Systems and methods for physiological parameter monitoring

Abstract

The embodiments of this specification provide a system and method for monitoring physiological parameters. The method includes: receiving a first signal representing a pulse wave, the first signal being related to the cardiac activity of a subject; receiving multiple second signals, the second signals representing time-varying information of the pulse wave of the subject; identifying a first feature of the first signal; identifying a second feature of one of the multiple second signals; determining the physiological parameters of the subject based on the first feature, the second feature and a calibration model, wherein the calibration model is determined based on calibration data; the calibration data includes one or a combination of stored calibration data, similar data, empirical data or collected calibration data related to the subject, wherein the similar data is calibration data of a group of subjects with the same or similar features.

Description

[0001] Cross-references

[0002] The present application is a divisional application of the Chinese application with application number 201680038198.X and application date of March 28, 2016, which is the Chinese national phase application corresponding to the international patent application with application number PCT / CN2016 / 077469, which claims the priority of the international patent application with application number PCT / CN2015 / 083334 filed on July 3, 2015, the priority of the international patent application with application number PCT / CN2015 / 096498 filed on December 5, 2015, and the priority of the international patent application with application number PCT / CN2016 / 070017 filed on January 4, 2016, the contents of which are incorporated herein by reference. Technical Field

[0003] The present disclosure generally relates to a system and method applicable to healthcare-related fields. Specifically, the present disclosure relates to a system and method for physiological parameter monitoring. Background Art

[0004] Conventional blood pressure measurement systems, also known as sphygmomanometers, use Korotkoff sounds or oscillometric methods to determine blood pressure based on the relationship between external pressure and arterial volume pulse size. Conventional blood oximetry systems use photoelectric sensors placed on the patient's finger to detect pulse wave related signals to assess blood oxygen levels. Such systems and methods operate independently and typically discontinuously with intervals of several minutes or longer between consecutive measurements. Continuous monitoring of multiple physiological parameters can be beneficial, for example, in hypertension control and cardiovascular risk prediction. Summary of the invention

[0005] Some embodiments of the present disclosure relate to an apparatus, including a storage device storing instructions and at least one processor. When the at least one processor executes the instructions, the at least one processor may perform one or more of the following operations. Receive a first signal representing a pulse wave, the first signal being related to the cardiac activity of a subject; receive multiple second signals, the second signals representing time-varying information of the pulse wave of the subject; identify a first feature of the first signal; identify a second feature of one of the multiple second signals; determine a physiological parameter of the subject based on the first feature, the second feature and a calibration model, wherein the calibration model is determined based on calibration data; the calibration data includes one or a combination of stored calibration data, similar data, empirical data or collected calibration data related to the subject, wherein the similar data is calibration data of a group of subjects with the same or similar features.

[0006] Some embodiments of the present disclosure relate to a method. The method may include one or more of the following operations. Receive a first signal representing a pulse wave, the first signal being related to the cardiac activity of a subject; receive multiple second signals, the second signals representing time-varying information of the pulse wave of the subject; identify a first feature of the first signal; identify a second feature of one of the multiple second signals; determine a physiological parameter of the subject based on the first feature, the second feature and a calibration model, wherein the calibration model is determined based on calibration data; the calibration data includes one or a combination of stored calibration data, similar data, empirical data or collected calibration data related to the subject, wherein the similar data is calibration data of a group of subjects with the same or similar features.

[0007] Some additional features of the present application may be described in the following description. Some additional features of the present application will be apparent to those skilled in the art by inspection of the following description and corresponding drawings or understanding of the production or operation of the embodiments. The features of the present disclosure may be realized and obtained by practice or use of the methods, means and combinations of various aspects of the specific embodiments described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The present disclosure is further described below by exemplary embodiments. These exemplary embodiments are described in detail with reference to the accompanying drawings. These embodiments are non-limiting exemplary embodiments, in which similar reference numerals represent similar structures, and wherein:

[0009] Figure 1 A schematic diagram showing an exemplary system configuration according to various embodiments of the present disclosure, in which a system for monitoring physiological signals may be deployed;

[0010] Figure 2 According to some embodiments of the present disclosure, Figure 1 An exemplary schematic diagram of an engine of the system shown in ;

[0011] Figure 3 is a flow chart of an exemplary process according to some embodiments of the present disclosure, wherein a method for estimating a physiological signal may be deployed;

[0012] Figure 4 is a block diagram of the structure of the acquisition module according to some embodiments of the present disclosure;

[0013] Figure 5 is a block diagram of the structure of the second acquisition unit according to some embodiments of the present disclosure;

[0014] Figure 6is a block diagram of the structure of the analysis module according to some embodiments of the present disclosure;

[0015] Figure 7 is a block diagram of the construction of a calibration unit according to some embodiments of the present disclosure;

[0016] Figure 8 is a flow chart of a process for estimating a physiological parameter of interest according to some embodiments of the present disclosure;

[0017] Figure 9-A and Figure 9-B Provided are exemplary processes for monitoring physiological parameters according to some embodiments of the present disclosure;

[0018] Fig.10 An exemplary personal health manager according to some embodiments of the present disclosure is shown;

[0019] Fig.11 Describes the construction of a mobile device that may be used to implement a particular system or portion thereof in conjunction with the present disclosure;

[0020] Fig.12 Describes the configuration of a computer that may be used to implement a particular system or portion thereof in conjunction with the present disclosure; and

[0021] Fig.13 Exemplary devices according to some embodiments of the present disclosure are shown. DETAILED DESCRIPTION

[0022] In the following detailed description, numerous specific details are set forth by way of example in order to provide a thorough understanding of the relevant invention. However, it should be apparent to those skilled in the art that the present disclosure can be implemented without such details. In other cases, in order to avoid unnecessarily obscuring aspects of the present disclosure, known methods, procedures, systems, components and / or circuits have been described at a higher level (without details).

[0023] The present disclosure relates to systems, methods and programming aspects of physiological parameter monitoring, such as blood pressure monitoring. The systems and methods relate to improved sensor design and signal processing. The systems and methods disclosed herein can monitor multiple physiological parameters. The features of the systems and methods may include, for example, a combination of one or more of real-time, simultaneity, continuity, non-invasiveness, improved accuracy, etc. In some embodiments, the systems and methods disclosed herein can monitor a variety of cardiovascular activities and related information, including, for example, a combination of one or more of blood pressure information, ECG information, blood oxygen information, etc. In some embodiments, blood pressure can be estimated based on pulse wave related information (e.g., a combination of one or more of pulse transit time (PTT), pulse wave arrival time (PAT), etc.). In some embodiments, blood oxygen level can be estimated based on photoplethysmography (PPG) signals. The systems and methods disclosed herein can be used in medical institutions (e.g., hospitals) or homes. The following description will refer to PPT and combine blood pressure monitoring for illustration, and is not intended to limit the scope of the present disclosure. By way of example only, the systems and methods disclosed herein can utilize one or more other pulse wave related information or signals (e.g., PAT) for blood pressure monitoring.

[0024] These and other features and characteristics of the present disclosure, together with the methods of operation and function of the structural elements and combinations of parts involved and the economic factors of manufacturing, will become more apparent after considering the following description with reference to the accompanying drawings, all of which form a part of this specification. However, it should be expressly understood that the drawings are for illustration and description purposes only and are not intended to limit the scope of the present disclosure. As used in the specification and claims, the singular forms "a", "an", and "the" include the plural forms unless the context clearly dictates.

[0025] Figure 1 An exemplary system configuration according to some embodiments of the present disclosure is shown, in which a system 100 can be deployed. The system 100 can monitor physiological parameters of interest. The system 100 can include a measurement device 110, a database (e.g., a server 120), an external data source 130, and a terminal 140. Multiple components of the system 100 can be directly or indirectly connected to each other via a network 150.

[0026] The measuring device 110 can measure a signal. The signal can be a cardiovascular signal. The signal can be related to or used to calculate or estimate a physiological parameter of interest. The measuring device 110 can include, for example, a combination of one or more of a clinical device, a home device, a portable device, a wearable device, etc. The clinical device used here can be a device that meets the application standards and / or specifications for clinical environments (including, for example, hospitals, doctors' offices, nursing homes, etc.). The clinical device can be used by a healthcare worker or with the help of a healthcare worker. The home device used here can be a device that meets the application standards and / or specifications for use at home or in a non-clinical environment. The home device can be used by a professional or a non-professional. The clinical device or the home device or a portion thereof can be portable or wearable. Exemplary clinical devices include a combination of one or more of a stethoscope, an oscilloscope, an ECG monitor, a PPG monitor, etc. Exemplary home devices include a combination of one or more of an oscilloscope, a home ECG monitor, a pulse meter, etc. Exemplary portable devices include a combination of one or more of an oscilloscope, a portable ECG monitor, a portable PPG monitor, etc. Exemplary wearable devices include glasses 111, shoulder straps 112, smart watches 113, footbands 114, leg bands 115, armbands 116, chest bands 117, neckbands 118, etc. One or more combinations thereof. The examples of the measuring device 110 mentioned above are provided for illustrative purposes only and are not intended to limit the scope of the present disclosure. The measuring device 110 may take other forms, including combinations of one or more of finger guards, wristbands, bras, underwear, chest bands, etc.

[0027] Merely by way of example, the measuring device 110 is a wearable or portable device that can be used to measure one or more cardiovascular signals. In some embodiments, the wearable or portable device can process at least some of the measured signals, estimate the physiological parameters of interest based on the measured signals, display the results including the physiological parameters of interest in the form of, for example, images, audio alarms, etc., and perform wired or wireless communication with other devices or servers (e.g., server 120). In some embodiments, the wearable or portable device can communicate with other devices (e.g., terminal 140) or servers (e.g., cloud servers). The device or server can process at least some of the measured signals, estimate the physiological parameters of interest based on the measured signals, and display the results including the physiological parameters of interest in the form of a combination of, for example, one or more of, for example, images, audio alarms, etc.

[0028] In some embodiments, the operations of processing measured signals, estimating physiological parameters, displaying results, or performing wired or wireless communications may be performed by an integrated device or by independent devices that are connected or communicated with each other. Such integrated devices may be portable or wearable. In some embodiments, at least some of the independent devices may be portable or wearable, or located near the subject whose signals are measured or whose physiological parameters of interest are estimated or monitored. By way of example only, the subject wears a measurement device 110 that can measure one or more cardiovascular signals; the measured one or more cardiovascular signals are transmitted to a smartphone, which can calculate or estimate the physiological parameters of interest based on the measured signals. In some embodiments, at least some of the independent devices are located away from the subject. By way of example only, the subject wears a measurement device 110 that can measure one or more cardiovascular signals; the measured one or more signals are transmitted to a processor, which can calculate or estimate the physiological parameters of interest based on the measured signals; the calculated or estimated physiological parameters of interest may be provided to the subject or other users other than the subject (e.g., a combination of one or more of a doctor, a healthcare provider, a family member of the subject, etc.).

[0029] In some embodiments, the measuring device 110 may include multiple types of sensors, such as one or more combinations of electrode sensors, optical sensors, photoelectric sensors, pressure sensors, acceleration sensors, gravity sensors, temperature sensors, humidity sensors, etc. The measuring device 110 may monitor and / or detect one or more types of variables, including, for example, one or more combinations of temperature, humidity, user or subject input, etc. The measuring device 110 may also include a positioning system (e.g., a GPS receiver) or a position sensor, and the position information may be transmitted to a server 120, an external data source 130, a terminal 140, etc., through the network 150. The position information and the measured signal may be transmitted simultaneously or sequentially.

[0030] The system may include or communicate with a server or database configured to store the library 1100 and / or the algorithm 121. The server or database may be server 120. Server 120 may be a cloud server. By way of example only, server 120 may be implemented in a cloud server that may provide a combination of one or more of storage capacity, computing power, etc. The library 1100 may collect or store data. The data may include personal data, non-personal data, or both. The data may include static data, dynamic data, or both. Exemplary static data may include various information about the subject, including identity, contact information, birthday, health history (e.g., whether the subject has a history of smoking, information about previous surgery, food allergies, drug allergies, medical history, genetic disease history, family health history, etc.), gender, nationality, height, weight, occupation, habits (e.g., health-related habits, such as exercise habits), educational background, hobbies, marital status, religious beliefs, etc., etc., or a combination of one or more. Exemplary dynamic data may include a combination of one or more of the subject's current health status, medications the subject is taking, medical treatments the subject is undergoing, meals, physiological signals or parameters about the subject at multiple time points or over a period of time (e.g., pulse transit time (PTT), systolic blood pressure (SBP), diastolic blood pressure (DBP), etc.), etc.

[0031] The library 1100 may be stored locally in the measurement device 110 or the terminal 140. The library 1100 may include different parts (e.g., personal data, general data, etc.) with different access control levels. For example, the personal data may record data and information related to each user, but the subjects may have different access permissions to different parts of the personal data 1110. For example, the personal data of subject 1, the personal data of subject 2, and the personal data of subject N may be stored in the library 1100, but subject 1 may only have full access rights to his / her own personal data and limited access rights to the personal data of other users.

[0032] Personal data may further include, but are not limited to, title, history and preferences. In addition, the title may have the basic information and medical history of the subject. The title may include, but are not limited to, the age, sex, race, occupation, health status, medical history, lifestyle, marital status and other personal information of the subject. The history may record measured data (M), calibration value (C) (or calibration data), results (SBP, DBP, BP) and additional information related to each measurement and / or calibration. In addition, additional information may be any internal or external variables that occur when the subject is measured and / or calibrated. External variables may include room temperature, humidity, air pressure, weather, climate, time and date, etc. Internal variables such as body temperature, metabolic rate, mood, activity level, activity type, diet and health status, etc. The examples of the additional information mentioned above are only for providing better explanations, and the additional information related to each measurement and / or calibration may be other types of information, such as the viscosity and other rheological data of the subject's blood. In certain embodiments, the additional information recorded in the title and the concept of information are interchangeable. When some information initially recorded in the title changes with each measurement, these information may also be considered as additional information.

[0033] Preferences may have information related to the model, such as the subject's optimal model and coefficients and the optimal model applicability, indicating which (some) optimal models are used under what conditions or with what additional information. The subject's historical data may refer to all information stored in history. Preferences may also include the subject's grade, which grades the reliability of the subject's personal data and may be considered a weighting factor when classifying the subject's personal data into similar data. For example, a subject who uploads calibration values ​​(C) weekly may have a better grade than a subject who calibrates only once a year. Examples of the above-mentioned information are recorded in preferences, which may also include other information, such as the portion of personal data that the subject is willing to share with other users or organizations.

[0034] General data may include some non-private or non-personalized data that can be accessed by other users or subjects. General data may include records of databases of all models, logic, and public data, such as models and coefficients, logical judgments for distinguishing similar data from personal data, and statistical results related to calibration values. Similar data can be selected and obtained from the personal data of multiple subjects, and the logical judgment of classifying personal data into similar data is used to find the most closely related data based on the subject's title and additional information in history. The logical judgment of classifying personal data into similar data can also consider the level in the preference to weight the data obtained from different subjects. The above-mentioned examples of information recorded in general data are only for better explanation. General data may also include other information related to each regression analysis, such as error (E, E', E"). More descriptions can be found in international applications such as PCT / CN2015 / 083334 filed on July 3, 2015 and PCT / CN2015 / 096498 filed on December 5, 2015, the contents of which are included here by reference.

[0035] As used herein, a subject may refer to a person or animal from which signals or information are acquired and physiological parameters are acquired, estimated or monitored. By way of example only, a subject may be a patient from which cardiovascular signals are acquired and blood pressure is estimated or monitored based on the acquired cardiovascular signals.

[0036] One or more algorithms 121 in the server 120 can be applied to data processing or analysis, as described elsewhere in this disclosure. The above description of the server 120 is provided for illustrative purposes only and is not intended to limit the scope of this disclosure. The server 120 can have different structures or configurations. For example, the algorithm 121 is not stored in the server 120; instead, the algorithm 121 can be stored locally in the terminal 140. In addition, the library 1100 can also be stored in the terminal 140.

