DE112017008334T5 - PORTABLE DIAGNOSTIC DEVICE - Google Patents

Abstract

A portable diagnostic device is described which is worn on the body of a user and can provide the user with various types of health-related information. The portable diagnostic device can provide non-invasive, accurate, and continuous real-time data relating to a user's heart rate, hemoglobin level, body temperature, oxygen level, glucose level and blood pressure. In some implementations, the portable diagnostic device can also provide electrocardiogram (EKG) data or the detection of Parkinson's symptoms, such as involuntary movement of a user's hand. A user can configure the portable diagnostic device to provide information on one, more, or all of the above-mentioned health-related information.

Description

BACKGROUND

As people become more aware of their health, there is a growing demand among users to obtain personal health information in a simple and convenient manner. Often times, users must wear multiple devices, each of which provides information on a single aspect of the user's health. There is a need for an apparatus that can provide information about multiple aspects or the health of a user in a simple, non-invasive, and convenient manner.

ABSTRACT

In general, innovative aspects of the article describe a portable diagnostic device and methods and systems for performing one or more medical diagnostic tests.

In some implementations, a system includes one or more computing devices and one or more storage devices that store instructions that, when executed by the one or more computing devices, cause the one or more computing devices to perform operations. The operations include: receiving an input corresponding to a request to begin a non-invasive diagnostic test to detect a medical health condition of a user, identifying one or more sensors for performing the non-invasive diagnostic test, activating the one or more identified Sensors based on the non-invasive diagnostic test, receiving signal data via the one or more sensors, obtaining a predicted value for 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 a loudspeaker.

Implementations can each optionally include one or more of the following features. For example, in some implementations, the non-invasive diagnostic test includes a glucose test and / or a cholesterol test and / or a hemoglobin test and / or an oxygen saturation level test and / or an electrocardiogram monitoring test.

In some implementations, the operations further include selecting a path based on raw data obtained from the received signal data and obtaining a set of particular 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 values.

In some implementations, the operations further include obtaining user clinical data and user demographic data from one or more databases, determining a clinical record range based on the obtained clinical user data and user demographic data, and mapping the clinical record range to the predicted value for the non-invasive diagnostic test.

In some implementations, the operations further include determining a second non-invasive diagnostic test to be performed based on (i) a user pattern for associating the second non-invasive diagnostic test with the non-invasive diagnostic test and (ii) a medical history of the user; 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 predicted value for the second non-invasive diagnostic test based in part on the user profile; and determining a second test result based on the second predicted value and the received second signal data.

In some implementations, obtaining the predicted value for the non-invasive diagnostic test includes determining the predicted value for the non-invasive diagnostic test using the second test result.

In some implementations, the second non-invasive diagnostic test is a cholesterol test that is performed concurrently with the non-invasive diagnostic test, which is a glucose test.

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

In some implementations, the one or more sensors include a wireless cardiac electrode and / or a piezoelectric vibration sensor and / or an infrared sensor and / or a temperature sensor and / or an accelerometer and / or a microelectromechanical system (MEMS) sensor.

In accordance with aspects of the disclosed subject matter, a wristwatch includes one or more computing devices and one or more storage devices that store instructions that, when executed by the one or more computing devices, cause the one or more computing devices to perform operations. The operations include selecting a glucose test and a cholesterol test based on a user profile, identifying one or more sensors for performing the glucose test and cholesterol test, activating the one or more identified sensors, receiving signaling data about the one or more sensors, receiving 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, through a display or a speaker of the wrist watch, the glucose test result and the cholesterol test result.

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 set of specific 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 values.

The above aspects and implementations, which are further described in this specification, offer several advantages. For example, a single portable diagnostic device can perform multiple non-invasive diagnostic tests, including non-invasive glucose tests, non-invasive cholesterol tests, and non-invasive hemoglobin tests. The portable diagnostic device can also receive electrocardiogram (EKG) data or the detection of Parkinson's symptoms such as involuntary movement of a user's hand. The portable wireless electrode diagnostic device can track users' medical history and illnesses and use predictive values, algorithms and mapping databases to provide higher accuracy and reliable results for glucose, cholesterol and / or hemoglobin tests. The predictive values include the clinical and demographic information of the user and can therefore more accurately perform calculations that take into account parameters such as skin color and age that can affect glucose or hemoglobin levels. The predictive values can also be used to correlate the predictive values with the susceptibility of the user to certain diseases.

Other aspects include respective methods, systems, devices, computer readable storage media, and computer programs designed to implement the acts of the above methods.

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

Figure list

• 1 , 2 , 3 , 4th and 5 illustrate example implementations of a portable diagnostic device.
• 6th FIG. 10 depicts an exemplary system implemented in a portable diagnostic device.
• 7th FIG. 10 illustrates a flow diagram of an example method for determining a user's glucose level.
• 8th FIG. 10 depicts a flow diagram of an exemplary method for determining a glucose predictive value.
• 9 FIG. 10 illustrates a flow diagram of an exemplary method for determining a hemoglobin level of a user.
• 10 FIG. 10 depicts a flow diagram of an exemplary method for determining a hemoglobin predictive value.
• 11 FIG. 10 illustrates a flow diagram of an exemplary method for determining a blood pressure level of a user.
• 12 FIG. 10 illustrates a flow diagram of an exemplary method for determining a blood pressure prediction value.
• 13 FIG. 10 illustrates a flow diagram of an exemplary method for determining a user's cholesterol level.
• The same reference numbers and designations in the various drawings identify the same elements.

DETAILED DESCRIPTION

This disclosure relates generally to a handheld diagnostic device that can be worn on a user's body to provide various types of health-related information to the user. In some implementations, the portable diagnostic device can perform one or more medical diagnostic tests and provide non-invasive, accurate, and continuous real-time data relating to a user's heart rate, hemoglobin level, body temperature, oxygen level, glucose level, cholesterol, and blood pressure. In some implementations, the portable diagnostic device can also provide electrocardiogram (EKG) data and the detection of Parkinson's symptoms such as involuntary movement of a user's hand. A user can configure the portable diagnostic device to provide information about one or more of the diagnostic tests mentioned above. Implementations of the portable diagnostic device are described below with reference to the figures.

