used to generate Einthoven's triangle, EKG maps, and to calculate additional information, such as the user's heartbeat. In some cases, PQRST complexes can be used to diagnose cardiac conditions.

In some embodiments, the wearable diagnostic device may perform a process for obtaining a body temperature associated with a user wearing the wearable diagnostic device. After the wearable diagnostic device is wrapped around the user's arm and in contact with the user's skin, the temperature sensor in the wearable diagnostic device will obtain the user's temperature based on the skin contact. For example, a temperature sensor can touch the wrist and can obtain data on the user's body temperature over a specific period of time. The temperature sensor provides sensor data to one or more processors, which can convert the received data to a Fahrenheit temperature scale and average temperature data collected over one or more time periods to generate the user's likely body temperature.

In some embodiments, the wearable diagnostic device may perform a process for obtaining hand vibrations associated with a user wearing the wearable diagnostic device. One or more processors may perform operations using a timer and an accelerometer or piezoelectric vibration sensor to obtain hand vibration measurements. For example, upon receiving an indication that the user is interested in viewing hand vibrations, the processor may set a timer for a specific time period, such as 60 seconds, and instruct the accelerometer or piezoelectric vibration sensor to obtain vibration data for the specific time period. An accelerometer or piezoelectric vibration sensor can sense the number and intensity of the user's hand vibrations over a specific period of time and provide data to the processor indicating the number of vibrations.

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

The above-described process of obtaining information about the 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. The wearable diagnostic device has sufficient processing power to perform any of these procedures at any time and in response to user requests.

In some embodiments, multiple diagnostic tests may be run sequentially or simultaneously. For example, an EKG test can be done before or during a blood pressure test. An oxygen saturation test and a temperature test can be done before or during the hemoglobin test. Blood pressure and temperature tests can be done before or during the glucose test. Other variations and combinations are possible. For example, if the user has a medical history of certain conditions, such as diabetes, the wearable diagnostic device may periodically perform the tests most relevant to the user's condition, such as the glucose diagnostic test, and may run before, after, or during the most relevant diagnostic test Take other tests, such as blood pressure tests, cholesterol tests, or oxygen saturation tests.

In some embodiments, the wearable diagnostic device may determine one or more tests to be performed in conjunction with the user-requested diagnostic test, although the user may enter a request to perform one type of diagnostic test (eg, a glucose test), For example a blood pressure test. Additional tests may be determined based on one or more criteria, such as, for example, to be performed concurrently, sequentially, or in pairs as commonly selected by the user, default settings, manufacturer settings, physician recommendations. In some embodiments, additional tests may be determined based on the user's medical history. For example, if the user suffers from diabetes and high blood pressure, the wearable diagnostic device may also perform a glucose test whenever the user selects a blood pressure test. If the user chooses a glucose test, the wearable diagnostic device can also perform a blood pressure test.

Results obtained from performing one or more of the above-described procedures may be stored in memory in the wearable diagnostic device, or in a database or cloud account associated with the user. Results obtained from performing one or more of the above-described processes may also be displayed by a display. In some embodiments, the wearable diagnostic device may output a visual 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 audible or electronic alarms. For example, if, for example, the EKG includes some signs of abnormal heart motion or a body temperature exceeds 102 degrees Fahrenheit, the wearable diagnostic device can produce sound output, such as sound waves, to advise the user to see a doctor.

It is to be understood that the embodiments and/or actions described in this specification may be implemented in digital electronic circuits, or in computer software, firmware or hardware, including the structures disclosed in this specification and their structural equivalents, or in one or multiple combinations. Embodiments may be implemented as one or more computer program products, eg, one or more modules of computer program instructions encoded on a computer-readable medium for execution by or to control the operation of a data processing apparatus. The computer-readable medium may be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter implementing a machine-readable propagated signal, or a combination of one or more thereof. The term "data processing apparatus" encompasses all apparatus, apparatus and machines for processing data, including, for example, programmable processors, computers, or multiple processors or computers. In addition to hardware, an apparatus may include code that creates an execution environment for the computer program in question, eg, code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of these. A propagated signal is an artificially generated signal, such as a machine-generated electrical, optical or electromagnetic signal, which is generated to encode information for transmission to suitable receiving equipment.

Computer programs (also known as programs, software, software applications, scripts, or codes) may be written in any form of programming language, including compiled or interpreted languages, and may be written in any form, including as a stand-alone program or as a module, component , subroutines, or other units applicable to the computing environment) deployment. Computer programs do not necessarily correspond to files in the file system. Programs may be stored in a portion of a file that holds other programs or data (for example, one or more scripts stored in a markup language file), in a single file dedicated to the program in question, or in multiple coordination files (for example, a file that stores one or more modules, subroutines, or portions of code). A computer program can execute on a single computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communications network.

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

For example, processors in a computer for the execution of a computer program include both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Typically, a processor will receive instructions and data from read-only memory or random access memory, or both.

Although this specification contains numerous details, these should not be construed as limitations on the scope of the disclosure or as claimed, but as descriptions of specific features of particular embodiments. Certain features that are described in this specification in the context of separate implementations can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination. Furthermore, although features may be described above as functioning in certain combinations and may even be claimed in this regard, in some cases one or more features from a claimed combination may be removed from that combination and the claimed combination A protected combination may involve a subcombination or a variation of a subcombination.

It is to be understood that the phrase "one or more/one or more of" and the phrase "at least one(s) of" include any combination of elements. For example, the phrase "one or more/one or more of A and B" includes A, B, or A and B. Similarly, the phrase "at least one (species) of A and B" includes A, B or A and B.