[0037] The external data source 130 may include a combination of one or more of a variety of organizations, systems, and devices. Exemplary data sources 130 may include a combination of one or more of a medical institution 131, a research device 132, a traditional device 133, and an external device 134. The medical institution 131 or the research device 132 may provide, for example, a combination of one or more of personal medical records, clinical test results, experimental research results, theoretical or mathematical research results, algorithms suitable for processing data, etc. The traditional device 133 may include a cardiovascular signal measurement device, such as a mercury sphygmomanometer. The external device 134 may monitor and / or detect one or more types of variables, including, for example, a combination of one or more of temperature, humidity, user or subject input, etc. The examples of the external data sources 130 mentioned above are provided for illustrative purposes only and are not intended to limit the scope of the present disclosure. For example, the external data source 130 may include other sources and other types of data, such as information about the subject or his or her family's inheritance.

[0038] The terminal 140 in the system 100 may be configured to process at least some of the measured signals, estimate physiological parameters of interest based on the measured cardiovascular signals, display results including physiological parameters of interest in the form of images, store data, control access to the system 100 or parts thereof (e.g., access to personal data stored in or accessible from the system 100), manage input-output from or about the subject, etc. The terminal 140 may include, for example, a combination of one or more of a mobile device 141 (e.g., a smartphone, a tablet, a laptop, etc.), a personal computer 142, other devices 143, etc. The other devices 143 may include devices capable of working independently, or processing units or processing modules assembled in other devices (e.g., smart home terminals). By way of example only, the terminal 140 includes a CPU or processor in the measuring device 110. In some embodiments, the terminal 140 may include Figure 2 The described engine 200 and the terminal 140 may also include a measuring device 110 .

[0039] Network 150 may be a single network or a combination of different networks. For example, network 150 may be a local area network (LAN), a wide area network (WAN), a public network, a private network, a dedicated network, a public switched telephone network (PSTN), the Internet, a wireless network, a virtual network, or a combination thereof. Network 150 may also include a variety of network access points, such as a base station or an Internet exchange point ( Figure 1 ), etc., through which a data source or any component of the system 100 described above can be connected to the network 150 so as to transmit information through the network 150.

[0040] Various components of or accessible from system 100 may include memory or electronic storage media. Such components may include, for example, information about Figure 2 A combination of one or more of the discussed measuring devices 110, servers 120, external data sources 130, terminals 140, external devices 240, etc. The memory or electronic storage medium of any component of the system 100 may include one or both of the following: a system memory (e.g., a disk) provided integrally with the component (i.e., substantially non-removable) and a removable memory removably connected to the component via, for example, a port (e.g., a USB port, a FireWire port, etc.) or a drive (e.g., a disk drive, etc.). The memory or electronic storage medium of any component of the system 100 may include one or more virtual storage resources (e.g., cloud storage, a virtual private network, and / or other virtual storage resources) or operate in connection with the above.

[0041] The memory or electronic storage medium of system 100 may include a dynamic memory device that can store information and instructions executed by a processor of a chip system (SoC, such as a chipset including a processor), other processors (or computing units), etc. One or more combinations thereof. The memory or electronic storage medium can also be used to store temporary variables or other intermediate information during the execution of instructions by the processor. Some or all of the memory or electronic storage medium may be implemented as a dual embedded memory module (DIMM) and may be one or more of the following types of memory: static random access memory (SRAM), burst SRAM or synchronous burst SRAM (BSRAM), dynamic random access memory (DRAM), fast page mode DRAM (FPM DRAM), enhanced DRAM (EDRAM), extended data output DRAM (EDO DRAM), burst extended data output DRAM (BEDO DRAM), burst extended data output DRAM (BEDO DRAM), enhanced DRAM (EDRAM), synchronous DRAM (SDRAM), JEDEC SRAM, PCIOO SDRAM, double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), direct bus DRAM (DRDRAM), ferroelectric RAM (FRAM) or any other type of memory device. The memory or electronic storage medium may also include a read-only memory (ROM) and / or another static memory device, which may store static information and instructions for the processor and / or other processors (or computing units) of the SoC. Additionally, memory or electronic storage media may include magnetic disks, optical disks, or flash memory devices to store information and instructions.

[0042] In some embodiments, the SoC may be part of a core processing or computing unit of a component from or accessible from the system 100. The SoC may receive and process input data and instructions, provide outputs and / or control other components of the system. In some embodiments, the SoC may include a microprocessor, a memory controller, memory, and peripheral components. The microprocessor may also include cache memory (e.g., SRAM), which, together with the memory of the SoC, may be part of a memory hierarchy for storing instructions and data. The microprocessor may also include one or more logic modules, such as a field programmable gate array (FPGA) or other logic array. Communication between the microprocessor and the memory in the SoC may be facilitated by a memory controller (or chipset), which may also facilitate communication with peripheral components (e.g., a combination of one or more of a timer, a real-time timer, a power-on reset generator, etc.). The SoC may also include other components, including a combination of one or more of a timing source (e.g., an oscillator, a phase-locked loop, etc.), a voltage regulator, a power management circuit, etc.

[0043] By way of example only, the system 100 may include a wearable or portable device. The wearable or portable device may include a SoC and a plurality of sensors. Exemplary sensors may include a combination of one or more of a photoelectric sensor, a conductivity sensor, and the like. The SoC may process signals acquired by at least some of the plurality of sensors. The acquired signals may be a combination of a plurality of physiological signals, including, for example, a combination of one or more of PPG, ECG, and the like. The SoC may calculate a physiological parameter of interest based on the acquired signals. Exemplary physiological parameters of interest may be a combination of one or more of blood pressure, blood oxygen level, ECG information, heart rate, and the like.

[0044] In some embodiments, the external data source 130 may receive data from the measurement device 110, the server 120, the terminal 140, etc., or any combination thereof, through the network 150. By way of example only, the external data source 130 (e.g., a medical institution or a smart home system, etc.) may receive information about the subject (e.g., a combination of one or more of location information, data from a cloud server or terminal, etc.) based on the data received from the measurement device 110 or the terminal 140. In some other embodiments, the measurement device 110 may receive data from the server 120, the external data source 130, etc., or any combination thereof, through the network 150. By way of example only, the measurement device 110 may receive information about the subject (e.g., a combination of one or more of the subject's current / historical health status, medications the subject is taking, medical treatments the subject is undergoing, current / historical meals, current emotional conditions, historical physiological parameters about the subject (e.g., PTT, SBP, DBP), etc.). In addition, the terminal 140 may receive data from a combination of one or more of the measurement device 110, the server 120, the external data source 130, etc.

[0045] Figure 1 is a specific example of system 100. The configuration of system 100 is not limited to Figure 1 For example, the server 120 may be omitted, and all its functions may be transferred to the terminal 140. In another example, the server 120 and the terminal 140 may be ignored, and all their functions may be transferred to the measuring device 110. The system may include a variety of devices or a combination of devices in different embodiments.

[0046] In one example, the system may include a wearable or portable device and a mobile device (e.g., a smartphone, a tablet, a laptop, etc.). The wearable or portable device may be used to acquire one or more combinations of physiological signals, environmental information, etc. The mobile device may be used to receive signals or information acquired by the wearable or portable device. The mobile device may calculate one or more physiological parameters of interest based on the acquired signals or information and related data retrieved from other sources (e.g., a server, a memory contained in the wearable or portable device, a memory contained in the mobile device, etc.). The retrieved related data may include, for example, current / historical information stored on the server. Exemplary current / historical information may include a combination of one or more of the current / historical health status of the subject, current / historical medications taken by the subject, current / historical medical treatments performed by the subject, current / historical meals, current / historical emotional conditions, current / historical physiological parameters of the subject (e.g., PTT, SBP, DBP, ECG information, heart rate, blood oxygen level), etc. The wearable or portable device or the mobile device may display or report or store at least some of the combinations of one or more of the acquired signals, information, retrieved related data, calculated one or more physiological parameters of interest, etc. The display or report may be provided to the subject, to another user other than the subject, to a server, or to another device.

[0047] In another example, the system may include a wearable or portable device that can perform functions including: acquiring physiological signals or environmental information; retrieving relevant data from other sources (e.g., a server, a memory contained in the wearable or portable device, etc.); calculating one or more physiological parameters of interest based on the acquired signals, information, or retrieved relevant data; and displaying, reporting, or storing at least some of a combination of one or more of the acquired signals, information, retrieved relevant data, calculated one or more physiological parameters of interest, etc. The display or report may be provided to the subject, other users other than the subject, a server, or other device.

[0048] In another example, the system may include a wearable or portable device that can perform functions including: acquiring physiological signals and environmental information; communicating with a server to transmit at least some of the acquired signals or information to the server so that the server can calculate one or more physiological parameters of interest; receiving the calculated one or more physiological parameters of interest from the server; displaying, reporting or storing at least some of the combination of one or more of the acquired signals, information, calculated one or more physiological parameters of interest, etc. The display or report can be provided to the subject, other users other than the subject, a third party, a server or other device. In some embodiments, communication between the wearable or portable device and the server can be achieved by connecting the wearable or portable device to a network (e.g., network 150). In some embodiments, communication between the wearable or portable device and the server can be achieved by a communication device (e.g., a mobile device, such as a smartphone, a tablet, a laptop, etc.) that communicates with both the wearable or portable device and the server.

[0049] In another example, the system may include a wearable or portable device, a mobile device (e.g., a smart phone, a tablet, a laptop, etc.), and a server. The wearable or portable device may be used to obtain a combination of one or more of physiological signals, environmental information, etc. The mobile device may be used to receive signals or information obtained by the wearable or portable device, and may calculate one or more physiological parameters of interest based on the received signals and / or information retrieved from the wearable or portable device and related data retrieved from other sources (e.g., a server, a memory contained in the wearable or portable device, or a memory contained in the mobile device). The mobile device may display, report, or store at least some of the combinations of one or more of the collected signals, information, retrieved related data, calculated one or more physiological parameters of interest, etc. The display or report may be provided to a subject, other users other than the subject, a third party, a server, or other devices.

[0050] In another example, the system may include an integrated clinical device or a home device. The integrated device may be wearable or portable. The integrated device may be used to collect one or more combinations of physiological signals, environmental information, etc. The integrated device may further include an output device that can display, report, or output at least some of the combinations of one or more of the collected signals, information, retrieved related data, calculated one or more physiological parameters of interest, etc. The display or report may be provided to the subject, other users other than the subject, a third party, a server, or other devices. The integrated device may perform one or more measurement operations for calibrating the integrated device.

[0051] In another example, the system may include an integrated clinical device or a home device and a server. The integrated device may be wearable or portable. The integrated device may perform the following functions: collecting physiological signals and environmental information; communicating with a server to transmit at least a portion of the collected signals or information to the server so that the server can calculate one or more physiological parameters of interest; receiving one or more physiological parameters of interest calculated from the server; and displaying, reporting or storing at least some of the combinations of one or more of the collected signals, information, retrieved related data, calculated one or more physiological parameters of interest, etc. The display or report may be provided to a subject, other users other than the subject, a third party, a server or other device. The integrated device may perform one or more measurement operations for calibrating the integrated device. In some embodiments, the communication between the integrated clinical device or the home device and the server may be implemented by connecting the integrated clinical device or the home device to a network (e.g., network 150). In some embodiments, the communication between the integrated clinical device or the server may be implemented by a communication device (e.g., a mobile device such as a smartphone, a tablet computer, a laptop, etc.), which communicates with the wearable or portable device and the server at the same time.

[0052] In some embodiments, the system may provide a user interface to allow the subject, other users or entities outside the subject to exchange information (including input or output systems) with the system disclosed herein. The user interface may be implemented in a terminal device, including, for example, a combination of one or more of a mobile device, a computer, etc. The user interface may be integrated in the system, such as a display device of the system. People with appropriate access rights can access the system. Access rights may include, for example, a combination of one or more of the rights to access some or all information about the subject, the rights to update some or all information about the subject, etc. The access rights may be associated with or linked to a set of login credentials. As an example only, the system may provide three layers of access rights. The first layer may include full access rights on subject information, allowing the receipt and update of information about the subject. The second layer may include partial access rights on subject information, allowing the receipt and update of partial information about the subject. The third layer may include minimum access rights on subject information, allowing the receipt or update of partial information about the subject. Different login authentication information may be associated with different access rights to information about the subject in the system. As used herein, updating may include providing information that does not exist in the system, or modifying existing information with new information.

[0053] As an example only, the system can receive information about the subject provided by the user interface. Information about the subject can include basic information and optional information. Exemplary basic information can include height, weight, age (or date of birth), gender, arm length, nationality, occupation, habits (e.g., health-related habits, such as exercise habits), educational background, hobbies, marital status, religious beliefs, health-related history (e.g., whether the subject has a history of smoking, food allergies, drug allergies, medical history, family health history, genetic disease history, information about previous surgery, etc., one or more combinations), contact information, emergency contacts, etc. One or more combinations. Exemplary optional information can include the current health status of the subject, the medication the subject is taking, and the medical treatment diet that the subject is taking. The system can receive information about specific measurements of physiological parameters of interest, such as through the user interface. Examples of such information can include the motion state of the subject at or before and after the acquisition time (defined elsewhere in this disclosure), the emotional state of the subject at or before and after the acquisition time, the stress level of the subject at or before and after the acquisition time, etc. One or more combinations. The system can receive one or more options or instructions through the user interface. In some embodiments, options or instructions can be provided by the subject or other users outside the subject in response to questions or prompts of the system to answer questions or make choices. In one example, the options or instructions may include the frequency of measurement (e.g., once a week, once a month, twice a week, twice a month, once a day, twice a day, etc.), the preferred format of information displayed to the subject or other users other than the subject (e.g., a combination of one or more of an email, a voice message, a text message, an audio alert, tactile feedback, etc.). In another example, the options or instructions may include information about calculating a parameter of interest, such as rules about how to select a combination of one or more of a model, a function, calibration data, etc.

[0054] In some embodiments, the system may provide information to the subject or other users other than the subject through a user interface. Exemplary information may include a combination of one or more of an alarm, a suggestion, a prompt, etc. In one instance, if a triggering event occurs, an alarm may be provided or displayed to the subject or other users other than the subject. An exemplary triggering event may be that at least some of the acquired information or physiological parameters of interest exceed a threshold. As an example only, a triggering event may be that the acquired heart rate exceeds a threshold (e.g., higher than 150 beats per minute, lower than 40 beats per minute, etc.). For another example, a triggering event may be that a physiological parameter of interest (e.g., estimated blood pressure) exceeds a threshold. In another example, a suggestion may be provided or displayed to the subject or other users other than the subject. An exemplary recommendation may be a request to enter specific data (e.g., a combination of one or more of basic information, optional information, update parameters of interest, update models, update functions, update options, and instructions, etc.). A prompt may be provided or displayed to the subject or other users other than the subject. An exemplary prompt may include a prompt to take a prescribed medication, take other medications, perform a measurement of a physiological parameter of interest, etc., or a combination of one or more of the above.

[0055] In some embodiments, the system can communicate with the subject, the user and / or the third party other than the subject through the user interface. An exemplary third party can be an external device of a doctor, a health care worker, a medical institution, a research device, a subject, or a user who has a family or friend relationship with the subject, etc. An exemplary communication can be related to one or more combinations of the subject's health status, eating habits, exercise habits, prescription drugs, instructions or steps for performing measurements, etc. In some embodiments, the user interface accessible to a third party or a third party can be the same or different from the user interface accessible to the subject or the subject. In one example, the output or data can be transmitted to a third party (for example, a combination of one or more of a computer, a terminal in a doctor's office, a hospital where the health care worker is located and the subject's health status is monitored, etc.). The third party can provide feedback information or instructions related to the output information through the user interface. As an example only, the third party can receive information about one or more physiological parameters of interest of the subject, and accordingly provide suggestions for the actions that the subject will take (for example, taking prescription drugs, taking other drugs, contacting or visiting a third party, etc., one or more combinations); the system can send suggestions to the subject.

[0056] Figure 2An exemplary diagram including an engine 200 is shown. The engine 200 can be configured to acquire one or more signals, and to calculate or estimate one or more physiological parameters of interest based on the acquired signals. As shown, the engine 200 can be connected to or otherwise communicate with, for example, an external device 240 and a server 120. The engine 200 can include an information acquisition module 210, an analysis module 220, and an output module 230. The information acquisition module 210 can be configured to acquire signals or information about a subject, such as a combination of one or more of physiological signals, information about the health status of the subject, etc. The analysis module 220 can be configured to analyze the acquired signals or information, or determine or estimate physiological parameters of interest, or perform both. The output module 230 can be configured to output a combination of one or more of the acquired signals or information, physiological parameters of interest, etc. As used herein, a module can have an independent processor, or use a system shared processor. The processor can perform functions according to instructions associated with a variety of modules. For example, according to relevant instructions, the analysis module 220 can retrieve the acquired signals and perform calculations to obtain one or more physiological parameters of interest.