The 1-5 FIG. 13 illustrates various views of a portable diagnostic device. In some implementations, the portable diagnostic device may be implemented in the form of a wristwatch, as in FIG 1-5 shown. 6th Figure 10 illustrates additional details of the components included in the portable diagnostic device according to some implementations. In general, the portable diagnostic device can be implemented in various suitable shapes, shapes, and sizes and can be any electronic device that can be attached to a user's left arm and is capable of obtaining multiple health diagnostic measurements for the user.

Referring to the 1-5 the portable watch can have a case 101 , an ad 102 , removable wireless heart electrodes 103 , 113 , a strap 104 , 109 , a piezo vibration sensor 105 , an infrared (IR) / light / laser sensor 106 , a lanyard 107 , a temperature sensor 108 , Control buttons 110 , 111 , a back cover 112 , a belt lanyard 114 , a strap fastener 115 , a charging port 116 , a power supply 117 , an outer belt layer 118 , 120 , an insulated wire 119 , an accelerometer 121 , a performance manager 122 , a system-on-chip (SoC) 123 and a circuit breaker 124 include. The portable watch can be worn on a left arm 150 worn by a user.

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

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

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

In some implementations, one or more processors can transmit and receive data using a wireless transceiver. Data can be transmitted and received between the portable diagnostic device and a secondary device. The secondary device can be a user selected device, a network server, or a device configured to communicate with the wearable wristwatch. For example, the user can select another device owned by the user to which data from the portable diagnostic device is to be sent. In another example, the portable diagnostic device may be configured to transmit and receive data according to a user request or a predetermined schedule for transmitting and receiving data to / from one or more network servers.

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

The databases in the networks can include a cloud database or a database 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 often provide the ability to query, secure, and replicate rules, enforce rules, provide security, perform calculations, perform change and access logs, and automate optimization. A DBMS typically includes a modeling language, a data structure, a database query language, and a transaction mechanism. The modeling language is used to define the schema of each database in the DBMS according to the database model, which may include a hierarchical model, a network model, a relational model, an object model, or any other applicable known or useful organization. Data structures can include fields, records, files, objects, and any other known or convenient structure for storing data. A DBMS can also contain metadata about the stored data.

In some implementations, the databases can include a user database that can store information about a user, such as an alias, a medical history, and any medical conditions of the user. The user database can also store data relating to licenses, authorizations and certifications for access to various resources and software. In some cases, the user can select an option to anonymize user data so that any information that can identify a user is anonymized and user identification information is removed before data is stored in the user database.

In general, various suitable wireless protocols can be used to communicate data to and from the portable diagnostic device. For example, the portable diagnostic device can communicate with one or more networks, one or more devices, or one or more servers via WiFi or Bluetooth communication. In general, different types of networks can be communicated with and different communication protocols can be used.

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

The case 101 also includes a circuit breaker 124 . In some implementations, in response to the selection of the circuit breaker 124 so that the circuit breaker 124 is in a power off position, one or more processors send a command to the performance manager 122 send to provide electrical power to one or more components of the portable diagnostic device, such as the display 102 , to stop. In some implementations, the performance manager can 122 in response to the selection of the circuit breaker 124 so that the circuit breaker 124 is in a switch-on position, a command to the power supply 117 send to one or more components of the portable diagnostic device, such as the display 102 to supply electrical power.

The case 101 is with the straps 104 , 109 , the lanyard 107 and the belt connecting means 114 that can be adjusted to fit the portable diagnostic device around wrists of different sizes. For example, the straps 104 , 109 and the strap connecting means 114 wrapped around a user's wrist and a strap fastener 115 can the straps 104 , 109 and the strap connecting means 114 hold in a fixed position. A back cover 112 and a heart electrode 113 can be attached to a lower portion of the housing 101 be arranged.

One or more insulated wires 119 can be integrated into the structure of the portable diagnostic device and provide an electrical connection to various components of the portable diagnostic device. For example, as in 6th Illustrated can be one or more insulated wires 119 be arranged along a central axis of the portable diagnostic device and an electrical connection between the power supply 117 and the case 101 as well as between the power supply 117 and the IR / light / laser sensor 106 provide.

The portable diagnostic device can also have one or more sensors such as cardiac electrodes 103 , 113 , a piezo vibration sensor 105 , an infrared (IR) / light / laser sensor 106 , a temperature sensor 108 , an accelerometer 121 and an impedance sensor. In some implementations, the piezo vibration sensor 105 contain a MEMS sensor (MEMS: microelectromechanical system).

The wireless heart electrodes 103 , 113 may include a metallic conductive material and are configured to detect waveforms of cardiac electrical potential, such as voltages generated during contraction of the heart. The wireless heart electrode 103 is on an outer belt layer 118 arranged, which corresponds to an outer periphery of the portable diagnostic device. The wireless heart electrode 113 is on the back cover 112 arranged or incorporated into them so that they can touch the skin of the user when the portable diagnostic device is around the arm 150 of a user is attached. The wireless heart electrodes 103 , 113 can be detached and reconnected to the portable diagnostic device, and can wirelessly communicate data 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 with a wavelength in the range of, for example, 650-1400 nanometers (nm). This area is particularly useful for obtaining data indicative of the presence of oxygen, glucose, and hemoglobin molecules in the user's blood. The signal detector can be designed to detect infrared signals received from the body of the user.

In some implementations, the IR / light / laser sensor 106 Detect pulse signals and process the detected signals using the pulse wave transit time (PWTT) in the arterial method. These pulse signals can be combined with data received by the piezo vibration sensor 105 can be recorded to predict blood pressure non-invasively. The IR / light / laser sensor 106 can also be used to determine or obtain data indicative of oxygen saturation levels (SpO ₂ ) in the user's blood non-invasively.