Accordingly, specific embodiments 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.

Claims (20)

1. A system comprising: One or more computer devices and one or more storage devices that store instructions that, when executed by the one or more computer devices, cause the one or more computer devices to perform operations comprising: receiving input corresponding to a request to initiate a non-invasive diagnostic test to detect a user's medical condition; determining one or more sensors for performing the non-invasive diagnostic test; activating the determined one or more sensors based on the non-invasive diagnostic test; receive signal data via the one or more sensors; obtaining a predictive value for the non-invasive diagnostic test based in part on user characteristics; determining a test result based on the predicted value and the received signal data; and The test results are output through a display or speaker. 2. The system of claim 1, wherein the non-invasive diagnostic tests include one or more of the following: a glucose test, a cholesterol test, a hemoglobin test, an oxygen saturation level test, and an electrocardiogram monitoring test. 3. The system of claim 1, wherein the operations further comprise: selecting a path based on raw data obtained from the received signal data; and obtain a set of determined values based on the selected path, Wherein determining the test result based on the predicted value and the received signal data includes determining the test result based on the determined value. 4. The system of claim 1, wherein the operations further comprise: Obtain user clinical data and user demographic data from one or more databases; determining a clinical dataset scope based on the obtained user clinical data and the user demographic data; and The clinical dataset range is mapped to the predictive value of the non-invasive diagnostic test. 5. The system of claim 1, wherein the operations further comprise: 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 second signal data via one or more sensors of the second set; obtaining a second predicted value for the second non-invasive diagnostic test based in part on the user characteristic; and A second test result is determined based on the second predicted value and the received second signal data. 6. The system of claim 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. 7. The system of claim 5, wherein when the non-invasive diagnostic test is a glucose test, the second non-invasive diagnostic test is a cholesterol test performed concurrently therewith. 8. The system of claim 5, wherein the non-invasive diagnostic test and the second non-invasive diagnostic test are performed by a watch including the one or more computer devices. 9. The system of claim 1, wherein the one or more sensors comprise one or more of the following: wireless cardiac electrodes, piezoelectric vibration sensors, infrared sensors, temperature sensors, accelerometers, and microelectromechanical system (MEMS) sensor. 10. A computer-implemented method comprising: receiving input corresponding to a request to initiate a non-invasive diagnostic test to detect a user's medical condition; Determining, by one or more computer devices, one or more sensors for performing the non-invasive diagnostic test; activating the determined one or more sensors based on the non-invasive diagnostic test; receive signal data via the one or more sensors; obtaining, by the one or more computer devices, a predictive value for the non-invasive diagnostic test based in part on user characteristics; determining, by the one or more computer devices, a test result based on the predicted value and the received signal data; and The test results are output by the one or more computer devices through a display or speaker. 11. The computer-implemented method of claim 10, wherein the non-invasive diagnostic tests include one or more of the following: a glucose test, a cholesterol test, a hemoglobin test, an oxygen saturation level test, and an electrocardiogram monitoring test . 12. The computer-implemented method of claim 10, wherein the operations further comprise: selecting a path based on raw data obtained from the received signal data; and obtain a set of determined values based on the selected path, Wherein determining the test result based on the predicted value and the received signal data includes determining the test result based on the determined value. 13. The computer-implemented method of claim 10, wherein the operations further comprise: Obtain user clinical data and user demographic data from one or more databases; determining a clinical dataset scope based on the obtained user clinical data and the user demographic data; and The clinical dataset range is mapped to the predictive value of the non-invasive diagnostic test. 14. The computer-implemented method of claim 10, wherein the operations further comprise: A second non-invasive diagnostic test to be performed is determined based on (i) a user profile 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 second signal data via one or more sensors of the second set; obtaining a second predicted value for the second non-invasive diagnostic test based in part on the user characteristic; and A second test result is determined based on the second predicted value and the received second signal data. 15. The computer-implemented method of claim 14, wherein obtaining a predictive value for the non-invasive diagnostic test comprises using the second test result to determine a predictive value for the non-invasive diagnostic test. 16. The computer-implemented method of claim 14, wherein when the non-invasive diagnostic test is a glucose test, the second non-invasive diagnostic test is a concurrent cholesterol test. 17. The computer-implemented method of claim 14, wherein the non-invasive diagnostic test and the second non-invasive diagnostic test are performed by a watch including the one or more computer devices. 18. The computer-implemented method of claim 10, wherein the one or more sensors include one or more of the following: wireless cardiac electrodes, piezoelectric vibration sensors, infrared sensors, temperature sensors, accelerometers, and Micro Electro Mechanical Systems (MEMS) sensors. 19. A watch comprising: One or more computer devices and one or more storage devices that store instructions that, when executed by the one or more computer devices, cause the one or more computer devices to perform operations comprising: Select glucose test and cholesterol test based on user characteristics; determining one or more sensors for performing the glucose test and the cholesterol test; activating the determined one or more sensors; receive signal data via the one or more sensors; obtaining a first predicted value of the glucose test based in part on the user characteristic; determining a glucose test result and a cholesterol test result based on the first predicted value and the received signal data; and The glucose test results and the cholesterol test results are output through a display or speaker of the watch. 20. The watch of claim 19, wherein the one or more sensors include an infrared sensor and a piezoelectric vibration sensor; The operations also include: selecting a path based on raw data obtained from the received signal data; and obtaining a set of determined values based on the selected path; and 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.

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