[0057] The information acquisition module 210 can be configured to obtain signals or information from or about one or more subjects. As used herein, the acquisition process can be realized by receiving signals or information sensed, detected or measured by sensors, or by receiving input from subjects or from other users outside the subjects (e.g., doctors, medical staff, family members of subjects, etc., one or more combinations thereof). For simplicity, the acquired signal or information can be referred to as acquired information. As used herein, information can include signals about subjects acquired by devices (including, for example, sensors), environmental information acquired by devices (including, for example, sensors), information acquired in other ways, such as from inputs of other users outside the subjects, information processed or preprocessed, etc., etc., as described. Exemplary sensors can include combinations of one or more of electrode sensors, optical sensors, photoelectric sensors, pressure sensors, acceleration sensors, gravity sensors, temperature sensors, humidity sensors, etc.

[0058] Exemplary acquired information may include physiological information. In the exemplary context of determining blood pressure, physiological information may include cardiovascular signals. Exemplary cardiovascular signals may include PPG signals, ECG signals, ballistocardiogram (BCG) signals, blood pressure (BP), systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse rate (PR), heart rate (HR), heart rate variability (HRV), heart murmurs, blood oxygen saturation concentration, blood density, blood pH, bowel sounds, brain waves, fat content, blood flow rate, etc. One or more combinations. Exemplary acquired information may include information about the subject, such as a combination of one or more of height, weight, age, gender, body temperature, arm length, medical history, etc. Exemplary acquired information may include information from or about the subject's surroundings at or around the acquisition time (referred to as environmental information). Exemplary environmental information may include a combination of one or more of temperature, humidity, air pressure, air velocity, ambient light intensity, etc. As used herein, acquisition time may refer to a time point or time period for acquiring information about the subject (e.g., the subject's physiological information).

[0059] The information acquisition module 210 can receive or load information from the external device 240, the server 120 or other devices (not shown) including, for example, ECG monitors, PPG monitors, respiratory monitors, brain wave monitors, blood oxygen monitors, blood glucose monitors and devices with similar functions. In the present disclosure, the term "monitor" and the term "sensor" can be used interchangeably. Examples of the external device 240 can include a combination of one or more of a smart watch, headphones, glasses, bracelets, necklaces, etc. The external device 240, the server 120 or such other devices can be local or remote. For example, the server 120 and the engine 200 can be connected via a local area network (LAN) or the Internet. The external device 240 and the engine 200 can be connected via a local area network or the Internet. Other devices and the engine 200 can be connected via a local area network or the Internet. The information transmission between the information acquisition module 210 and the external device 240, the server 120 or such other devices can be a combination of one or more of a wired connection, a wireless connection, etc.

[0060] The information acquisition module 210 can receive information provided by the subject or other users other than the subject, for example, through an input device. The input device may include, but is not limited to, a keyboard, a touch screen (e.g., a touch screen with tactile or tactile feedback), a voice input device, an eye tracking input device, a brain monitoring system, etc., one or more combinations thereof. The information received by the input device can be transmitted to a processor via, for example, a bus, for further processing. The processor for further processing the information obtained from the input device can be a digital signal processor (DSP), a system on a chip (SoC), or a microprocessor, etc., one or more combinations thereof. Other types of input devices may include a cursor control device such as a mouse, a trackball, or a cursor direction key, etc., to transmit information about direction and / or command selection to, for example, a processor.

[0061] The description of the information acquisition module 210 is for illustrative purposes and does not limit the scope of the present disclosure. Many alternatives, modifications, and variations will be apparent to those skilled in the art. The features, structures, methods, and other features of the exemplary embodiments described herein may be combined in various ways to obtain additional and / or alternative exemplary embodiments. For example, the storage unit ( Figure 2 (not shown) can be added to the information acquisition module 210 to store the acquired information.

[0062] The analysis module 220 may be configured to analyze the acquired information. The analysis module 220 may be connected to or otherwise communicate with one or more information acquisition modules 210-1, 210-2, ..., 210-N to receive at least a portion of the acquired information. The analysis module 220 may be configured to perform one or more operations, including, for example, a combination of one or more of preprocessing, calculation, calibration, statistical analysis, etc. Any of the above operations may be performed based on at least some of the acquired information, or an intermediate result from another operation (e.g., an operation performed by the analysis module 220 or other components of the system 100). For example, the analysis may include one or more operations, including: preprocessing at least a portion of the acquired information; identifying feature points or features of the acquired information or preprocessed information; calculating intermediate results based on the identified feature points or features; performing calibration; analyzing information about the subject provided by the subject or other users other than the subject; analyzing information about the subject's surroundings at or before and after the acquisition time; estimating a combination of one or more of the physiological parameters of interest, etc.

[0063] Some analysis operations may be performed in parallel or in series. As used herein, parallel execution may indicate that some analysis operations may be performed at the same or approximately the same time; serial execution may indicate that some analysis operations may be started or performed after other analysis operations are started or completed. In some embodiments, the serial execution of two operations may indicate that one operation is started after the other operation is completed. In some embodiments, the serial execution of two operations may indicate that one operation is started after the other operation has been completed, and that the two operations partially overlap. In some embodiments, at least two analysis operations may be performed in parallel. In some embodiments, at least two analysis operations may be performed in series. In some embodiments, some analysis operations may be performed in parallel, while some analysis operations may be performed in series.

[0064] The analysis or some analysis operations can be performed in real time, i.e., at the acquisition time or before and after the acquisition time. The analysis or some analysis operations can be performed with a delay after the information is acquired. In some embodiments, the acquired information can be stored for delayed analysis. In some embodiments, the acquired information can be preprocessed and stored for further analysis at a later time. The delay can be a few seconds, a few minutes, a few hours, or a few days or a longer time. After the delay, the analysis can be triggered by an instruction from a subject or other users outside the subject (e.g., a combination of one or more of a doctor, a medical staff, a family member of the subject, etc.) or one or more of the instructions stored in the system 100. By way of example only, the instructions stored in the system 100 can specify a combination of one or more of the duration of the delay, the time to be analyzed, the frequency of the analysis to be performed, the triggering event for triggering the analysis execution, etc. The instructions stored in the system 100 can be provided by the subject or other users outside the subject. An exemplary triggering event can be that at least some of the acquired information or the physiological parameter of interest exceeds a threshold value. By way of example only, the triggering event can be that the acquired heart rate exceeds a threshold value (e.g., higher than 150 beats per minute, lower than 40 beats per minute, etc.). As used herein, "exceeding" may be greater than or less than a threshold. For another example, a triggering event may be that a physiological parameter of interest (eg, estimated blood pressure) exceeds a threshold.

[0065] The analysis module 220 may be centralized or distributed. The centralized analysis module 220 may include a processor ( Figure 2 The processor may be configured to perform operations. The distributed analysis module 220 may include a plurality of operation units ( Figure 2 (not shown). The operation units may be configured to jointly perform operations for the same analysis. In a distributed configuration, the execution of multiple operation units may be controlled or coordinated by, for example, server 120.

[0066] The acquired information, the intermediate results of the analysis, or the analysis results (e.g., the physiological parameters of interest) may be analog or digital. In the exemplary context of blood pressure monitoring, the acquired information, the intermediate results of the analysis, or the analysis results (e.g., the physiological parameters of interest) may include, for example, a combination of one or more of a PPG signal, an ECG signal, a BCG signal, BP, SBP, DBP, PR, HR, HRV (heart rate variability), a heart murmur, blood oxygen saturation concentration (or blood oxygen level), blood density, blood pH, bowel sounds, brain waves, fat content, blood flow rate, etc.

[0067] The analysis results (e.g., physiological parameters of interest about the subject) may be affected by a variety of factors or conditions, including, for example, one or more combinations of environmental factors, factors attributable to the subject's physiological condition, factors attributable to the subject's psychological condition, etc. One or more such factors may affect a combination of one or more of the accuracy of the acquired information, the accuracy of the intermediate results of the analysis, the accuracy of the analysis results, etc. For example, the physiological parameters of interest may be estimated based on the correlation with the acquired information; factors attributable to the physiological condition may cause deviations from the correlation; the factor may affect the accuracy of the physiological parameters of interest estimated based on the correlation. By way of example only, a cardiovascular signal about a subject may vary with, for example, a combination of one or more of time, the subject's psychological condition, etc. The correlation between a cardiovascular signal and a physiological parameter of a subject (e.g., the correlation of a PPT value with blood pressure) may vary with, for example, the subject's psychological condition, the subject's psychological condition, the environment around the subject, etc., or a combination thereof. Such influences may be offset in the analysis.

[0068] In an analysis, information about influencing conditions (e.g., environmental information, physiological conditions, psychological conditions, etc.) can be obtained, and calibration or adjustment can be performed accordingly during the analysis process. As an example only, calibration or adjustment can be performed with the help of a calibration factor. For example, an environmental calibration factor can be introduced into the analysis based on environmental information obtained from or about the subject's surrounding environment at or before and after the acquisition time. Exemplary environmental information may include one or more of temperature, humidity, air pressure, air flow rate, ambient light intensity, etc. Exemplary environmental calibration factors may include one or more of temperature calibration factors, humidity calibration factors, air pressure calibration factors, air flow rate calibration factors, ambient light intensity calibration factors, etc. For another example, calibration or adjustment can be performed by performing a calibration (e.g., calibration model, calibration function, etc.) for estimating the correlation of physiological parameters of interest. For another example, calibration or adjustment can be performed based on information about influencing conditions through correlations from multiple correlation coefficients for estimating physiological parameters of interest.

[0069] The description of the analysis module 220 is for illustrative purposes and does not limit the scope of the present disclosure. Many alternatives, modifications, and variations will be apparent to those skilled in the art. The features, structures, methods, and other features of the exemplary embodiments described herein may be combined in various ways to obtain additional and / or alternative exemplary embodiments. For example, the cache unit ( Figure 2 (not shown) may be added to the analysis module 220 for storing intermediate results or real-time signals or information during the above process.

[0070] The output module 230 can be configured to provide an output. The output can include a combination of one or more of the physiological parameter of interest, at least some of the acquired information (e.g., acquired information for estimating the physiological parameter of interest), etc. The transmission of the output can be through a combination of one or more of a wired connection, a wireless connection, etc. Once the output is available for transmission, the output can be transmitted in real time. The output can be delayed after the output is available for transmission. The delay can be a few seconds, minutes, hours, or days or a longer time. After the delay, the output can be triggered by a combination of one or more of instructions from the subject or other users or third parties outside the subject or instructions stored in the system 100. By way of example only, the instructions stored in the system 100 can specify a combination of one or more of the delay duration, the time to transmit the output, the frequency of the output to be transmitted, the triggering event, etc. The instructions stored in the system 100 can be provided by the subject or other users outside the subject. An exemplary triggering event can be that the physiological parameter of interest or at least some of the acquired information thereof exceeds a threshold. By way of example only, the triggering event can be that the acquired heart rate exceeds a threshold (e.g., above 150 beats per minute, below 40 beats per minute, etc.). As another example, a triggering event may be a physiological parameter of interest (eg, estimated blood pressure) exceeding a threshold value.

[0071] The output for transmission may be, for example, a combination of one or more of analog form, digital form, etc. The output may be, for example, a combination of one or more of a chart, a code, a voice message, text, a video, an audio alarm, a tactile effect, etc. The output may be displayed on a local terminal or transmitted to a remote terminal or both. The terminal may include, for example, a combination of one or more of a personal computer (PC), a desktop computer, a laptop computer, a smart phone, a smart watch, etc. By way of example only, the output may be displayed on a wearable or portable device worn by the subject and also transmitted to a computer or terminal in a doctor's office or hospital where the healthcare provider is located and the subject's health condition is monitored.

[0072] The output module 230 may include or communicate with a display device that can display output or other information to the subject or other users outside the subject. The display device may include a liquid crystal display (LCD), a light emitting diode (LED) based display, or any other flat panel display, or may use a cathode ray tube (CRT), a touch screen, etc. The touch screen may include, for example, a resistive touch screen, a capacitive touch screen, a plasma touch screen, a vector pressure sensing touch screen, an infrared touch screen, etc., or a combination of one or more.

[0073] The external device 240 may include any type of local or remote device or apparatus associated with or in communication with the system 100 or a portion thereof. For example, the external device 240 may include a combination of one or more of a memory device, a display device, a measuring device 110, an input device, and the like.

[0074] In some embodiments, the storage module ( Figure 2 not shown) or storage unit ( Figure 2 (not shown) can be integrated into the engine 200. In some embodiments, the storage unit ( Figure 2 The storage module (not shown) can be integrated into any one of the information acquisition module 210, the analysis module 220, and the output module 230. Figure 2 not shown) or storage unit ( Figure 2 The storage module (not shown) can be used to store intermediate results or analysis results. Figure 2 not shown) or storage unit ( Figure 2 Not shown) can be used as a data cache. The storage module ( Figure 2 not shown) or storage unit ( Figure 2 The storage module (not shown) may include a hard disk, a floppy disk, a selector tube memory, a RAM, a DRAM, an SRAM bubble memory, a thin film memory, a magnetic plated wire memory, a phase change memory, a flash memory, a cloud disk, etc., or a combination of one or more thereof. Figure 2 not shown) or storage unit ( Figure 2 not shown) can include information about Figure 1 and memory or electronic storage media described elsewhere in this disclosure.

[0075] In some embodiments, the engine 200 does not include a storage module or storage unit, and the external device 240 or the server 120 can be used as a storage device accessible to the engine 200. The server 120 can be a cloud server that provides cloud storage. As used herein, cloud storage is a data storage model in which digital data is stored in a physical storage across multiple servers (and often in multiple locations) in a logical pool. The physical environment (including, for example, a logical pool, a physical storage across multiple servers) can be owned and managed by a hosting company. The hosting company can be responsible for keeping the data available and accessible, and keeping the physical environment protected and running. Such cloud storage can be accessed through a cloud service, a network service application programming interface (API) or through an application that utilizes an API. Exemplary applications include a combination of one or more of a cloud desktop storage, a cloud storage gateway, a web-based content management system, etc. The server 120 may include a public cloud, a personal cloud, or both. For example, the acquired information can be stored in a personal cloud, which can be accessed after authorization by authentication (e.g., a combination of one or more of a username, a password, a password, etc.). Non-personalized information including, for example, methods or computational models can be stored in a public cloud. No authorization or authentication is required to access the public cloud. The information acquisition module 210, the analysis module 220, and the output module 230 can retrieve or load information or data from a public cloud or a personal cloud. Any of these modules can be configured to transmit signals and data to a public cloud or a personal cloud.

[0076] The connection or transmission between any two of the information acquisition module 210, the analysis module 220 and the output module 230 can be through a combination of one or more of a wired connection, a wireless connection, etc. At least two of these modules can be connected to different external devices. At least two of these modules can be connected to the same external device. The external device 240 can be connected to one or more modules through a combination of one or more of a wired connection, a wireless connection, etc. It should be understood by those skilled in the art that the above embodiments are only used to describe the invention in the present disclosure, and many modifications and changes can be made to the present disclosure without departing from the spirit of the invention disclosed in the present disclosure. For example, the information acquisition module 210 and the output module 230 can be integrated into an independent module configured to obtain and output signals or results. The independent module can be connected to the analysis module 220 through a combination of one or more of a wired connection, a wireless connection, etc. The three modules in the engine 200 can be partially integrated into one or more independent modules or share one or more units.

[0077] The connection or transmission between the modules of the system 100 or between the modules and the external device 240 or between the system and the server 120 should not be limited to the above description. All connections or transmissions can be used in combination or can be used independently. The modules can be integrated into an independent module, that is, the functions of these modules can be implemented by independent modules. Similarly, one or more modules can be integrated into a single chip of the external device 240. Any of the above-mentioned connections or transmissions can be a combination of one or more of wired connections, wireless connections, etc. For example, the wired connection or wireless connection can include, for example, a combination of one or more of wires, cables, satellites, microwaves, Bluetooth, radio, infrared, etc.