The piezo vibration sensor 105 and the accelerometer 121 can be used to measure vibrations of the user's hand. For example, the accelerometer 121 Detect changes in direction, speed, vibrations and rotations. A temperature sensor 108 can be used to measure the user's body temperature. The temperature sensor 108 can be implemented in various suitable ways. For example, the temperature sensor 108 a thermocouple, a silicon band gap sensor, a thermometer, or a thermistor comprising one or more measuring resistors. A change in the electrical resistance in the measuring resistors can correspond to a change in body temperature. In some implementations, temperature sensors can include active and passive components, as well as resistive and semiconductor materials, fabricated in a single package of ICs.

The sensors can be used to obtain one or more measurements, as described in more detail below. Operations performed using the portable diagnostic device to determine glucose levels, hemoglobin levels, blood pressure levels, EKG, heart rate levels, body temperature, and hand vibrations are described in more detail below. Operations begin with the user placing the portable diagnostic device on their left arm 150 the user attaches and the strap 104 , 109 and / or the belt connecting means 114 and / or the strap fastener 115 and / or the outer belt layer 118 , 120 adjusted to the portable diagnostic device around the left arm 150 to fasten the user.

When the portable diagnostic device on the left arm 150 attached to the user, the IR / light / laser sensor 106 be in contact with a lower portion of the user's left wrist just above the user's radial ulnar artery. As mentioned above, the IR / light / laser sensor 106 contain a signal generator to generate and transmit an infrared signal. The signal can come from the IR / light / laser sensor 106 transferred to the user's skin on the lower part of the user's left wrist.

The IR / light / laser sensor 106 can detect a reflection of the signal from the user's skin. The detected signal including absorption spectrum data can then be converted to a digital signal using an analog-to-digital converter (ADC). As a result of the conversion, raw digital data is generated corresponding to the signal received from the user's skin. The raw digital data can be processed to determine the absorption spectra that correspond to the presence of various molecules in the user's blood, such as oxygen, glucose, hemoglobin. By determining the absorption spectra, the presence and corresponding amounts 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. The various sets of raw digital data can be accumulated and averaged to provide a single set of raw data for the user.

In some implementations, the portable diagnostic device can obtain information associated with the health of the user. The information associated with the health of the user can be, for example, the residence of a user, a doctor of a user, a pharmacist of a user, an owner of a medical record of a user, the demographic associations of a user with one or more groups, a diet of the user, an indication a number of the user's children, a medical history of the user, one or more previous or current illnesses, e.g. allergies, surgery, genetic diseases of the user, one or more health concerns of the user and one or more blood levels about which the user would like to know details, include, but are not limited to. For example, the user can indicate that the user is particularly interested in the glucose level or blood pressure level of the user. The user can also indicate how often the user would like to receive information about the glucose level or the blood pressure level of the user.

The information associated with the health of the user can be used to create a user profile. The user profile can be stored locally in the portable diagnostic device or in a database or a server which is located remotely from the portable diagnostic device. In some implementations, user profile data can be edited in one or more ways before it is stored or used, so that personally identifiable information is removed. For example, a user's identity can be edited so that no personally identifiable information can be determined for the user, or a user's geographic location, identity, or demographic background can be generalized to the extent a user wishes so that certain details of the user cannot be determined. This gives the user control over what information is collected and how that information is used. To implement this, controls may be provided to the user via the portable diagnostic device that allows the user to choose whether or when the systems, programs, or features described herein can collect or provide user information.

In some implementations, the user can subscribe to or choose to participate in a service that allows the portable diagnostic device to receive and update the user's medical information in real time. In particular, the portable diagnostic device can be updated with test results, results of visits to the doctor, diagnoses or prescriptions. For example, the user can authorize the doctor, pharmacist, or owner of the user's medical records to share the user's medical information on a server or database in which the user profile is stored. This server or database can be managed through a subscription service that collects information from various sources to update the user profile. The portable diagnostic device can receive updates to the user's medical information in real time, periodically, or on demand.

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

It is understood that although features that are related to the 1-6 relate to the portable diagnostic device worn on the left arm of the user, the portable diagnostic device may take other different forms, and in some cases may be worn or applied to other parts of the user's body. Furthermore, one or more additional components can be contained in or coupled to the portable diagnostic device. For example, in some implementations, the portable diagnostic device may include a speaker for outputting information using a sound wave and / or a microphone for receiving audio input corresponding to, for example, a user command, request, or feedback.

In some implementations, the portable diagnostic device may further be configured to perform one or more diagnostic tests to determine a glucose level and / or a hemoglobin level and / or a blood pressure level associated with the user wearing the portable diagnostic device based on received or received Obtain information related to the user's health. These processes are in the 7-12 further described.

Referring to the 7th , 9 and 13 For example, the portable diagnostic device may receive an indication that a test should be performed (702, 902, 1302). For example, the portable diagnostic device can receive an indication that a glucose test ( 702 ), a hemoglobin test ( 902 ) or a cholesterol test ( 1302 ) should be carried out. The indication that a test should be performed may include one or more of the following: a selection by the user to perform a test; and a request by one or more processors according to a scheduled time. For example, the portable diagnostic device can be programmed such that it delivers a blood pressure measurement at specific times or after specific time periods. The portable diagnostic device can then schedule days and times when a specific measurement, such as a blood pressure measurement, is to be made available to the user, and can initiate the process of determining blood pressure and oxygen saturation levels at the scheduled times.

After receiving the indication that a test should be performed (702, 902, 1302), the one or more processors of the portable diagnostic device can determine the type of sensor used for the test and activate the particular type of sensor, which in the case of a glucose or Hemoglobin tests one or more IR / light / laser sensors ( 704 , 904 ) may contain. In the case of a cholesterol test, the activated sensors can include an IR / light / laser sensor and / or an impedance sensor and / or an electromagnetic sensor. Activating the sensors may include operations including, but not limited to, providing increased power to the sensors, the Warm up the sensors to emit or receive a signal, such as an IR signal, or configure or calibrate the sensors.