[0078] The engine 200 may be implemented in one or more processors. The modules or units of the engine 200 may be integrated into one or more processors. For example, the information acquisition module 210, the analysis module 220, and the output module 230 may be implemented in one or more processors. One or more processors may be connected to a memory device ( Figure 2 ), external device 240, and server 120. One or more processors can receive signals or data from the memory device ( Figure 2 The processors 240 and the server 120 retrieve or load signals, information or instructions, and process the signals, information, data or instructions or a combination thereof to calculate one or more physiological parameters of interest. The one or more processors may also connect or communicate with other devices related to the system 100, and transmit or share signals, information, instructions, physiological parameters of interest, etc. with such other devices through, for example, a mobile phone APP, a local or remote terminal, or a combination of one or more of the above.

[0079] Figure 33 is a flowchart of an exemplary process for estimating physiological parameters of interest according to some embodiments of the present disclosure. Information about the subject may be acquired in step 310. Information acquisition may be performed by the information acquisition module 210. The acquired information may include physiological information of the subject, environmental information about the acquisition time or the surrounding environment of the subject around the acquisition time, and information provided by the subject or other users other than the subject. The acquired information may include a combination of one or more of PPG signals, ECG signals, pulse rate, heart rate, heart rate change, blood oxygen saturation concentration, respiration, muscle state, bone state, brain waves, blood lipid content, blood sugar content, height, weight, age, gender, body temperature, arm length, medical history, room temperature, humidity, air pressure, air flow rate, ambient light intensity, etc. At least some of the acquired information may be analyzed in step 320. Through the analysis, multiple features of at least some of the acquired information may be identified. For example, the acquired information may include PPG signals and ECG signals; the identified features of these signals may include, for example, a combination of one or more of waveforms, feature points, peak points, valley points, amplitudes, time intervals, phases, frequencies, cycles, etc. An analysis based on the identified features may be performed in step 320. For example, a physiological parameter of interest may be calculated or estimated based on the identified features. The physiological parameters of interest estimated based on the obtained PPG signal and ECG may include, for example, a combination of one or more of BP, SBP, DBP, blood oxygen level, etc. The physiological parameters of interest may be output in step 330. Some of the acquired information may be output in step 330. The output may be a combination of one or more of display to the subject or other users other than the subject, printing, storage in a memory device or server 120, transmission to a device for further processing, etc. It should be noted that after the analysis in step 320, a new acquisition step may be performed in step 310.

[0080] This description is for illustrative purposes and does not limit the scope of the present disclosure. Many alternatives, modifications and variations will be apparent to those skilled in the art. The features, structures, methods and other features of the exemplary embodiments described herein may be combined in various ways to obtain additional and / or alternative exemplary embodiments. For example, a preprocessing step may be added between step 310 and step 320. In the preprocessing step, the acquired signal may be preprocessed to reduce or remove noise or interference in the initially acquired signal. For example, complex real-time digital filtering may be used to reduce or remove high-frequency noise in a PPG or ECG signal so that its features can be accurately identified. Exemplary preprocessing methods may include a combination of one or more of low-pass filtering, band-pass filtering, wavelet transform, median filtering, morphological filtering, curve fitting, Hilbert-Huang transform, and the like. Descriptions of methods and systems for reducing or removing noise in physiological signals (e.g., PPG signals or ECG signals) can be found in International Application No. PCT / CN2015 / 077026 filed on April 20, 2015, International Application No. PCT / CN2015 / 077025 filed on April 20, 2015, and International Application No. PCT / CN2015 / 079956 filed on May 27, 2015, the contents of which are incorporated herein by reference. Between step 310 and step 320, or Figure 3 One or more other optional steps are added elsewhere in the exemplary process shown. Examples of such steps may include storing or caching the acquired information.

[0081] Figure 4It is a block diagram of the structure of the information acquisition module shown in some embodiments of the present disclosure. The information acquisition module 210 can be connected or otherwise communicated with, for example, an external device 240, an analysis module 220, an output module 230 and a server 120 through a network 150. The information acquisition module 210 can be configured to collect information related to the subject, information provided by the subject, a user other than the subject and / or a related third party (for example, a doctor, a health care worker, a medical institution, a research institution, an external device of the subject or a user with a good relationship with the subject, etc.), and one or more combinations of environmental information from the surrounding environment of the subject at the acquisition time or before and after the acquisition time. The information acquisition module 210 may include a first acquisition unit 410 and a second acquisition unit 420. The first acquisition unit 410 may be configured to collect a first signal or first information including a first signal related to the subject. The second acquisition unit 420 may be configured to collect a second signal or second information including a second signal related to the subject. The first acquisition unit 410 and the second acquisition unit 420 can collect signals in real time. The first signal and the second signal can be collected synchronously at the same time or before and after the time. In some embodiments, in addition to the first acquisition unit 410 and the second acquisition unit 420, the information acquisition module 210 may include one or more other acquisition units ( Figure 4 In some embodiments, the first acquisition unit 410 and the second acquisition unit 420 may be integrated into an independent module or unit.

[0082] In some embodiments, the first acquisition unit 410 may be configured to acquire an ECG signal of a subject. The first acquisition unit 410 may include an ECG monitor ( Figure 4 ECG monitor (not shown). Figure 4 The ECG monitor (not shown) can be of any type, such as a clinical device, a home device, a wearable device, a portable device, etc. Figure 4 The first acquisition unit 410 may include a plurality of electrodes for recording potential changes associated with cardiovascular activity of the subject. The electrodes may be arranged in a 12-pin format, a 5-pin format, a 3-pin format, etc. The electrodes may be located on one or more limbs and / or on the chest of the subject. For example, in a 5-pin format, the electrodes may be located on the chest of the subject. In some embodiments, the first acquisition unit 410 may include a control unit ( Figure 4 The control unit ( Figure 4 (not shown) may be configured as a parameter for controlling the acquisition process. The parameter may include a combination of one or more of a sampling frequency, a sampling time interval, etc. In some embodiments, the first acquisition unit 410 may include a storage unit ( Figure 4 Storage unit ( Figure 4(not shown) can be used to store a combination of one or more of the collected first signals, parameters, etc. In some embodiments, these collected signals and parameters can be stored in any storage device disclosed anywhere in this disclosure.

[0083] In some embodiments, the second acquisition unit 420 may be configured to acquire a PPG signal or to acquire information including a PPG signal. In some embodiments, the second acquisition unit 420 may include a blood oxygen monitor ( Figure 4 Blood oxygen monitor (not shown). Figure 4 The device (not shown) may be configured to collect blood oxygen information of the subject through a photoelectric sensor. The blood oxygen information may be estimated based on two or more PPG signals. In some embodiments, at least one of the collected PPG signals together with the ECG signal may be used to calculate PTT, and PTT may be used to estimate blood pressure values ​​based on a model (for example, refer to International Patent Application No. PCT / CN2015 / 083334 filed on July 3, 2015, and International Patent Application No. PCT / CN2015 / 096498 filed on December 5, 2015).

[0084] In some embodiments, the blood oxygen monitor ( Figure 4The photoelectric sensor may include one or more transmitting ends and one or more receiving ends. The transmitting end may include one or more light sources. The light source may emit one or more of ultrasonic, radio, microwave, millimeter wave, infrared, visible light, ultraviolet, gamma ray, or X-ray electromagnetic radiation. In this article, light may also include any wavelength within the radio, microwave, infrared (IR), visible light, ultraviolet (UV) or X-ray spectrum, and any suitable electromagnetic radiation wavelength may be applicable to the system, device or device disclosed herein. By way of example only, the transmitting end may include two light sources, a red light emitting light source such as a red light emitting diode (LED) and an IR light emitting light source such as an IR LED; the transmitting end may emit light into the subject's tissue at a wavelength for calculating a physiological parameter of interest (e.g., blood oxygen information) of the subject. In this article, for simplicity, a specific wavelength may also include a wavelength within a specific wavelength range. For example, the red light wavelength may be between about 600nm and about 700nm, and the IR wavelength may be between about 800nm ​​and about 1000nm. In an embodiment using a sensor array, each sensor may emit a single wavelength. The receiving end can be used to receive the signal generated by the emitted light passing through the subject. In some embodiments, the second acquisition unit 420 can be configured to collect the PPG signal of the subject from multiple different body positions (for example, a combination of one or more of the head, neck, chest, abdomen, upper limbs, wrists, waist, thighs, knees, ankles, etc.). In some embodiments, one or more photosensors can be placed at any one of the multiple body positions. In some embodiments, one or more photosensor arrays can be placed at any one of the multiple body positions.

[0085] In some embodiments, the second acquisition unit 420 may include a control unit ( Figure 4 not shown) and / or storage unit ( Figure 4 Similarly, the control unit ( Figure 4 The storage unit (not shown) may be configured to control the acquisition process of the second signal or the second information. Figure 4 Not shown) can be configured to store the acquired signals and / or information.

[0086] The information collection module 210 may include one or more other collection units ( Figure 4(not shown in the figure). For example, the collection unit can be configured to collect basic information related to the subject, such as height, weight, age (or date of birth), gender, arm length, nationality, occupation, habits (e.g., health-related habits such as exercise habits), educational background, hobbies, marital status, religious beliefs, health-related history (e.g., whether the subject has a history of smoking, food allergies, drug allergies, medical history, family health history, genetic disease history, information about previous surgeries, etc.), contact information, emergency contacts, etc. The basic information related to the subject can be provided by the subject, other users other than the subject, and / or a related third party (e.g., a doctor, a health care worker, a medical institution, a research institution, an external device of the subject, or a user who has a good relationship with the subject, etc.).

[0087] For another example, the acquisition unit may be configured to acquire environmental information around the subject, including one or more combinations of temperature, humidity, air pressure, air flow, ambient light intensity, etc. The environmental information may be acquired in real-time mode (e.g., at or before the acquisition time), or may be acquired at a certain time interval (e.g., independent of the acquisition time).

[0088] In a further example, one or more acquisition units may be configured to acquire an EMG signal of a subject by a pressure sensing method, acquire a temperature data of a subject by a temperature sensing method, etc. In a further example, the acquisition unit may be configured to acquire a BCG signal, blood concentration information, blood pH information, etc., etc.

[0089] The one or more acquisition units can communicate with the one or more sensors to acquire information sensed, detected or measured by the one or more sensors. Exemplary sensors include one or more combinations of electrode sensors, optical sensors, photoelectric sensors, conductivity sensors, pressure sensors, accelerometers, gravity sensors, temperature sensors, humidity sensors, etc.

[0090] By way of example only, an optical sensor may include an integrated photodetector and a light source. The optical sensor may also include an amplifier. The light source may emit radiation of a combination wavelength of one or more, such as the visible spectrum, the infrared region, etc. The photodetector may detect radiation (or referred to as reflected radiation) generated by light (of a wavelength or wavelength range) that strikes or enters the tissue and / or is reflected by the tissue and reaches the photodetector. The optical sensor may be placed at a body position of the subject to detect a signal related to the subject's pulse. For example, the optical sensor may be a PPG sensor. In some embodiments, the optical sensor may include a plurality of light sources, wherein the light source may emit light of a wavelength or light within a wavelength range. Thus, a plurality of light sources may emit light of various wavelengths, or light within respective wavelength ranges. For example, the light source may emit red light and infrared light. In some embodiments, the optical sensor may include a plurality of photodetectors, wherein the photodetector may be used to detect reflected radiation generated by light of a wavelength or wavelength range. In some embodiments, the photodetector may be used to detect reflected radiation generated by light of various wavelengths or light within respective wavelength ranges. For example, the photodetector may be used to detect reflected radiation generated by red light and infrared light.

[0091] In some embodiments, multiple PPG sensors may be assembled in one device. One of the multiple PPG sensors may include a light source and a photodetector, the light source may emit light of one or a wavelength, and the photodetector may be used to detect reflected radiation generated by the emitted light (one or a wavelength). The multiple PPG sensors may include a PPG sensor having a red light emitting light source and a photodetector that can detect reflected radiation generated by red light, and a PPG sensor having an infrared light emitting light source and a photodetector that can detect reflected radiation generated by infrared light. In some embodiments, at least two of the multiple PPG sensors may be placed at different locations on the subject's body. For example, one PPG sensor may be placed on the subject's upper arm, and another PPG sensor may be placed on the subject's finger. In some embodiments, at least two of the multiple PPG sensors may be placed at the same location or around the same location on the subject's body. For example, two PPG sensors may be placed on the subject's upper arm. For another example, two PPG sensors may be placed on the subject's finger. In some embodiments, the device may include a PPG sensor; the PPG sensor may include multiple light sources and a photodetector, the light sources may emit light of various wavelengths or light within a wavelength range; the photodetector may be used to detect reflected radiation generated by the emitted light of various wavelengths or within respective wavelength ranges.

[0092] The device may be a wearable or portable device, including, for example, a combination of one or more of a T-shirt, a smart watch, a wristband, etc. The device may further include one or more processors or processing units. The processor or processing unit may be configured to control the information acquisition process, or may be configured to perform one or more operations of any module. Signals or data may be transmitted between sensors placed in different locations. The transmission may be via a wireless connection (e.g., a combination of one or more of WiFi, Bluetooth, near field communication (NFC), etc.), a wired connection, etc., or a combination of one or more of the above. For example, the signal received by the sensor may be transmitted via a wireless body sensor network (BSN) or an in-vivo communication (IBC).

[0093] The above description is intended to be illustrative and not to limit the scope of the present disclosure. Many alternatives, modifications, and variations will be apparent to those skilled in the art. The features, structures, methods, and other characteristics of the exemplary embodiments described herein may be combined in various ways to obtain additional and / or alternative exemplary embodiments. For example, the acquisition unit may be integrated into an independent unit that is configured to acquire more than one piece of information or signal related to a subject. At least a portion of the acquisition unit may be integrated into one or more independent units. One or more acquisition units may share a common control unit ( Figure 4 not shown) and / or a common storage unit ( Figure 4 not shown).

[0094] Figure 5 4 is a block diagram of the structure of the second acquisition unit 420 shown in some embodiments of the present disclosure. The second acquisition unit 420 can be configured to acquire a second signal or second information including a second signal related to a subject. In some embodiments, the second acquisition unit 420 can acquire physiological parameters of interest, such as blood oxygen saturation related to the subject. The second acquisition unit 420 may include, but is not limited to, a first detection unit 510, a second detection unit 520, an A / D converter 530, a processing unit 540, and a storage unit 550.

[0095] The first detection unit 510 and / or the second detection unit 520 may be configured to collect one or more pulse wave related signals. The first detection unit 510 and the second detection unit 520 may include at least one transmitting end ( Figure 5 not shown) and at least one receiving end ( Figure 5(not shown), at least one transmitting end is configured to emit one or more lights toward the subject, and at least one receiving end is configured to receive a signal generated by the emitted light. In some embodiments, the transmitting end may include a plurality of light sources, and the light source may emit radiation of one or more wavelengths in, for example, a visible spectrum, an infrared spectrum, a far infrared spectrum, etc. As an example only, the light source may include light of a suitable wavelength, including, for example, a combination of one or more of red light, green light, blue light, infrared light, purple light, yellow light, orange light, etc. For simplicity, the light source of the first detection unit 510 may be referred to as a first light source for emitting a first light, and the light source of the second detection unit 520 may be referred to as a second light source for emitting a second light. The first light source and the second light source may be the same or different. The first light source and the second light source may be separately constructed or combined into an integrated light source or share a light source. The first light and the second light may be emitted alternately, separately or synchronously. The first light and the second light may be the same or different. In some embodiments, the first light may be red light, and the second light may include a radiation beam (e.g., infrared light) with a wavelength at the isosbestic point of oxygenated hemoglobin (HbO) and hemoglobin (Hb).

[0096] In some embodiments, the first detection unit 510 and / or the second detection unit 520 may include one or more receiving terminals ( Figure 5 ). As an example only, the first detection unit 510 may include a first receiving end and the second detection unit 520 may include a second receiving end. The first receiving end and the second receiving end may be the same or different. In some embodiments, the first receiving end and the second receiving end may be integrated into an independent device (for example, the first detection unit 510, the second detection unit 520, or any independent device in the system). In some embodiments, the first detection unit 510 and the second detection unit 520 may share a receiving end. The receiving end may include a photodetector that can detect reflected radiation or transmitted radiation generated by light that impinges on or enters the tissue of the subject and is reflected by the tissue or penetrates the tissue and reaches the receiving end.