The one or more activated sensors can obtain measurements that characterize the user (706, 906, 1306). For example, the one or more IR / light / laser sensors can be designed to each transmit an infrared signal or a pulse signal for a short period of time, e.g. 30 seconds, and detect a reflection of the emitted signal from the user's skin. In some cases, reflection measurements and current measurements can be obtained iteratively using an impedance sensor or under different environments, such as different pulse 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 other current or infrared measurements can be made after applying a magnetic field of various low powers for a period of time, e.g. B. 10 seconds was generated.

In the case of glucose and hemoglobin tests, the measured detected signals are processed and based on the raw data values ( 708 , 908 ) a certain path is selected. In particular, each of the detected signals contains absorption spectra that can be converted into raw digital data using an analog-to-digital converter (ADC). The processing may optionally include additional processing operations such as downconverting, filtering, convolution, mixing, and any other suitable signal processing operation.

The raw digital data, which corresponds to the signal received from the user's skin, is used as the basis for the selection of a particular path and corresponding prediction values and slope regression values ( 710 , 910 ) is used. For example, when performing a glucose test, an exemplary map as shown in TABLE I can be used to select a particular pathway and appropriate prediction and slope regression values. If the raw values are greater than or equal to 500 and less than or equal to 700, path A and a corresponding glucose (G) prediction value of 917 and a G slope regression value of 0.838 are selected. If the raw values are greater than or equal to 701 and less than or equal to 850, path B and a corresponding G prediction value of 919 and a G slope regression value of 0.838 are selected. If the raw values are greater than or equal to 851 and less than or equal to 950, path C and a corresponding G prediction value of 1001 and a G slope regression value of 0.838 are selected. If the raw values are greater than or equal to 951 and less than or equal to 1100, path D and a corresponding G prediction value of 1004 and a G slope regression value of 0.838 are selected. If the raw values are greater than or equal to 1101, path E and a corresponding G prediction value of 981 and a G slope regression value of 0.838 are selected. TABLE I. path RAW DATA VALUE (X) G PREDICTION VALUE G SLOPE REGRESSION VALUE A. 500≤X≤700 917 0.838 B. 701≤X≤850 919 0.838 C. 851≤X≤950 1001 0.838 D. 951≤X≤1100 1004 0.838 E. 1101≤X 981 0.838

The prediction and slope regression values obtained can then be used to determine a glucose level of the user (712). To determine a glucose level, the portable diagnostic device can apply generalizability theory and G-slope regression to the set of raw data and determine values indicative of a glucose level using [Equation 1] as noted below. $$\text{Glucose level}={\text{Predictive values}}_{\left(\text{G-clinical sample regression}\right)}-\left(\text{G regression slope}\right)*\left(\text{Raw data}\right)$$

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

When performing a hemoglobin test, an exemplary map as shown in TABLE II can be used to select a particular pathway and appropriate predictive and slope regression values. If the raw scores are equal to or greater than 500 and less than or equal to 700, path A and a corresponding hemoglobin (Hb) prediction value of 31.5 and an Hb slope regression value of 0.114 are selected. If the raw scores are equal to or greater than 701 and less than or equal to 850, path B and a corresponding Hb prediction value of 67 and an Hb slope regression value of 0.114 are selected. If the raw scores are equal to or greater than 851 and less than or equal to 950, path C and a corresponding Hb prediction value of 81 and an Hb slope regression value of 0.114 are selected. If the raw scores are equal to or greater than 951 and less than or equal to 1,100, path D and a corresponding Hb prediction value of 98 and an Hb slope regression value of 0.114 are selected. If the raw values are greater than or equal to 1101, then path E and a corresponding Hb prediction value of 100.5 and an Hb slope regression value of 0.114 are selected. TABLE II path RAW DATA VALUE (X) Hb FORECAST VALUE Hb SLOPE REGRESSION VALUE A. 500≤X≤700 31.5 0.114 B. 701≤X≤850 67 0.114 C. 851≤X≤950 81 0.114 D. 951≤X≤1100 98 0.114 E. 1101≤X 100.5 0.114

The prediction and slope regression values obtained can then be used to determine a hemoglobin level of the user (912). To determine a hemoglobin level, the portable diagnostic device may apply an Hb slope regression to the set of raw data and determine values indicative of a hemoglobin level using [Equation 2], as noted below. $$\text{Hb value}=\left(\left(\text{Hb regression slope}\right)*\left(\text{Raw data}\right)\right)-{\text{Prediction values}}_{\left(\text{Hb clinical sample regressio}\right)\text{n}}$$

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

For raw data values that are less than 500 in the glucose or hemoglobin test, the portable diagnostic device can determine that the values are incorrect and initiate another attempt to obtain the raw data values via the one or more IR / light / laser sensors. If after three attempts no values above 500 can be obtained, the portable diagnostic device can display an error message indicating to the user that a glucose or hemoglobin test cannot currently be performed.

When a cholesterol test is performed to determine the levels of cholesterol (e.g. HDL, LDL, triglyceride molecules) associated with the user wearing the portable diagnostic device, a lower portion of the user's left wrist will be compared to the same as measured by the IR / Light / laser sensor exposed to emitted infrared signal. This area of the user's skin can also be used for a short period of time, e.g. B. 10 or 30 seconds, are exposed to a magnetic field, after which blood particles with the exception of cholesterol molecules arrange themselves according to the positive and negative pole of the electromagnetic element. Data indicative of the infrared absorption spectra can be obtained before and after the application of the magnetic field (1306), and the obtained data can then be processed (1308). The processing of the received data can include converting the data into a digital signal using an analog Digital converter (ADC) and calculating a difference between the absorption spectra values before and after the application of the magnetic field to obtain a raw value.

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

First, a subject's demographic profile can be recorded. The demographic profile may contain various types of descriptive information about the subject, such as the subject's age, gender, ethnicity, skin color, marital status, and / or illnesses. For example, a subject may have a demographic profile of a widowed 48-year-old Caucasian male with skin cancer. An invasive cholesterol test can be performed for each subject using a variety of suitable methods, and the test results can be added to the subject profile.

Next, current and infrared signal measurements of the subject can be obtained using resistive and infrared sensors, respectively, as described above. The current and infrared signal measurements can be taken before a magnetic field is applied and for a period of time after a magnetic field is applied. Differential values of the measured signals can be calculated and saved as raw values. The raw values are mapped to cholesterol levels that were obtained from the invasive cholesterol tests and are stored in the subject profile.

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

In general, different types of demographic classes can be formed. For example, in some cases a demographic class may be restricted by age, e.g. B. the age of people in their forties. In some cases, a demographic class can include multiple demographic characteristics, such as age and ethnicity or age, ethnicity and gender. Accordingly, the database may contain a mapping table that maps the likely cholesterol levels based on the raw values associated with particular demographic classes. It is noted here that the database can be created anonymously so that the identity of subjects is neither revealed nor known.

Referring again to 13 gets the predicted cholesterol level from the database based on the in operation 1308 obtained raw values obtained (1310). In particular, a cholesterol level that is too high in surgery 1308 obtained raw score is mapped, and a demographic profile of the user wearing the portable diagnostic device is obtained with reference to the mapping table in the database.

Next, the predicted cholesterol level can be determined as the likely cholesterol level of the user wearing the portable diagnostic device (1312). In some implementations, the predicted cholesterol level 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, e.g. B. 3%, the portable diagnostic device can perform the operations 1302 to 1310 repeat to get a new predicted cholesterol level. Such iteration can continue until a predicted cholesterol level is within a threshold difference from the user's most recently obtained cholesterol level. In some cases, if three iterations are performed without meeting the threshold difference, the predicted cholesterol value obtained on the third iteration can be determined as the likely cholesterol level of the user wearing the portable diagnostic device. The certain probable cholesterol level of the user can then be viewed through the display 102 of the portable diagnostic device.

In the exemplary implementations above, predictive values were used to determine glucose, hemoglobin, or cholesterol levels. The predictive values can be determined using several different methods and based on several factors. The method for obtaining predictive values for cholesterol levels is described above. The methods for obtaining predictive values for glucose and hemoglobin levels are described in relation to the 8th and 10 further described.

Referring to 8th to obtain a predictive value for a user's glucose level, the portable diagnostic device may obtain user profile information (802). The user profile information may include a user's location, a user's doctor, a user's pharmacist, an owner of a user's medical record, a user's demographic associations with one or more groups, a user's diet, an indication of a number of the user's children, a medical history of the user, one or more past or present illnesses, for example allergies, surgery, genetic diseases of the user, one or more health concerns of the user, and one or more blood levels about which the user wishes to know details.

Using information from the user profile, the portable diagnostic device can evaluate the value of clinical correlation information for glucose (cCIG) to the user ( 804 ) based on a blood pressure and a temperature of the user. In some implementations, the cCIG can be a matrix of values representative of the likely blood pressure and temperature. In general, various suitable methods can be used to obtain the blood pressure and temperature of the user. In some implementations, blood pressure can be obtained, as referenced in this specification 11 and 12 and the temperature can be obtained using a temperature sensor included in the portable diagnostic device. In some cases, the cCIG can be determined using clinical information obtained from the user profile, such as the user's medical history, which includes data such as a doctor's report, past or present illnesses, clinical diagnoses, and so on.

Next, the portable diagnostic device may determine the clinical linked demographics value for glucose (cLDG) to the user (806). The cLDG may be determined based in part on one or more user characteristics, such as a demographic of the user, an age of the user, and other personal characteristics of the user. For example, if the crude glucose value noted above is associated with a particular pathway, such as path A, or the user profile indicates a particular demographic origin or profile of the user, the cLDG value can be determined in a manner that the cLDG value corresponds to the raw glucose level or the specific demographic origin or profile of the user. In some implementations, the cLDG can be a matrix of values representative of various characteristics, e.g. B. Age, diet, gender of the user, if the user has agreed to provide this personal information.

Upon receipt of the cCIG and cLDG, the portable diagnostic device can determine a clinical record range based on the cCIG and cLDG (810). The determined clinical record area is mapped into a glucose prediction value for the glucose level of the user (812). A database in which various clinical data record areas and glucose prediction values are stored can be queried with the particular clinical data record area and can return a glucose prediction value which is mapped to the particular clinical data record area. The predicted glucose value can then be provided and used to determine the user's glucose level (814).

Referring to 10 to obtain a predictive value of a user's hemoglobin level, the portable diagnostic device may obtain user profile information (1002). The user profile information may include a user's location, a user's doctor, a user's pharmacist, an owner of a user's medical record, a user's demographic associations with one or more groups, a user's diet, an indication of a number of the user's children, a medical history of the user, one or more past or present illnesses, for example allergies, surgery, genetic diseases of the user, one or more health concerns of the user, and one or more blood levels about which the user wishes to know details.

Using information from the user profile, the portable diagnostic device can evaluate the value of clinical correlation information for hemoglobin (cCIHb) for the user ( 1004 ) determine. The cCIHb can be determined using clinical information obtained from the user profile, such as the user's medical history, which includes data such as a doctor's report, past or present illnesses, clinical diagnoses, and so on. In some implementations, the cCIHb can be a matrix of values representative of various aspects of the user's medical history, if the user has agreed to provide this personal information.

Next, the portable diagnostic device may determine the clinical linked demographics value for hemoglobin (cLDHb) for the user by obtaining oxygen saturation and temperature data of the user (1006). In some implementations, the cLDHb can be a matrix of values representative of the likely oxygen saturation and temperature of the user. In general, various suitable methods can be used to maintain the user's oxygen saturation and temperature. In some implementations, oxygen saturation can be obtained, as referred to in this specification 11 and the temperature can be obtained using a temperature sensor included in the portable diagnostic device. In some implementations, the cLDHb may be determined based in part on information provided by the user profile, such as one or more user characteristics, such as a demographic of the user, an age of the user, and other personal characteristics of the user.

Upon receipt of the cCIHb and cLDHb, the portable diagnostic device can determine a clinical record range based on the cCIHb and cLDHb (1010). The determined clinical record area is mapped into a hemoglobin prediction value for the hemoglobin level of the user (1012). A database in which various clinical data record areas and hemoglobin prediction values are stored can be queried with the particular clinical data record area and can return a hemoglobin prediction value which is mapped to the particular clinical data record area. The hemoglobin predictive value can then be provided and used to determine the hemoglobin level of the user (1014).