[0097] The A / D converter 530 may be configured to convert an analog signal into a digital signal. The signal detected by the first detection unit 510 and / or the second detection unit 520 may be transmitted to the A / D converter 530 in real time or after a time delay. As an example only, during the blood oxygen information acquisition process, at least two pulse wave related signals (e.g., PPG signals) may be detected. The PPG signal may be converted into a digital signal, and the converted digital signal may be stored in the storage unit 550 or any storage device disclosed at any location of the present disclosure. The converted digital signal may be used in a subsequent process (e.g., a calculation process). In some embodiments, the detected signal may be processed by the processing unit 540, and the processed signal may be stored in the storage unit 550. Thereafter, the processed signal may be loaded by the A / D converter.

[0098] The processing unit 540 may be configured to process the signal detected by the first detection unit 510 and / or the second detection unit 520 or the signal stored in the storage unit 550. In some embodiments, processing may be performed to reduce or remove noise, baseline drift, artifacts or other interference in the detected signal. Illustrative processing techniques may include a combination of one or more of low-pass filtering, high-pass filtering, band-pass filtering, phase filtering, wavelet transform, median filtering, morphological filtering, curve fitting, Hilbert transform, statistical analysis such as correlation, etc. A detailed description of the method and system for reducing or removing noise can be found in International Patent Application No. PCT / CN2015 / 077026 filed on April 20, 2015, International Patent Application No. PCT / CN2015 / 077025 filed on April 20, 2015, and International Patent Application No. PCT / CN2015 / 079956 filed on May 27, 2015, the contents of which are incorporated herein by reference.

[0099] In some embodiments, processing may be performed to identify features, such as a direct current (DC) component of a signal, an alternating current (AC) component of a signal, a root mean square value within a specific time period, a peak point, a valley point, an amplitude or ratio, a logarithm, and the like. In this document, the direct current (DC) component of a signal may be referred to as a non-pulsating component or a constant component of a signal. In this document, the alternating current (AC) component of a signal may be referred to as a pulsating component or a time-varying component of a signal. In some embodiments, the signal may be a PPG signal. The PPG signal may be considered to be a signal superposition of a DC component and an AC component, wherein the AC component may be a pulsating component or a time-varying component of a PPG signal, which is caused by the expansion and relaxation of blood; and the DC component may be a non-pulsating component or a constant component of a PPG signal, which is caused by the absorption of light by tissue, non-pulsating blood, and / or venous blood. In some embodiments, processing may be performed to calculate a physiological parameter of interest (e.g., blood oxygen information) based on the detected signal and / or features extracted from the signal. During the calculation of the physiological parameter of interest, features extracted from the detected pulse wave-related signal may be used; and a calibration curve equation, a mathematical calculation model, or a function may be retrieved. During the calculation, parameters such as oxygen saturation coefficient, light attenuation change, light attenuation change ratio between wavelengths, or HbO and Hb concentrations may be determined. By way of example only, when infrared light and red light are applied to the first detection unit 510 and the second detection unit 520, the blood oxygen level (or blood oxygen saturation) may be calculated based on, for example, a synthetic PPG signal. The calculation may include the use of a linear or nonlinear model. In some embodiments, the blood oxygen level may be estimated or calculated based on the ratio of the AC component of the PPG signal to the DC component of the PPG signal.

[0100] The storage unit 550 may be configured to store data, signals, characteristic values, information, or calculation models recorded by the second acquisition unit 420, generated by the second acquisition unit 420, or input to the second acquisition unit 420. The storage unit may include a memory ( Figure 5The storage unit 550 may include one or both of a system memory (e.g., a disk) and a removable memory, the system memory being integrally arranged with the component (e.g., substantially non-removable), and a removable memory being removably connected to the component through, for example, a port (e.g., a USB port, a FireWire port, etc.) or a drive (e.g., a disk drive, etc.). The storage unit 550 may include one or more virtual memory resources (e.g., cloud storage, a virtual private network, and / or other virtual storage resources) or be connected to one or more virtual memory resources for operation. The storage unit 550 may include a hard disk, a floppy disk, a selector memory, a random access memory (RAM), a dynamic random access memory (DRAM), a static random access memory (SRAM), a bubble memory, a thin film memory, a magnetic plated wire memory, a phase change memory, a flash memory, a cloud disk, etc., or a combination of one or more thereof. The storage unit 550 may be connected to or otherwise communicate with other modules or units in the system, the server 120, and the external device 240.

[0101] The second acquisition unit 420 may include one or more other units 560. In some embodiments, the other units 560 may include a controller or a microprocessor or share the same controller or microprocessor with other modules or units inside or outside the second acquisition unit 420. The controller or microprocessor may be configured to control the operating factors or conditions of the first detection unit 510, the second detection unit 520, the A / D converter 530, the processing unit 540, and the storage unit 550. The factors or conditions may include one or more combinations of the on / off of the detection step, the sampling frequency, the detection cycle, the processing parameters, etc.

[0102] The above description is intended to be illustrative and not to limit the scope of the present disclosure. Many alternatives, modifications, and variations will be apparent to those skilled in the art. The features, structures, methods, and other characteristics of the exemplary embodiments described herein may be combined in various ways to obtain additional and / or alternative exemplary embodiments. For example, the storage unit 550 may be integrated in Figure 5 Any of the units described, or the storage unit 550 is not required, and any storage device disclosed anywhere in the present disclosure may be used. In some embodiments, the first detection unit 510 and the second detection unit 520 may be integrated into a separate unit, which is configured to detect more than one pulse wave related signal. In some embodiments, at least a portion of the described units may be integrated into a separate unit. In some embodiments, the second acquisition unit 420 may further include a display device, which is configured to display the acquired signals and / or the determined physiological parameters of interest (e.g., blood oxygen saturation).

[0103] Figure 6 is a block diagram of the structure of the analysis module according to some embodiments of the present disclosure. The analysis module 220 can be connected or communicated with, for example, the external device 240 and the server 120 through the network 150. The analysis module 220 can estimate or calculate the physiological parameters of interest about the subject based on the acquired information. The analysis module 220 may include a preprocessing unit 610, an identification unit 620, a calculation unit 630, and a calibration unit 640.

[0104] The preprocessing unit 610 may be configured to preprocess the acquired information. Preprocessing may be performed to reduce or remove noise or interference in the original signal. For example, complex real-time digital filtering may reduce or remove high-frequency noise in the PPG or ECG waveform. Exemplary methods for preprocessing may include low-pass filtering, band-pass filtering, wavelet transform, median filtering, morphological filtering, curve fitting, Hilbert-Huang transform, etc. or any combination thereof. Descriptions of methods and systems for reducing or removing noise in physiological signals (e.g., PPG signals or ECG signals) may be found in International Patent Application No. PCT / CN2015 / 077026 filed on April 20, 2015, International Patent Application No. PCT / CN2015 / 077025 filed on April 20, 2015, and International Patent Application No. PCT / CN2015 / 079956 filed on May 27, 2015, the contents of which are incorporated herein by reference.

[0105] The pre-processing unit 610 may include one or more pre-processing sub-units ( Figure 6 The preprocessing subunit ( Figure 6 The preprocessing unit 610 may control or coordinate the preprocessing subunits ( Figure 6 The control or coordination may be performed by, for example, a controller ( Figure 6 The pre-processing sub-units may be arranged in series or in parallel.

[0106] This description is for illustrative purposes and does not limit the scope of the claims. Many substitutions, modifications and variations will be apparent to those skilled in the art. The features, structures, methods and other features of the exemplary embodiments described herein may be combined in various ways to obtain additional and / or alternative exemplary embodiments. For example, the pre-processing subunits may be combined in various ways to achieve better pre-processing effects. It should be noted that the pre-processing subunits are not necessary for the system functions. Similar modifications should fall within the boundaries and limits of the claims.

[0107] The recognition unit 620 is configured to analyze the acquired information to identify or confirm features. In some embodiments, the acquired information may be pre-processed before being processed by the recognition unit 620. In the exemplary context of blood pressure monitoring, the acquired information may include a combination of one or more of a PPG signal, an ECG signal, a BCG signal, etc.; exemplary features of the acquired information may include waveforms, feature points, peak points, valley points, amplitudes, time intervals, phases, frequencies, cycles, etc., or any combination thereof.

[0108] The recognition unit 620 can be configured to analyze different types of information or different parts of information. The analysis can be performed by, for example, one or more recognition subunits ( Figure 6 (not shown). For example, the acquired information includes multiple types of physiological signals (e.g., PPG signals and ECG signals), and can be analyzed by different recognition subunits. Exemplary methods that can be used in the recognition unit 620 may include threshold method, syntactic pattern recognition, Gaussian function depression, wavelet transform, QRS composite detection, linear discriminant analysis, quadratic discriminant analysis, decision tree, decision table, neighbor classification, wavelet neural network algorithm, support vector machine, gene expression programming, hierarchical clustering, mean clustering analysis, Bayesian network algorithm, principal component analysis, Kalman filtering, Gaussian regression, linear regression, hidden Markov model, association rules, inductive logic method, etc. or any combination thereof. Multiple methods can be used in parallel or in combination. Just as an example, when processing two types of signals, the recognition unit can use two different methods. For another example, when processing one type of signal, the recognition unit can use two different methods, such as performing another method after one method.

[0109] This description is for illustrative purposes and does not limit the scope of the present disclosure. Many alternatives, modifications, and variations will be apparent to those skilled in the art. The features, structures, methods, and other features of the exemplary embodiments described herein may be combined in various ways to obtain additional and / or alternative exemplary embodiments. By way of example only, the analysis features may be uploaded to a public or personal cloud and may be used for subsequent calculations or calibrations. For another example, the identification subunit ( Figure 6Such modifications should fall within the boundaries and confines of this disclosure.

[0110] The calculation unit 630 can be configured to perform a variety of calculations to determine, for example, coefficients of a model or function regarding a physiological parameter of interest, combinations of one or more of the physiological parameters of interest, etc. For example, the calculation unit 630 can be configured to calculate, for example, different coefficients of a model or function regarding a physiological parameter of interest, different coefficients of different models or functions that reflect the association of the physiological parameter of interest with one or more measurable signals or other information. The calculation unit 630 may include one or more calculation subunits ( Figure 6 The physiological parameters of interest may include, for example, one or any combination of PTT, PTTV (pulse transit time variation), BP, SBP, DBP, pulse rate, heart rate, HRV, heart murmur, blood oxygen saturation concentration, blood density, blood oxygen level, etc.

[0111] Exemplary methods that can be used for the calculation unit 630 may include direct mathematical calculations, indirect mathematical calculations, compensation calculations, vector operations, function operations, wave velocity evaluations, equation parameter evaluations, tension evaluations, etc., or any combination thereof. One or more calculation models can be integrated into the calculation subunit, or the calculation model can be placed in the server 120, or the calculation model can be placed in a public cloud. When calculating different coefficients or physiological parameters, different models can be loaded. For example, a linear calculation model in one calculation subunit can be used to calculate SBP, while another nonlinear calculation model in another calculation subunit can be used to calculate DBP. The initial data or intermediate results used to calculate the physiological parameters of interest can be retrieved or loaded from one of the information acquisition module 210, the analysis module 220, the server 120, the external device 240, etc., or any combination thereof. The initial data and intermediate results can be combined in the calculation unit 630 in a variety of ways.

[0112] This description is for illustrative purposes and does not limit the scope of the present disclosure. Many substitutions, modifications and variations will be apparent to those skilled in the art. The features, structures, methods and other features of the exemplary embodiments described herein may be combined in various ways to obtain additional and / or alternative exemplary embodiments. In one embodiment, the calculated coefficients or calculated physiological parameters may be used as intermediate results for further analysis. For another example, a single physiological parameter of interest or a group of related physiological parameters of interest may be calculated by a computing unit.

[0113] The calibration unit 640 may be configured to perform calibration. Calibration (also referred to as a calibration process or calibration procedure) may include one or more steps: retrieving calibration data (or calibration values) for a subject; obtaining a set of information about the subject using a device to be calibrated or used for future processing (e.g., a wearable or portable device); for a calibration device, determining a calibration model or a portion thereof relative to the subject, etc. One or more combinations thereof. The set of information obtained may include one or more combinations of information provided by the subject or other users other than the subject, or information obtained using the device to be calibrated, etc. A set of calibration data may include specific physiological parameters of interest obtained during the calibration process, information related to specific physiological parameters of interest obtained during the same calibration process.

[0114] By way of example only, the device to be calibrated may calculate blood pressure (including SBP and DBP) based on PTT derived from an ECG waveform acquired using the device and a PPG waveform acquired using the same device or another device. In some embodiments, the device to be calibrated may be part of a system outside the calibration unit 640. A set of calibration data may include SBP and DBP, both of which are measured by healthcare personnel in a hospital environment, and may include a corresponding ECG waveform and a corresponding PPG waveform acquired using the device to be calibrated. The corresponding ECG waveform and the corresponding PPG waveform acquired using the device to be calibrated may correspond to the SBP and DBP measured by the healthcare personnel. The corresponding ECG waveform and the corresponding PPG waveform may be collected using the device to be calibrated at or around the time when the healthcare provider measures the SBP and DBP.

[0115] In some embodiments, a set of calibration data may include SBP, DBP, and a corresponding ECG waveform and a corresponding PPG waveform, all of which are collected using the device to be calibrated. For example, the calibration unit 640 may include or communicate with a cuff blood pressure monitor (see Figure 7). The cuff blood pressure detector can be integrated into the system or device or a part thereof (e.g., a computing unit, an information acquisition module, etc.). For example, a cuff blood pressure detector, an ECG monitor that can collect ECG information, and one or more PPG sensors that can be packaged in a device or system or a part thereof. The cuff blood pressure detector can measure SBP and DBP at a certain time interval (e.g., 15 minutes, 30 minutes, 1 hour, 2 hours, 1 day, etc.). The set of calibration data can be automatically collected according to the default settings of the system or instructions preset by the subject or other users other than the subject (also referred to as third parties). An exemplary third party can be a doctor, a health care worker, a medical institution, a research institution, an external device of the subject, or a user who has a good relationship with the subject, etc. The set of calibration data collected by the calibration unit 640 can be transmitted to the computing unit 630 or other modules or units in real time. The set of calibration data can be stored in a storage device disclosed at any location of the present disclosure, or can be stored in the server 120. If necessary, the set of calibration data can be automatically loaded from the storage device or the server 120.

[0116] One or more sets of calibration data can be used to determine coefficients of the calibration model or other parts of the calibration model. The calibration model can be used in a further process to calculate the physiological parameters of interest based on another set of information obtained using the calibration device. In a further process, the calibration device can obtain a set of information that is the same or similar to the set of information obtained for calibration. For example, the other information may include information obtained using the same device as the calibration process (e.g., a device including one or more sensors), information of the same type as the information obtained in the calibration (e.g., a combination of one or more of the subject's age, the time of acquisition in a day, the subject's physiological condition or psychological condition, etc.), etc. Accordingly, the calibration model can be used to calculate or estimate the physiological parameters of interest. Exemplary methods that can be used for calibration to obtain the calibration model may include a combination of one or more of regression analysis, linear analysis, function operation, reconstruction, Fourier transform, Laplace transform, etc.

[0117] During the calibration process, a set of calibration data may include physiological parameters of particular interest, which are obtained based on measurements using one or more devices other than the device to be calibrated. By way of example only, the physiological parameters of particular interest may be obtained based on measurements performed on a subject by a calibration unit 640 (e.g., a blood pressure cuff monitor). For another example, the physiological parameters of particular interest may be obtained based on measurements performed on a subject by a health care professional in a hospital or doctor's office. For another example, the physiological parameters of particular interest may be obtained based on measurements performed on a subject by a subject or other person using a clinical device or a home device. For example, measuring the physiological parameters of interest may use devices including, for example, one or more of a stethoscope device, an oscilloscope device, an ECG management device, a PPG management device, and the like.

[0118] During the calibration process, a set of calibration data may include specific physiological parameters of interest that are calculated or estimated in advance by the system or a portion thereof. As an example only, the physiological parameters of interest calculated by the system based on a set of collected information and a calibration function in the system can be used for the next calibration to update or generate a calibration model, and the updated calibration model can be used to calculate the physiological parameters of interest in the future (the first aspect of the above-mentioned calibration process). For another example, the physiological parameters of interest calculated by the system based on a set of collected information and a calibration function in the system can be used for the next measurement of the physiological parameters of interest (the second aspect of the above-mentioned calibration process). The calculated physiological parameters of interest of the subject can be stored in a storage device disclosed at any location in the present disclosure or in the server 120 for future use in conjunction with the subject or other subjects.

[0119] In the schematic context of estimating a subject's BP (including SBP and DBP) based on PTT, the correlation between BP and PTT can be represented by a model including mathematical processing and a factorization function, and the factorization function can include a function (f) and a coefficient (B). In this article, calibration can include at least two aspects. The first aspect is to determine a model based on one or more sets of calibration data (or calibration values). The determined model can be referred to as a calibration model. The calibration model is used in a specific measurement, and a signal needs to be collected to provide PTT, and a set of calibration data including PTT0, SBP0, and DBP0. The correlation between BP and PTT may depend on other factors other than PTT. As an example only, the correlation between BP and PTT may depend on HRV and PTTV in addition to PTT. The calibration model is used in a specific measurement, and a signal needs to be collected to provide PTT, HRV, and PTTV, and a set of calibration data including PTT0, SBP0, DBP0, HRV0, and PTTV0.

[0120] The first aspect of calibration can be performed using personalized calibration data, or homogeneous or empirical data related to the subject. This aspect of calibration can be performed in real time while a particular measurement is being performed. A model for estimating BP use based on PTT in a particular measurement can be derived based on one or more sets of calibration data. One or more sets of calibration data can be selected based on PTT in a particular measurement. This aspect of calibration can be performed offline independent of a particular measurement.

[0121] The second aspect of calibration includes collecting a set of calibration data that is applied to the calibration model so that blood pressure can be estimated based on the PTT collected in a particular measurement according to the model and the set of calibration data. In some embodiments, the set of calibration data for a particular measurement can be selected from, for example, multiple sets of calibration data. Multiple sets of calibration data can include personalized data, homogeneous data, or empirical data related to the subject. Multiple sets of calibration data can be stored in the system, such as library 1100 (see Figure 1 ). Multiple sets of calibration data may be stored in a server that is part of the system or accessible from the system. In some embodiments, the set of calibration data may be selected based on the PTT in a particular measurement.

[0122] The calibration model for a specific subject can be based on the calibration data of the same subject. The calibration model for a specific subject can be based on the calibration data of the same subject and the calibration data from a group of subjects (e.g., the same type of data described in other locations of this disclosure). The calibration model for a specific subject can be based on the calibration data from a group of subjects (e.g., the same type of data or empirical data discussed in other locations of this disclosure). A specific subject may be included or not included in the group. The calibration data can be stored in a combination of one or more of the memory or server 120 disclosed in any location of this disclosure. The personalized calibration data of different subjects can be stored in the corresponding personal accounts of each subject in the server 120 or the personal cloud. The calibration data from multiple subjects can be stored in a non-personalized database for future use. For example, the calibration data from multiple subjects can be divided based on one or more features of the corresponding subjects. Exemplary features can include, for example, a combination of one or more of age, sex, stature, weight, body fat percentage, skin color, family health history, lifestyle, exercise habits or other habits, diet, psychological condition, health status, education history, occupation, etc. In some embodiments, a portion of the calibration data so divided (eg, the same type of data described elsewhere in this disclosure) can be used by a group of subjects sharing the same or similar characteristics for calibration purposes.

[0123] This description is for illustrative purposes only and does not limit the scope of the present disclosure. Many alternatives, modifications, and variations will be apparent to those skilled in the art. The features, structures, methods, and other characteristics of the exemplary embodiments described herein may be combined in various ways to obtain additional and / or alternative exemplary embodiments. For example, a storage unit ( Figure 6 The storage unit (not shown) may be added to the calibration unit 640 or the calculation unit 630 or a combination thereof. The storage unit in the calibration unit 640 may store calibration data or historical data about the calibration process. The storage unit associated with the calculation unit 630 may store calculation algorithms or data related to the calculation process. In addition, the same type of data may be used as initial data or intermediate results during calibration.

[0124] The analysis module 220 may be implemented in one or more processors. The various units of the analysis module 220 may be implemented in one or more processors. For example, the preprocessing unit 610, the identification unit 620, the calculation unit 630, and the calibration unit 640 may be implemented in one or more processors. One or more processors may be connected to a memory device ( Figure 6 (not shown), information acquisition modules 1, 2 and 3, external device 240 and server 120 to transmit signals or data. One or more processors can receive signals or data from the memory device ( Figure 6 The processors may retrieve or load signals, information or instructions from the information acquisition modules 1, 2 and 3, the external device 240 and the server 120, and process the signals, information, data or instructions or a combination thereof to perform one or more combinations of one or more calculations, calibrations, etc. of interest. The processor(s) may also be connected or communicated with other devices related to the system 100, and transmit or share signals, information, instructions, physiological parameters of interest, etc. with such other devices through, for example, a combination of one or more of a mobile phone APP, a local or remote terminal, etc.

[0125] Figure 7 7 is a block diagram of the structure of the calibration unit 640 according to some embodiments of the present disclosure. In some embodiments, the calibration unit 640 can be configured to collect one or more sets of calibration data. The calibration unit 640 can perform a calibration process with respect to a physiological parameter of interest, such as blood pressure. The calibration unit 640 can include, but is not limited to, a pneumatic device 710, a controller 720, a memory 730, and a transceiver 740.

[0126] The pneumatic device 710 may be configured to collect a set of calibration data (e.g., a combination of one or more of SBP0, DBP0, etc.). The pneumatic device 710 may be used to generate compressed air pressure and receive a set of data according to the change in air pressure. In some embodiments, the pneumatic device 710 may include a cuff blood pressure detector. The cuff blood pressure detector may include a cuff (not shown), a pump (not shown), a valve (not shown), a pressure sensor (not shown), etc. The cuff blood pressure detector may inflate the cuff and collect a set of calibration data (e.g., a combination of one or more of SBP0, DBP0, etc.). In some embodiments, the cuff may include an air bag placed therein. The pump may be used to provide air to the air bag, or to control the speed or volume of the air supply. The valve may be used to control the on / off of the air supply, the inflation or deflation of the air bag in the cuff. The pressure sensor may be used to detect pressure or pressure fluctuations in real time, or may be used to generate an electrical signal about pressure or pressure fluctuations. In some embodiments, these components may be connected or integrated in the cuff. In some embodiments, some or all of the components may be integrated in the controller 720.

[0127] The controller 720 may be configured to control parameters of a process or operation performed by the calibration unit 640, the pneumatic device 710, or the transceiver 740. The parameters may include one or more combinations of an on / off state, a switching frequency of on or off, a time interval of switching, a time interval of data collection (e.g., 15 minutes, 30 minutes, 1 hour, 2 hours, 5 hours, 10 hours, 12 hours, 1 day, 2 days, 1 week, 2 weeks, 1 month, 2 months, etc.). In some embodiments, one or more control operations of the pneumatic device 710, such as the air supply speed or volume, the inflation or deflation of the airbag, etc., may be performed by the controller 720. In some embodiments, the controller 720 may control the inflation and deflation of the airbag according to the pressure detected by the pressure sensor. For example, when the pressure reaches or exceeds a predetermined value (which may generally be greater than the systolic pressure) and / or remains at a predetermined value for a predetermined period of time, the controller 720 stops inflating the airbag and then triggers deflation of the airbag. The predetermined value and the predetermined time may be set by the system default, the subject, other users other than the subject (e.g., a doctor), etc. During deflation, the pressure may be recorded, processed and / or analyzed to obtain some calibration data, e.g., SBP0 and DBP0 values. In some embodiments, the controller 720 may be configured to process received signals or information, e.g., converting a signal from one format to another (e.g., from an analog signal to a digital signal), calculating SBP0 or DBP0 based on pressure changes, etc. In some embodiments, the controller 720 may be configured to assist in the communication and / or operation of the pneumatic device 710, the memory 730, and the transceiver 740. In some embodiments, the controller 720 may be configured to perform a calibration process to obtain a calibration model that may be used in a calculation process. In some embodiments, the controller 720 may be integrated in the pneumatic device 710.

[0128] The memory 730 may be configured to store data, signals, information, calculation models, or calibration models recorded in the calibration unit 640, generated by the pneumatic device 710, generated by the controller 720 after the calibration process, or input into the calibration unit 640 from the external device 240, the subject, or a user other than the subject. The memory 730 may include a storage device disclosed anywhere in the present disclosure, such as a system memory, a storage module or unit integrated in any module or unit of the system. The memory 730 may be integrated in Figure 6 Any of the units described in the disclosure or the memory 730 is not required, and any storage device disclosed anywhere in the disclosure may be used.

[0129] The transceiver 740 may be configured to receive or transmit data, signals, information or instructions between the pneumatic device 710, the controller 720, the memory 730, the external device 240 or the server 120. One or both of the receiving and transmitting steps may be implemented by a combination of one or more of radio technology, RF technology, telephone or Ethernet, etc. The receiving and transmitting steps may be performed synchronously or asynchronously. In some embodiments, the transceiver 740 may be integrated in the pneumatic device 710 or the controller 720. The transceiver 740 may receive signals or information from other parts of the system. For example, the transceiver 740 may receive the PTTO collected by the calculation unit 530 of the analysis module 220. PTTO may correspond to the blood pressure (including, for example, SBP0 and DBP0) measured by the pneumatic device 710 at or around the time of collecting the signal (e.g., ECG and PPG) used to derive PTTO. The time (including, for example, SBP0 and DBP0) at which the blood pressure is measured by the pneumatic device 710 may be simultaneous with the collection time. The time at which blood pressure is measured by the pneumatic device 710 (including, for example, SBP0 and DBP0) can be separated from the collection time by 1 minute, 2 minutes, 3 minutes, 4 minutes, 5 minutes, 8 minutes, 10 minutes, 15 minutes, 20 minutes, 30 minutes, or 60 minutes.

[0130] In some embodiments, the calibration unit 640 can be configured to automatically perform the calibration process. By way of example only, the calibration process can be performed at a certain time interval. The time interval can be 15 minutes, 30 minutes, 1 hour, 2 hours, 5 hours, 10 hours, 12 hours, 1 day, 2 days, 1 week, 2 weeks, 1 month, 2 months, etc. The time interval can be set by system default, the subject, other users other than the subject (e.g., a doctor), etc. The calibration process can be performed remotely based on instructions provided by, for example, other users other than the subject. For example, a healthcare provider at a first location can provide instructions to perform a calibration process on the subject at a second location away from the first location. For example, the first location can be a doctor's office, the second location can be a ward or the subject's home, and the calibration unit 640 can perform the calibration process accordingly. When a triggering event occurs, the calibration process can be automatically performed. For example, by other components of the system (e.g., based on, for example, Figure 8 The calibration process can be automatically performed when abnormal blood pressure is collected by the controller 720 or other modules or units inside or outside the calibration unit 640. The pump can automatically inflate the airbag at a time interval, so that signals or information about blood pressure can be automatically collected. The collected signals or information can be processed to generate a set of calibration data (e.g., SBP0, DBP0, etc.). The set of calibration data can be loaded by the calculation unit 630 and used in the calculation process. The measurement of blood pressure can be assisted by the signal or information collection for calibration purposes through other modules or units inside or outside the controller 720 or the calibration unit 640.

[0131] Figure 8 Flowchart of an exemplary process for estimating blood pressure according to some embodiments of the present disclosure. Starting at step 810, information including a first signal and a second signal may be acquired. Signal acquisition may be performed by the information acquisition module 210. In some embodiments, the first signal and the second signal may be synchronously acquired at the same time or approximately at the same time. In some embodiments, one signal may be acquired before the other. In some embodiments, information including the first signal or the second signal or related to the first signal or the second signal may be acquired in step 810. For example, blood oxygen information may be acquired. For another example, basic information and / or environmental information related to the subject may be acquired.

[0132] As an example only, the first signal or the second signal may be a physiological signal, such as an ECG signal, a pulse wave related signal (such as PPG), a phonocardiogram (PCG) signal, an impedance cardiogram (ICG) signal, etc., or any combination thereof. In some embodiments, the first signal and the second signal may be of different types. For example, the first signal and the second signal may be a combination of an ECG signal and a PPG signal, a combination of an ECG signal and a PCG signal, a combination of an ECG signal and an ICG signal, etc. In some embodiments, the first signal and the second signal may be of the same type. For example, the first signal and the second signal may be two PPG signals detected at different locations on the subject's body. Exemplary locations on the subject's body may include, for example, fingers, radial arteries, ears, wrists, toes, or locations that are more suitable for dynamic monitoring in the current sensor design.

[0133] In step 820, at least a portion of the acquired information may be preprocessed. In some embodiments, the acquired first signal and second signal may be preprocessed. Preprocessing may be performed to reduce or remove noise or interference in the signal or signal-related data. Exemplary methods that may be used for preprocessing may include low-pass filtering, band-pass filtering, wavelet transform, median filtering, morphological filtering, curve fitting, Hilbert-Huang transform, etc. or any combination thereof. During the preprocessing process, the methods mentioned herein may be used in parallel or in combination. A description of methods and systems for reducing or removing noise in physiological signals (e.g., PPG signals or ECG signals) may be found in International Patent Application No. PCT / CN2015 / 077026 filed on April 20, 2015, International Patent Application No. PCT / CN2015 / 077025 filed on April 20, 2015, and International Patent Application No. PCT / CN2015 / 079956 filed on May 27, 2015, the contents of which are incorporated herein by reference. Furthermore, real-time transformation of the time domain or the frequency domain may also be implemented in step 820, and the signal or related information may be used in the time domain, the frequency domain, or both.

[0134] In step 830, features of the first signal and the second signal may be identified or recognized. In the exemplary context of blood pressure monitoring, the first signal or the second signal may include a PPG signal, an ECG signal, a BCG signal, etc.; exemplary features of the first signal or the second signal may include a combination of one or more of a waveform, a feature point (or a reference point), a peak point, a valley point, an amplitude, a phase, a frequency, a period, etc. For example, the feature point may be a peak or valley of the first signal and a peak or valley of the second signal, such as an R wave peak of an ECG signal, a peak or valley of a PPG signal, the fastest rising point of a PPG signal, a high-order matrix or a high-order derivative of a PPG signal, a pulse area of ​​a PPG signal, a maximum positive peak of S2 of a PCG signal, or a peak of an ICG signal, etc.

[0135] In step 840, parameters can be calculated based on the characteristics of the identified first signal and the second signal. In some embodiments, the time interval between the characteristic points of the first signal and the second signal can be calculated. In one instance, the time interval between the ECG reference point (usually the R peak, but the Q / S peak, or even the peak of the P / T wave can also be used) and the reference point where the marked pulse arrives is called PTT. In another example, the time interval between two pulse wave related signals detected at different locations (e.g., between the carotid artery and the femoral artery) can be used as PTT. Further PTTV can be calculated based on a set of determined PTT approximations. HRV can be determined based on a set of △RR. As used herein, △RR refers to the time interval between two adjacent R waves (maximum points of the QRS waveform).

[0136] Before one or more features of the signal are identified, preprocessing can be performed to evaluate the collected signal (e.g., ECG signal, PPG signal, etc.). For example, the collected ECG signal can be evaluated before one or more features of the signal are identified. An evaluation can be performed to estimate whether a valid ECG signal is collected. The evaluation can be performed, for example, by a pattern recognition process. For example, the R peak of the collected ECG signal can be determined by a pattern recognition process. In some embodiments, the system can identify an abnormal signal or waveform that is not suitable for determining PTT (e.g., an abnormal sinus rhythm R wave, other physiological signals, etc.), so that the abnormal signal or waveform can be excluded to avoid its participation in subsequent calculations or analysis. In some embodiments, the collected ECG signal can be compared with a reference signal to determine whether the collected ECG signal includes an abnormal R wave. The reference signal can be a normal sinus rhythm ECG signal, or can be retrieved from a database including historical data.