By using predictive values to determine the user's glucose and hemoglobin levels in addition to using blood pressure and temperature data, the user's glucose and hemoglobin levels determined are much more accurate compared to methods of determining glucose and hemoglobin levels that do not have predictive values use. The predictive values include the clinical and demographic information of the user and can therefore more accurately perform calculations that take into account parameters such as skin color and age that can affect glucose or hemoglobin levels. The predictive values can also be used to correlate the predictive values with the susceptibility of the user to certain diseases, and thereby the user can take precautions to minimize the likelihood of acquiring those diseases and to improve the health and life expectancy of the user. In addition, these multiple tests can be performed and the results of the tests can be obtained through a single device if necessary, providing a high level of convenience for the user.

In addition to maintaining the user's glucose and hemoglobin levels, the portable diagnostic device can determine a user's blood pressure and oxygen saturation level. A flow diagram of an exemplary method for determining a user's blood pressure and oxygen saturation levels is shown in FIG 11 shown. Initially, the portable diagnostic device may receive an indication that a blood pressure determination should be performed (1102). The indication that a blood pressure determination should be performed can include a selection made by the user to provide a blood pressure measurement and / or a request made by one or more processors according to a scheduled time. For example, the portable diagnostic device can be programmed such that it delivers a blood pressure measurement at specific times or after specific time periods. The portable diagnostic device can then schedule days and times on which a blood pressure measurement is to be made available to the user, and can initiate the method for determining the blood pressure and oxygen saturation level at the scheduled times.

After receiving the indication that a blood pressure determination should be carried out (1102), the portable diagnostic device can determine the type of sensor used for the blood pressure test and activate the specific sensor type, which in the case of a blood pressure test can include the piezo vibration sensor and the IR / light / laser sensor (1104). Activating the sensors can involve several processes, including, but not limited to, providing increased power to the sensors and configuring or calibrating the sensors.

The activated piezo vibration sensor and the IR / light / laser sensor can receive user measurements (1106). For example, the piezo vibration sensor is designed to record or detect a movement and / or position and / or proximity and / or speed and / or direction of the user's hand and to generate electrical signals that reflect the detected touch and / or vibration and / or or push movements. The IR / light / laser sensor is designed to emit an infrared signal and to detect a reflection of the emitted infrared signal from the skin of the user. In some implementations, a preamplifier can be used to amplify weak pulse signals received by the piezo vibration sensor.

The signals detected by the piezo vibration sensor and the IR / light / laser sensor can be processed, for example, by performing analog-to-digital conversion (ADC) and filtering operations, to give an infrared target detection value (IRTD value) and a piezo vibration value (PV value) generate (1108). In general, various signal processing operations can be performed on the signals detected by the piezo vibration sensor and the IR / light / laser sensor. In some implementations, the operations 1104 - 1108 be repeated several times, and averages of the several IRTD and PV values can be determined.

Processing can also include comparing the determined IRTD and PV values with predicted IRTD and PV values. The comparison produces two sets of blood pressure values. Each of the two sets of blood pressure values is averaged to obtain the systolic and diastolic blood pressure values, respectively (1112). The systolic and diastolic blood pressure values can be used to determine mean arterial pressure (MAP) using [Equation 3], as noted below. $$\text{MAP}=\text{diastolic blood pressure}+\left(1/3\right)\left(\text{systolic blood pressure}-\text{diastolic blood pressure}\right)$$

The generation of predicted values for blood pressure measurements is described below with reference to FIG 12 explained. In some implementations, an oxygen saturation level of the user's blood can also be determined using [Equation 4] (1110). $$\text{Oxygen saturation level}=\left(\left({\text{C.}}_{\text{HbO2}}\right)/\left({\text{C.}}_{\text{HbO2}}+{\text{C.}}_{\text{Man}}\right)\right)*100$$

In [Equation 4], C _{HbO2 is} equal to a concentration of oxygenated hemoglobin and C _{Hb is} equal to a concentration of deoxygenated hemoglobin. The values for C _Hb02 and C _Hb can be obtained using the infrared sensor.

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

In the exemplary implementations mentioned above, predictive values were used to determine a user's blood pressure. The predictive values can be determined using several different methods and based on several factors. Referring to 12 In order to obtain a predicted value of a user's blood pressure, the portable diagnostic device may obtain user profile information (1202) as in the operations 802 and 1002 described. The portable diagnostic device can obtain the clinical and demographic information of the user from the user profile.

Using information from the user profile, the portable diagnostic device can determine the value of clinical correlation information for blood pressure (cCIBP) for the user and obtain ECG data (1204). The cCIBP can be determined using clinical information obtained from the user profile, such as the user's medical history, including data such as a doctor's report, past or present illnesses, clinical diagnoses, and so on. The EKG data can be obtained from various suitable sources, such as, for example, the user's medical history.

Next, the EKG data is processed to identify peaks and time differences between peaks, especially the R-wave ( 1206 ), to be determined. The portable diagnostic device can also determine the value of the clinically linked blood pressure demographics (cLDBP) for the user (1206). In some implementations, one or more processors in the portable diagnostic device may execute one or more programs and algorithms that detect peaks and time differences between detected peaks in a user's EKG. In some implementations, the cLDBP can be determined based in part on one or more user characteristics, such as a demographic of the user, an age of the user, and other personal characteristics of the user. In some implementations, the cLDBP can be an array of values representative of various characteristics, e.g. B. Age, diet, gender of the user, if the user has agreed to provide this personal information.

Upon receipt of the cCIBP and cLDBP, the portable diagnostic device can determine a clinical record range based on the cCIBP and cLDBP (1208). The determined clinical record area is mapped into a blood pressure prediction value for the glucose level of the user (1210). A database in which various clinical data record areas and blood pressure prediction values are stored can be queried with the particular clinical data record area and can return a blood pressure prediction value which is mapped to the particular clinical data record area. The predicted blood pressure value can then be provided and used to determine the user's blood pressure level (1212).

In some implementations, the portable diagnostic device can perform a process of obtaining an EKG and a heart rate level for the user wearing the portable diagnostic device.

When the portable diagnostic device is attached to the left arm of the user, a first heart electrode may be disposed on or within a lower surface of the portable diagnostic device and be in contact with an upper side of the left wrist of the user. A second cardiac electrode is placed on or within a top surface of the portable diagnostic device without touching the skin on the left arm of the user. The first heart electrode can sense a signal from the upper part of the wrist of the user on the left arm, and the second heart electrode can sense two signals when the second heart electrode is placed on the user's chest. The sensed signals may include waveforms of cardiac electrical potential, such as voltages generated during contraction of the heart. The data obtained from the three signals are converted into PQRST (Provocation, Quality, Radiation, Severity, Time) waves. The PQRST waves can be used to generate the Einthoven triangle, EKG diagrams and calculate additional information such as the user's heart rate. In some cases, the PQRST waves can be used to diagnose heart disease.

In some implementations, the portable diagnostic device may perform a process of obtaining a body temperature associated with the user wearing the portable diagnostic device. After the portable diagnostic device is wrapped around a user's arm and is in contact with the user's skin, the temperature sensor in the portable diagnostic device obtains a temperature of the user based on the skin contact. For example, the temperature sensor can touch the wrist and receive the body temperature data of the user over a certain period of time. The temperature sensor supplies the sensor data to one or more processors, which can convert the received data to the Fahrenheit scale and averaging the temperature data measured over one or more time periods in order to obtain a probable body temperature of the user.

In some implementations, the portable diagnostic device may perform a process of obtaining a hand vibration associated with the user wearing the portable diagnostic device. One or more processors can perform operations with a timer and accelerometer or a piezo vibration sensor to obtain hand vibration measurements. For example, a Processor upon receiving an indication that a user is interested in viewing information related to hand vibrations, set the timer for a specific time period, e.g. 60 seconds, and instruct the accelerometer or piezo vibration sensor to obtain vibration data for the specific time period. The accelerometer or piezo vibration sensor can sense the number and strength of the user's hand vibrations in the particular time period and provide data to the processor indicating the number of vibrations.

The processor can receive the data indicating the number of vibrations and can determine a frequency of the vibrations. If the vibrations have a frequency between 3 and 7 Hertz (Hz), the vibrations can be classified as vibrations associated with Parkinson's tremors. The processor may also obtain amplitude information from the accelerometer or piezo vibration sensor to determine the magnitude of any vibrations associated with Parkinson's tremors. Data indicative of the timing, strength and frequency of the vibrations can be stored in a user profile in a storage device and / or presented to the user via the display.

The processes described above for obtaining information regarding the glucose level, hemoglobin level, blood pressure level, EKG, heart rate level, body temperature, hand vibration and cholesterol level of a user can be carried out in parallel, simultaneously, or at different times. The portable diagnostic device has sufficient processing power to perform any of these processes at any time and in response to a user request.

In some implementations, multiple diagnostic tests can be run sequentially or simultaneously. For example, EKG tests can be performed before or during a blood pressure test. An oxygen saturation test and a body temperature test can be done before or during a hemoglobin test. A blood pressure test and a body temperature test can be done before or during a glucose test. Other variations and combinations are possible. For example, if a user has a medical history of certain diseases, such as being diabetic, the portable diagnostic device may periodically perform a test most relevant to the user's disease, such as a glucose diagnostic test, and other tests, such as a blood pressure test , perform a cholesterol test, or an oxygen saturation test before, after, or during the most relevant diagnostic test.

In some implementations, although the user requests to perform some type of diagnostic test, e.g. A glucose test, one or more tests, e.g. B. a blood pressure test to be carried out along with the user-requested diagnostic test. The additional tests can be determined based on one or more criteria, such as, for example, tests that are frequently selected by the user, by a default setting, by the manufacturer's settings, by a doctor's recommendation, to be performed simultaneously, sequentially or in pairs. In some implementations, the additional tests can be determined based on a user's medical history. For example, if a user has diabetes and high blood pressure, the portable diagnostic device can also perform a glucose test each time a blood pressure test is selected. If the user selects a glucose test, the portable diagnostic device can also perform a blood pressure test.

Results obtained by performing one or more of the processes described above can be stored in a memory of the portable diagnostic device or in a database or cloud account associated with the user. Results obtained by performing one or more of the processes described above can also be indicated by the display. In some implementations, the portable diagnostic device can emit a visual, audible, or electronic alarm if the particular glucose level and / or hemoglobin level and / or blood pressure level and / or EKG and / or heart rate level and / or body temperature and / or hand vibrations occur indicate serious illness. For example, the portable diagnostic device may produce an audio output, such as a sound wave, advising the user to see a doctor if, for example, the EKG shows some abnormal heart movement signs or the body temperature is above 102 degrees Fahrenheit.

It is understood that implementations and / or acts described in this specification can be implemented in digital electronic circuitry or in computer software, firmware or hardware, including the structures and structures disclosed in this specification their structural equivalents, or combinations of one or more of these. Implementations can be implemented as one or more computer program products, e.g. B. one or more modules of computer program instructions encoded on a computer readable medium for execution by or for controlling the operation of data processing devices. The computer readable medium may be a machine readable storage device, a machine readable storage substrate, a storage device, a composition of matter that causes a machine readable propagation signal, or a combination of one or more of these. The term “data processing device” includes all devices, devices and machines for processing data, including for example a programmable processor, a computer or several processors or computers. In addition to the hardware, the device may contain code that generates an execution environment for the computer program in question, e.g. B. code that forms a 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, e.g. B. a machine generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to a suitable receiving device.

A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, and it can be used in any form, including as a stand-alone program or as a module, component, Subroutine or other entity suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program can be in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to that program, or in multiple coordinated files (e.g. files in which one or more modules, subprograms or code parts are stored). A computer program can be executed on one computer or on several computers that are located at one location or distributed over several locations and are connected to one another by a communication network.