[0137] The ECG waveform and the PPG waveform are cyclic signals, that is, the characteristic points appear substantially cyclically or periodically. Therefore, PTT' can be approximately calculated by the time interval between the maximum point on the QRS complex wave on the ECG waveform and the peak point of the subsequent (second) PPG waveform. Similarly, PTT". The value of PTT' and the value of PTT" are greater than the value of PTT. When estimating blood pressure or other physiological parameters of interest based on such inaccurate PTT' and PTT" values, errors or deviations may occur. Such errors or deviations can be avoided or reduced by using a PPG waveform with the same period as the ECG waveform (driven by the same heartbeat). Therefore, during the identification of the characteristic points of the PPG waveform, a threshold value can be set for a time window or segment within which the characteristic points on the PPG waveform can be identified and used to determine PTT. In one example, the time window can be 2 seconds or less. By way of example only, the PPG that appears within 2 seconds from the identification of the maximum point on the ECG waveform is used to determine PTT. An analysis for identifying a reference point on a PPG waveform is performed on a segment of a PPG waveform to approximately calculate PTT. For another example, an analysis for identifying a reference point on a PPG waveform is performed on a segment of a PPG waveform between two consecutive peak points on an ECG waveform to approximately calculate PTT. As another example, the time window can be set according to the heart rate of the subject. For example, the time window can be set according to the heart rate of the subject at or before and after the acquisition time, or the average heart rate of the subject over a period of time, or the average heart rate of a group of people (e.g., a subgroup of people with the same or similar characteristics as the subject; exemplary characteristics may include age, gender, nationality, stature, weight, body fat percentage, skin color, family health history, lifestyle, exercise habits or other habits, diet, occupation, medical history, educational background, marital status, religious beliefs, etc.) One or more combinations of the above can be used to set the time window.

[0138] The ECG cycle or PPG cycle may vary. As an example, the ECG cycle or PPG cycle of different subjects may be different. For another example, the ECG or PPG cycle of the same subject may be different in different situations (for example, when the subject is exercising or sleeping, the subject is at different times of the day, at the same or similar time on different days, etc., one or more combinations). In one example, the time window threshold can be set based on the subject's heart rate (for example, the average person's cycle is about 60-120 times per minute). The heart rate can be an average value over a period of time (for example, a week, a month, a year, etc.). The heart rate can be the heart rate measured before and after the acquisition time or the acquisition time. The heart rate can be measured according to, for example, an ECG signal, a PPG signal, etc. The time window can be set or updated based on the measured heart rate. In another example, the time window can be set according to the subject's physiological information by, for example, a system, a subject, or other users other than the subject. For example, physiological information can include a combination of one or more of exercise, medication, mood, whether there is emotional stress, etc. In another example, the time window can be a fixed value defined by the system, the subject, or other users other than the subject (for example, the subject's doctor, health care personnel, etc.).

[0139] In step 850, a BP (blood pressure) value may be calculated based on the calculated parameters (e.g., a combination of one or more of the determined PTT (pulse transit time), PTTV (pulse transit time variation), and HRV, etc.). The calculation may be performed based on a calibration model. The calibration model may include a linear function or model and a nonlinear function or model. Calibration may be performed in step 860. Calibration may be performed regularly according to the system default instructions set by the subject. Calibration may be performed automatically at certain time intervals according to the system default instructions set by the subject. Calibration may take into account time-varying characteristics. Time-varying characteristics may include, for example, an arterial propagation path of a specific subject, a heart movement of a specific subject, real-time temperature or humidity, an updated baseline BP of a specific subject, an update of a database storing historical data (SBP / DBP values, BP calculation algorithms, etc.) of a specific subject, an update of a database storing reference data of people sharing the same or similar characteristics (e.g., one or more combinations of age, gender, body shape, weight, body fat percentage, skin color, family health history, lifestyle, exercise habits, diet, psychological condition, health condition, education history, occupation, etc.), etc., or any combination thereof.

[0140] In some embodiments, the measurement can be automatically performed according to, for example, a system default setting, an instruction preset by a subject or other user other than the subject (e.g., a doctor). For example, the measurement can be performed at a certain time interval, for example, 15 minutes, 30 minutes, 1 hour, 2 hours, 5 hours, 10 hours, 12 hours, one day, two days, one week, two weeks, one month, two months, etc. For another example, the measurement can be performed in real time. In some embodiments, the measurement can be performed manually by a subject or other user other than the subject (e.g., a doctor).

[0141] Although the foregoing has described what is considered to constitute the present disclosure and / or other examples, it should be understood that various modifications may be made thereto and the subject matter disclosed herein may be implemented in a variety of forms and instances, and the present disclosure may be applied to many applications, only some of which are described here. Those skilled in the art will recognize that the present disclosure may be modified and / or enhanced in a variety of ways. For example, pre-processing step 530 may not be necessary. In addition, if desired, a third signal may be obtained, which may be a signal of the same type as the first signal or the second signal, or may be a signal different from the first signal or the second signal.

[0142] Figure 9-A and Figure 9-B An exemplary process for monitoring physiological parameters according to some embodiments of the present disclosure is provided. Beginning at step 901, a first signal may be collected. The first signal may be an ECG signal. In some embodiments, information including the ECG signal may be collected. The first signal may be collected by the first acquisition unit 410 of the information acquisition module 210, or may be collected by other acquisition units (not shown), or may be input by the subject or other users other than the subject (e.g., a related third party). The first signal may be collected in real time, or may be collected at certain intervals.

[0143] In step 902, second information including multiple second signals may be collected. In some embodiments, the second information may be blood oxygen information of the subject. The second signal may be a PPG signal or other pulse wave related signal. Multiple second signals may be collected synchronously or may be collected one by one. Multiple second signals may be collected in real time or may be collected at certain intervals. Similarly, the second information or multiple second signals may be collected by the second acquisition unit 420 of the information acquisition module 210, or may be collected by other acquisition units (not shown), or may be input by the subject or other users other than the subject (e.g., a related third party).

[0144] As an example only, the second information may be blood oxygen information of the subject. The second signal may be a PPG signal or other pulse wave related signal. During the blood oxygen information acquisition, two PPG signals may be acquired. Two PPG signals may be detected from the subject based on light of two different wavelengths (or within respective wavelength ranges, such as red light and infrared light) emitted by one or more transmitting ends of one or more photosensors. The wavelength may be a combination of one or more of the visible spectrum, the infrared region, and the like. One or more features of the two PPG signals may be extracted. The extraction may be performed by the processing unit 540. The blood oxygen level may be determined or calculated based on the extracted features.

[0145] The first signal and the plurality of second signals may be collected synchronously at the same time or around the same time. In step 911, the collected first signal, the plurality of second signals and the determined blood oxygen information may be uploaded to the personal health manager 1000. The personal health manager 1000 will Fig.10 Described in detail in.

[0146] In step 903, a second signal may be selected from a plurality of second signals. The execution of the selection may be based on, for example, a system default setting, an instruction provided by the subject or other users other than the subject (e.g., a relevant third party), or a combination of one or more preset conditions (if applicable) stored in the system or server 120 that may be triggered. In some embodiments, the preset conditions may be related to parameters of the plurality of second signals, or parameters about the acquisition process of the plurality of second signals. For example, a second signal generated by a specific emission light (e.g., red light) may be selected. For another example, the selection may be performed based on the intensity of the plurality of second signals, for example, an intensity threshold may be set, and a signal whose intensity reaches the intensity threshold may be selected. For a further example, the selection may be performed based on the anti-interference ability of the emission light, for example, a signal generated by green light with strong anti-interference ability may be selected. In some embodiments, the preset conditions may be related to time parameters (e.g., morning, noon, evening, etc.), environmental parameters (e.g., temperature, humidity, air pressure, etc.), health conditions (e.g., whether exercising, taking medicine, good or bad mood, whether there is emotional stress, etc.). The selection may be performed automatically or manually. In some embodiments, the second signal may be a PPG signal. The PPG signal generated by green light with good anti-interference ability can be selected for subsequent processing.

[0147] In step 904, a parameter may be determined based on the identified first signal and the selected second signal. In some embodiments, the parameter may be pulse transit time (PTT). Figure 8. In some embodiments, parameters such as PTTV, HRV, etc. may also be determined. In step 905, one or more optimal models may be retrieved based on a combination of one or more of the subject's personal data, general data, additional information in history, etc. In this article, the optimal model may refer to a model that can provide a more accurate estimate of the physiological parameter of interest from the collected information compared to one or more other models. The information may be obtained from the server 120 or may be measured by various sensors. The sensor may be part of the system 100 or communicate with the system 100. Exemplary sensors may include an accelerometer that can measure the subject's motion during the measurement, a heart rate sensor that can measure the subject's heart rate during the measurement, a GPS receiver or location sensor that can measure the geographic location where the measurement occurs or the subject is located, a temperature sensor that can measure the subject's ambient temperature and / or body temperature at or before and after the collection time, a humidity sensor that can measure the ambient humidity at or before and after the collection time, etc. One or more combinations of the like. The retrieved optimal model may be used to estimate blood pressure based on the collected signal or information.

[0148] In step 906, the system may determine whether calibration data is available. In this context, the calibration data may include a combination of one or more of SBP0, DBP0, PTT0, PTTV0, HRV0, etc. The calibration data may be acquired by the calibration unit 640, or may be acquired by a healthcare provider, or may be acquired by an external device 240, etc. The calibration data may be stored in a memory device disclosed in the calibration unit 640, the server 120, or any other location of the present disclosure. If the answer is "no," at least some of the steps starting from node A 909 may be followed, such as Figure 9-BAs shown. If the answer is "yes", a set of calibration data can be selected in step 907. The selection can be performed according to the system default setting, the instruction input by the subject or other users other than the subject (e.g., a related third party), or the preset conditions as described above. In some embodiments, the preset condition can be a time parameter, which can be expressed as, for example, a time interval for obtaining calibration data. By way of example only, the time interval can be 15 minutes, 20 minutes, 30 minutes, 40 minutes, 1 hour, 2 hours, 5 hours, 10 hours, 12 hours, one day, two days, one week, two weeks, one month, two months, etc. For example, if the time interval is set to 15 minutes, a set of calibration data obtained within the last 15 minutes can be selected for estimating SBP and DBP. In some embodiments, the preset condition can be a condition regarding the physical condition determination process. Physical conditions can include whether to exercise, sleep, take medicine, etc. In this article, a physical condition can correspond to a type of calibration data. By way of example only, if the subject is in motion, the physical condition may be different from the resting state. In this case, a set of calibration data obtained under the same or similar physical condition can be selected. The physiological parameters of the subject may vary with changes in physical condition. Therefore, when the subject is in a specific physical condition, a corresponding set of calibration data may be used to participate in the subsequent calculation of the physiological parameters.

[0149] After selecting the set of calibration data in step 907, the system can proceed to step 908 to calculate the physiological parameters of interest. In some embodiments, the subject's blood pressure can be calculated based on the parameters determined in step 904 (e.g., PTT, PTTV, HRV, etc.), the optimal model selected in step 905, and the set of calibration data selected in step 907. A more detailed description of blood pressure calculation can be found in International Patent Application No. PCT / CN2015 / 083334 filed on July 3, 2015 and International Patent Application No. PCT / CN2015 / 096498 filed on December 5, 2015. In step 910, the calculated physiological parameters of interest can be uploaded to the personal health manager 1000. The personal health manager 1000 can be uploaded to the personal health manager 1000. Fig.10 In some embodiments, after calculating the physiological parameters of interest, the process may proceed to Node B 910 as shown in Section 9-B.

[0150] Figure 9-BThe figure shows a process for calculating or processing physiological parameters starting from node A 909 according to some embodiments of the present disclosure. If no calibration data is available, the system can proceed to step 912 to determine whether to use the system default value. If the answer is "yes", in step 914, the physiological parameters of interest can be calculated according to the default settings of the system. In some embodiments, blood pressure values ​​can be calculated. The default settings of the system can include a set of calibration data, such as (SBP0=120mmHg, DBP0=80mmHg, etc.). In some embodiments, the system can determine whether to use homogeneous data (not shown in Figure 9). In this article, a homogeneous group is defined as a group of people who at least share the same or similar characteristics, such as the same gender, similar age, similar height, similar weight, similar arm length, similar medical history, etc. One or more combinations. It should be noted that in addition to homogeneous data, empirical data can be obtained based on statistical analysis of a group of subject data that is not limited to homogeneous groups. In step 914, homogeneous data can be used as calibration data.

[0151] If the answer is "no", the system can proceed to step 913 to obtain a set of calibration data. The calibration data may include a combination of one or more of SBP0, DBP0, PTT0, PTTV0, HRV0, etc. The set of calibration data may be acquired by the calibration unit 640, or by a healthcare provider, or by an external device 240, etc. The acquisition of the set of calibration data may be triggered automatically or manually. In some embodiments, the calibration data may be automatically acquired at a certain time interval (e.g., 15 minutes, 30 minutes, etc.). In some embodiments, when the system detects that no calibration data is available, the operator (e.g., a doctor) may manually start the calibration data acquisition process. Thereafter, in step 914, the physiological parameters of interest (e.g., blood pressure) may be calculated based on the acquired set of calibration data. The physiological parameters of interest may be uploaded to a server such as a computer system. Fig.10 A personal health manager 1000 is shown.

[0152] In step 915, it can be selected whether to perform a comparison with historical data and / or similar data. The selection can be determined by the system or a part thereof (e.g., according to instructions provided by the subject, other users other than the subject, a third party, or instructions or rules derived by machine learning of previous data, previous behavior of the subject or other users other than the subject), or provided by the subject or other users other than the subject. If the comparison is not selected, the calculated physiological parameters of interest can be uploaded to the personal health manager 1000 in step 911. If the comparison is selected, information related to historical data and / or similar data can be generated in step 910. The relevant information can be stored in a memory device disclosed in the server 120, the analysis module 220, or any other location of the present disclosure. The form of the relevant information can be a combination of one or more of text, graphics, three-dimensional images, codes, voice messages, videos, audio alarms, tactile effects, etc. In step 911, the relevant information can be uploaded to the personal health manager 1000. As an example only, a change curve with historical data can be generated. The change curve can be displayed in real time in the personal health manager 1000.

[0153] This description is intended to illustrate, not to limit the scope of the present disclosure. Many substitutions, modifications, and variations are apparent to those skilled in the art. The features, structures, methods, and other characteristics of the illustrative embodiments described herein may be combined in various ways to obtain additional and / or alternative illustrative embodiments. For example, any collected signal, information, or data may be uploaded to server 120. Any determined or calculated data or intermediate data may be uploaded to server 120.

[0154] Fig.10 It is an example of the composition and organization of the personal health manager 1000 shown in some embodiments of the present application. The personal health manager 1000 can be stored in the server 120, locally stored in the measurement device 110, the terminal 140 connected to or communicating with the system, and the like. For example, the personal health manager 1000 can be stored in the system. For another example, the personal health manager 1000 can be transmitted to a relevant third party (e.g., a hospital). The personal health manager 1000 can be displayed on a combination of one or more of the terminal 140, the display device of the system (not shown), the display device of a third party, and the like. The personal health manager 1000 can be integrated as part of the system, or can be configured as an external device connected or communicating with the system.

[0155] like Fig.10As shown, the personal health manager 1000 may include multiple sections that show multiple physiological parameters, physiological signals or information of interest to the subject. These sections may include cardiac information (e.g., ECG, heart rate, etc.), blood oxygen related information (e.g., SpO2, pulse rate, PPG, etc.), blood pressure related information (e.g., SBP, DBP, calibration data (e.g., SBP0, DBP0, etc.), etc.). The acquired physiological signals, the determined parameters, and the calculated physiological parameters of interest may be displayed in real time on the personal health manager 1000. The subject or a user other than the subject may browse the section by selecting any time point (e.g., 5 a.m., 9 a.m., 10 a.m., 12 a.m., 3 p.m., 8 p.m., etc.) or any time interval (e.g., 15 minutes, 30 minutes, 1 hour, 2 hours, 5 hours, 10 hours, 20 hours, 1 day, 2 days, 1 week, 2 weeks, 1 month, 2 months, etc.) to view information related to the acquired signals, parameters, and information at any time.

[0156] In some embodiments, the personal health manager 1000 may include other relevant information, including, for example, personal information (e.g., a combination of one or more of age, gender, height, weight, medical history, etc.) and external environmental information (e.g., a combination of one or more of temperature, humidity, air quality, ultraviolet intensity, etc.).

[0157] In some embodiments, the personal health manager 1000 may include other relevant information, including, for example, reference information of relevant third parties ( Fig.10 The relevant third party may be a combination of one or more of a doctor, a health care worker, a medical institution, a research institution, etc. The reference information may be generated based on one or both of the calculated state of the physiological parameter of interest and the basic information of the subject. The reference information may be generated based on the relevant information generated in step 910. The reference information may include information about determining a surgical site, or whether anesthesia is required, or a combination of one or more of the type and dosage of medication, etc. The reference information may provide some guidance to the relevant third party when appropriate.