The processes and logic flows described in this specification can be executed by one or more programmable processors that execute one or more computer programs to perform actions by manipulating input data and generating output. The processes and logic flows can also be carried out by special logic circuitry, and the apparatus can also be implemented as this, e.g. B. an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit).

Processors in computers that execute a computer program include, for example, both general purpose and specialty microprocessors and any one or more processors of any type of digital computer. In general, a processor receives instructions and data from read-only memory or random access memory, or both.

While this specification contains many details, these should not be construed as limitations on the scope of the disclosure or what can be claimed, but rather as descriptions of features that are specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination with a single embodiment. In turn, various features described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. In addition, although features above may be described and even claimed as acting in certain combinations, one or more features of a claimed combination may in some cases be removed from the combination and the claimed combination may be directed to a sub-combination or variation of a sub-combination.

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

Therefore, certain implementations have been described. Other implementations are within the scope of the following claims. For example, the acts recited in the claims can be performed in a different order and still achieve desirable results.

Claims (20)

System that includes: one or more computing devices and one or more storage devices that store instructions that, when executed by the one or more computing devices, result in the one or more computing devices performing operations including: Receiving input corresponding to a request to begin 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 one or more identified sensors based on the non-invasive diagnostic test; Receiving signal data via the one or more sensors; Obtaining a predicted value for 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 Output of the test result via a display or a loudspeaker. System according to Claim 1 wherein the non-invasive diagnostic test comprises a glucose test and / or a cholesterol test and / or a hemoglobin test and / or an oxygen saturation level test and / or an electrocardiogram monitoring test. System according to Claim 1 the operations further comprising: selecting a path based on raw data obtained from the received signal data; and obtaining a set of specific values based on the selected path, wherein determining the test result based on the predicted value and the received signal data comprises determining the test result based on the determined values. System according to Claim 1 wherein the operations further comprise: obtaining user clinical data and user demographic data from one or more databases; Determining a clinical record range based on the obtained clinical user data and the user demographic data; and mapping the clinical record area to the predicted value for the non-invasive diagnostic test. System according to Claim 1 wherein the operations further comprise: determining a second non-invasive diagnostic test to be performed based on (i) a user pattern for associating the second non-invasive diagnostic test with the non-invasive diagnostic test and (ii) a medical history of the user; 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 predicted value for the second non-invasive diagnostic test based in part on the user profile; and determining a second test result based on the second predicted value and the received second signal data. System according to Claim 5 wherein obtaining the predicted value for the non-invasive diagnostic test comprises determining the predicted value for the non-invasive diagnostic test using the second test result. System according to Claim 5 wherein the second non-invasive diagnostic test is a cholesterol test that is performed simultaneously with the non-invasive diagnostic test, which is a glucose test. System according to Claim 5 wherein the non-invasive diagnostic test and the second non-invasive diagnostic test are performed by a wrist watch that includes the one or more computing devices. System according to Claim 1 , wherein the one or more sensors comprise a wireless heart electrode and / or a piezo vibration sensor and / or an infrared sensor and / or a temperature sensor and / or an accelerometer and / or a MEMS sensor (MEMS: microelectromechanical system). A computer-implemented method comprising: Receiving input corresponding to a request to begin a non-invasive diagnostic test to detect a medical condition of a user; Identifying, by one or more computing devices, one or more sensors for performing the non-invasive diagnostic test; Activating the one or more identified 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 for 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 Outputting, by the one or more computing devices, the test result via a display or speaker. Computer-implemented method according to Claim 10 wherein the non-invasive diagnostic test comprises a glucose test and / or a cholesterol test and / or a hemoglobin test and / or an oxygen saturation level test and / or an electrocardiogram monitoring test. Computer-implemented method according to Claim 10 the operations further comprising: selecting a path based on raw data obtained from the received signal data; and obtaining a set of specific values based on the selected path, wherein determining the test result based on the predicted value and the received signal data comprises determining the test result based on the determined values. Computer-implemented method according to Claim 10 wherein the operations further comprise: obtaining user clinical data and user demographic data from one or more databases; Determining a clinical record range based on the obtained clinical user data and the user demographic data; and mapping the clinical record area to the predicted value for the non-invasive diagnostic test. Computer-implemented method according to Claim 10 wherein the operations further comprise: determining a second non-invasive diagnostic test to be performed based on (i) a user pattern for associating the second non-invasive diagnostic test with the non-invasive diagnostic test and (ii) a medical history of the user; 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 predicted value for the second non-invasive diagnostic test based in part on the user profile; and determining a second test result based on the second predicted value and the received second signal data. Computer-implemented method according to Claim 14 wherein obtaining the predicted value for the non-invasive diagnostic test comprises determining the predicted value for the non-invasive diagnostic test using the second test result. Computer-implemented method according to Claim 14 wherein the second non-invasive diagnostic test is a cholesterol test that is performed simultaneously with the non-invasive diagnostic test, which is a glucose test. Computer-implemented method according to Claim 14 wherein the non-invasive diagnostic test and the second non-invasive diagnostic test are performed by a wrist watch that includes the one or more computing devices. Computer-implemented method according to Claim 10 , wherein the one or more sensors comprise a wireless heart electrode and / or a piezo vibration sensor and / or an infrared sensor and / or a temperature sensor and / or an accelerometer and / or a MEMS sensor (MEMS: microelectromechanical system). A watch that includes: one or more computing devices and one or more storage devices that store instructions that, when executed by the one or more computing devices, result in the one or more computing devices performing operations including: 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 the one or more identified 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 Output of the glucose test result and the cholesterol test result via a display or a loudspeaker of the wristwatch. Wristwatch after Claim 19 wherein the one or more sensors include an infrared sensor and a piezo vibration sensor; the operations further comprising: selecting a path based on raw data obtained from the received signal data; and obtaining a set of specific values based on the selected path; and wherein determining the glucose test result based on the first predicted value and the received signal data comprises determining the glucose test result based on the determined values.

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