[0158] In some embodiments, the personal health manager 1000 may include other relevant information, including, for example, health tips regarding one or more combinations of sleep, diet, exercise, etc. ( Fig.10Health tips can be retrieved from the server 120 based on a combination of one or more of the subject's basic information, calculated physiological parameters of interest, changes, etc. Relevant third parties (e.g., doctors, health care workers, medical institutions, research institutions, external devices of the subject, or users with a good relationship with the subject, etc.) can enter some health tips in the personal health manager. Similarly, the subject or other users other than the subject can also enter a list of memos about sleep, diet, exercise, etc. The subject can customize the information push about health tips according to options or instructions. A more detailed description of the personal health manager can be found in the international patent application numbered PCT / CN2015 / 096498 filed on December 5, 2015.

[0159] Fig.11 The construction of a mobile device that can be used to implement a particular system for implementing the present disclosure is depicted. In this example, the device (e.g., terminal 140) that can present information about blood pressure monitoring and interact is a mobile device 1100, including but not limited to smartphones, tablets, music players, game consoles, global positioning system (GPS) receivers, and wearable computing devices (e.g., glasses, watches, etc.) or in any other form. The mobile device 1100 in this example includes one or more central processing units (CPUs) 1140, one or more graphics processing units (GPUs) 1130, a display 1120, a memory 1160, a communication platform 1110 (such as a wireless communication module), a storage 1190, and one or more input / output (I / O) devices 1150. The mobile device 1100 may also include any other suitable components, including a system bus or a controller (not shown). As Fig.11 As shown, a mobile operating system 1170 (e.g., iOS, Android, Windows Phone, etc.) and one or more applications 1180 may be loaded into the memory 1160 of the storage 1190 for execution by the CPU 1140. The application 1180 may include a browser or any other suitable mobile application for receiving and presenting information about blood pressure monitoring or other information from the engine 200 of the mobile device 1100. User interaction with the information stream may be implemented through the I / O device 1150, and the user interaction may be provided to the engine 200 and / or other components of the system 100, for example, through the network 150.

[0160] To implement the various modules, units, and their functions described in this disclosure, a computer hardware platform may be used as one or more elements described herein (e.g., with respect to Figure 1-10 and Fig.13The computer system 100 and / or other components of the engine 200 and / or system 100 described herein are hardware platforms. The hardware elements, operating systems, and programming languages ​​of such computers are conventional in nature, and it is assumed that those skilled in the art are sufficiently familiar with these techniques to be able to adapt the techniques for blood pressure monitoring described herein. The computer with the user interface elements can be used to implement a personal computer (PC) or other type of workstation or terminal device, but the computer can also be used as a server if appropriately programmed. It is believed that those skilled in the art are familiar with the structure, programming, and general operation of such computer equipment, and the accompanying drawings should be self-explanatory.

[0161] Fig.12 Depicted is the construction of a computing device that can be used to implement the special system of the present disclosure. In conjunction with this special system herein, there is a functional block diagram illustration of a hardware platform having user interface elements. The computer can be a general-purpose computer or a special-purpose computer. Both can be used to implement the special system of the present disclosure. Computer 1200 can be used to implement any component of the blood pressure monitoring described herein. For example, engine 200, etc. can be implemented in a computer such as computer 1200 through its hardware, software program, firmware or a combination thereof. Although only one such computer is shown for convenience, the computer functions related to the blood pressure monitoring described herein can be implemented in a distributed manner on a large number of similar platforms to disperse the processing load.

[0162] The computer 1200 includes, for example, a communication port 1250, which is connected to a network and facilitates data communication through the network connected thereto. The computer 1200 also includes a central processing unit (CPU) 1220, in the form of one or more processors, for executing program instructions. The exemplary computer platform includes an internal communication bus 1210, a program memory, and different forms of data storage, such as a disk 1270, a read-only memory (ROM) 1230, or a random access memory (RAM) 1240, for storing various data files processed and / or transmitted by the computer and program instructions executed by the CPU. The computer 1200 also includes an input / output component 1260, which supports input / output between the computer and components therein (such as a user interface element 1280). The computer 1200 can also receive programming and data via network communications.

[0163] Thus, aspects of the methods of blood pressure monitoring and / or other processes as described above may be embodied in programming. The programmatic aspects of the technology may be considered to be a "product" or "article of manufacture" typically in the form of executable code and / or associated data carried or embodied in a machine-readable medium. Physical, non-transitory "memory" type media include any or all of the memory or other storage for a computer, processor, etc., or its associated modules, such as various semiconductor memories, tapes, disks, etc., which may provide storage at any time for software programming.

[0164] All or part of the software can communicate from time to time through a network such as the Internet or a variety of other long-distance communication networks. Such communication, for example, can cause the software to be loaded from one computer or processor to another computer or processor, such as from the management server or host computer of the engine 200 to load to the computing environment or the hardware platform of other systems of similar functions of implementing the computing environment or blood pressure monitoring. Therefore, another type of medium that can carry software elements includes light waves, radio waves and electromagnetic waves through wired and optical fixed telephone networks and various air links, such as used on the physical interface between local devices. Physical elements carrying such waves (such as wired or wireless links, optical links, etc.) can also be considered as the medium for carrying software. As used herein, unless limiting the entity "memory" medium, terms such as computer or machine "readable medium" refer to any medium that participates in providing instructions to the processor for execution.

[0165] Therefore, machine-readable media can be in many forms, including physical memory media, carrier media, or physical transmission media. Non-volatile memory media include, for example, optical or magnetic disks, such as memory devices in any computer, etc., which can be used to implement the system shown in the figure or any component thereof. Volatile memory media include dynamic memory, such as the main memory of such a computer platform. Physical transmission media include coaxial cables; copper wires and optical fibers, including wires that form buses within a computer system. Carrier transmission media can be in the form of electrical or electromagnetic signals, or in the form of sound or light waves, such as waves generated during radio frequency (RF) infrared (IR) communications. Therefore, common forms of computer-readable media include, for example: floppy disks, floppy disks, hard disks, magnetic tapes, any other magnetic media, CD-ROMs, DVDs or DVD-ROMs, any other optical media, punch cards, punched paper tapes, any other physical storage media with hole patterns, RAMs, PROMs and EPROMs, FLASH-EPROMs, any other memory chips or key cylinders, carriers that transmit data or instructions, cables or links that transmit such carriers, or any other media from which a computer can read programming code and / or data. Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a physical processor for execution.

[0166] Those skilled in the art will recognize that the present disclosure may be subject to various modifications and / or enhancements. For example, while the implementation of the various components described above may be included in a hardware device, it may also be implemented through a software-only solution, such as a device on an existing server. In addition, the blood pressure monitoring system disclosed herein may be implemented as firmware, a firmware / software combination, a firmware / hardware combination, or a hardware / firmware / software combination.

[0167] Examples

[0168] The following examples are provided for illustrative purposes only and are not intended to limit the scope of the present disclosure.

[0169] The system for measuring one or more physiological parameters of interest may include a test device 1300, an external device 240 ( Fig.13 Not shown, see Figure 2 ) and server 120( Fig.13 Not shown, see Figure 1 ). Fig.13 An exemplary test device 1300 according to some embodiments of the present disclosure is shown. The test device 1300 may include a measurement module 1310, a calibration module 1340, and / or a terminal 1360. The test device 1300 may be connected to or otherwise communicate with the terminal 1360.

[0170] The measurement module 1310 can be configured to collect information, for example, a combination of one or more of an ECG signal, a PPG signal, blood oxygen information, etc. The measurement module 1310 can also be configured to analyze the collected information, or determine or estimate a physiological parameter of interest (e.g., blood pressure), or both. The calibration module 1340 is configured to collect a set of calibration data. The set of calibration data can be transmitted to the measurement module 1310 in real time. In some embodiments, the set of calibration data can be transmitted to the server 120, or can be displayed in the personal health manager 1000. In this embodiment, the calibration module 1340 can be a cuff blood pressure detection device (e.g., a cuff blood pressure detector).

[0171] According to this embodiment, the measurement module 1310 includes an ECG acquisition unit configured to acquire ECG signals by an electrical sensing method, and a blood oxygen acquisition unit configured to acquire blood oxygen related information (e.g., blood oxygen data, multiple PPG signals) by a photoelectric sensing method. The acquired signals or information can be stored in the server 120, or in a storage device ( Fig.13 not shown) or any storage device disclosed anywhere in this disclosure.

[0172] The test device 1300 may be a wearable device, a portable device, a medical monitoring device in a hospital, or a health monitoring device in a home, etc. The test device 1300 may be a monitoring device, as shown in FIG. Fig.13 As shown (some details have been deleted for brevity). The measurement module 1310 includes an ECG acquisition unit and a blood oxygen acquisition unit. It can be seen that a plurality of electrodes 1320 are located on the chest of the subject, and these electrodes are configured to record one or more potential changes of the subject. These potential changes can constitute an ECG waveform, and the ECG waveform can be transmitted to the measurement module 1310 through one or more wires. It can also be seen that one or more photosensors 1330 are located on the subject's fingers, and these photosensors are configured to detect one or more PPG signals or pulse wave related signals. The detected signals can be transmitted to the measurement module 1310 by wire or wirelessly. In this embodiment, one or more photosensors are located on the subject's fingers, and this arrangement or positioning form is provided only for illustrative purposes only. In one embodiment, one or more photosensors may be located on the subject's upper arm.

[0173] According to this embodiment, the calibration module 1340 may include a cuff blood pressure detector. The cuff blood pressure detector may be configured to collect SBP and DBP values, which may be used as calibration data (e.g., a combination of one or more of SBP0, DBP0, PTT0, etc.) during one or more processes of the measurement module 1310. As shown in the figure, the cuff blood pressure detector may include a cuff (see 1340), a pneumatic device ( Fig.13 Not shown), cable 1350, transceiver ( Fig.13 not shown), and / or controller ( Fig.13 The cuff may be characterized by having an internal airtight pocket that can be secured to a portion of a subject to transmit pressure. For example, the cuff may be wrapped around the subject's upper arm to transmit pressure. The pneumatic device may include a pump, a valve, an analog / digital converter, etc. During the calibration data acquisition process, the pneumatic device may inflate the cuff and acquire multiple data (e.g., a combination of one or more of SBP0, DBP0, etc.). The acquired data may be transmitted by cable 1350 to a transceiver ( Fig.13 not shown) for use in subsequent processes.

[0174] The collected ECG signal, PPG signal, calibration data (e.g., a combination of one or more of SBP0, DBP0, PTT0, etc.) can be transmitted to the measurement module 1310 for calculating the blood pressure value of the subject. The calculation can be performed by the measurement module 1310, or can be performed by an analysis module (not shown) integrated in the measurement module 1310. In some embodiments, the measurement module 1310 can be a wearable or portable device, such as Fig.13 As shown, it is independent of and can communicate with one or more photosensors 1330, electrodes 1320 and / or calibration module 1340. In some embodiments, measurement module 1310 can be packaged together with calibration module 1340. For example, measurement module 1310 can be attached to a cuff of calibration module 1340.

[0175] Prior to calculation, one or more operations may be performed, such as one or more combinations of preprocessing, feature identification, parameter estimation, calibration, etc. More descriptions of the analysis can be found in International Patent Application No. PCT / CN2015 / 083334 filed on July 3, 2015 and International Patent Application No. PCT / CN2015 / 096498 filed on December 5, 2015. The calculated physiological parameters of interest can be uploaded to a server such as Fig.10 The personal health manager 1000 shown in FIG. 13 may be displayed in the terminal 1360 or may be sent to a relevant third party (eg, a medical institution). The details may be displayed on the display device of the measurement module 1310 (see FIG. Fig.13 )middle.

[0176] The test device 1300 may also include one or more additional components, including a combination of one or more of a WIFI device, a Bluetooth device, an NFC device, a GPS device, etc. For example, a WIFI device can be used to link to a wireless network. A Bluetooth device can be used for data transmission between some wired or wireless terminals within a certain distance. An NFC device can enable a terminal to establish radio communication within a short distance (10 cm or less). A GPS device can allow a subject to find his or her own location, or a GPS device can be used for a combination of one or more of navigation, etc. Additional components can be connected to or otherwise communicate with the measurement module 1310, the calibration module 1340, the terminal 1360, and the server 120.

[0177] The test device 1300 can be used in a health care institution (e.g., a hospital), or can be used at home. The test device 1300 can be used to monitor physiological parameters in real time. The collected signals, information, data, or calculated physiological parameters of interest can be displayed in real time on a display device (not shown) or in a terminal 1360. The subject, other users other than the subject (e.g., a doctor) can view the relevant information anytime and anywhere. In some embodiments, if the test device 1300 is used at home, the test device 1300 can communicate with a health care provider located far away from the subject's location. The communication can be directly implemented by the test device 1300, or indirectly implemented by, for example, a terminal 1360 carried by the subject. The subject's physiological parameters and location information can be transmitted to the health care provider in real time, periodically, or when a triggering event occurs. Schematic triggering events are described elsewhere in this disclosure. When an emergency occurs, such as when a physiological parameter exceeds a threshold, the health care provider can be notified, the subject can be located based on the positioning information from the GPS or position sensor, and medical services can be provided accordingly.

[0178] The basic concepts have been described above, and it is obvious that those skilled in the art will understand after reading this detailed disclosure that the foregoing detailed disclosure is only for illustration by way of example and is not restrictive. Although not explicitly stated herein, those skilled in the art may make various corrections, improvements and modifications. These, corrections, improvements and modifications are proposed by this disclosure and still fall within the spirit and scope of the exemplary embodiments of the disclosure.

[0179] Moreover, certain terms are used to describe the embodiments of the present disclosure. For example, the terms "one embodiment", "an embodiment", and / or "some embodiments" mean that the specific features, structures or characteristics described about the embodiment are included in at least one embodiment of the present disclosure. Therefore, it should be emphasized and noted that "an embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different parts of this specification does not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments of the present disclosure can be appropriately combined. In addition, the term "logic" represents hardware, firmware, software (or any combination thereof) that performs one or more functions. For example, examples of "hardware" include, but are not limited to, integrated circuits, finite state automata, or even combinational logic. Integrated circuits can be in the form of, for example, a processor of a microprocessor, an application specific integrated circuit, a digital signal processor, a microcontroller, etc.

[0180] In addition, it will be understood by those skilled in the art that various aspects of the present disclosure may be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product, or combination of substances, or any new and useful improvement thereof. Accordingly, various aspects of the present disclosure may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software that may be generally referred to herein as "circuits," "units," "modules," "components," or "systems." In addition, various aspects of the present disclosure may exist in the form of a computer product in one or more computer-readable media, the product containing computer-readable program code.

[0181] A computer readable signal medium may include a propagation data signal containing a computer program code, for example, on a baseband or as part of a carrier wave. Such propagation signals may be in a variety of forms, including electromagnetic forms, optical forms, etc., or suitable combinations. A computer readable signal medium may be any computer readable medium other than a computer readable storage medium, which may be connected to an instruction execution system, device or device to communicate, propagate or transmit a program for use. The program code contained in the computer readable signal medium may be propagated through any suitable medium, including radio, cable, quartz fiber cable, RF, etc., or a suitable combination of any of the above media.

[0182] The computer program code required for the operation of various aspects of the present disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages ​​such as "C" language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby and Groovy, or other programming languages. The program code can be run entirely on the user's computer, or as a separate software package on the user's computer, or partly on the user's computer and partly on a remote computer, or completely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network form, including a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., through the Internet using an Internet service provider), or in a cloud computing environment, or as a service such as software as a service (SaaS).

[0183] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this application, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the claimed processes and methods. Although the above disclosure discusses some embodiments of the present disclosure that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the disclosed embodiments. For example, although the implementation of the various components described above can be included in a hardware device, it can also be achieved only through a software solution, such as installing the described system on an existing server or mobile device. In addition, the financial management system disclosed herein can be implemented as firmware, a firmware / software combination, a firmware / hardware combination, or a hardware / firmware / software combination.

[0184] Similarly, it should be noted that in order to simplify the disclosure to facilitate understanding of one or more various inventive embodiments, the foregoing description of the disclosed embodiments sometimes groups multiple features into one embodiment, figure, or description thereof. However, this disclosure method does not mean that the claimed object requires more features than those mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

Claims

1. A device for monitoring physiological parameters, comprising: A storage device storing instructions; At least one processor executes the instructions to perform operations, the operations comprising: receiving a first signal representing a pulse wave, the first signal being associated with cardiac activity of the subject; receiving a plurality of second signals, the second signals representing time-varying information of the pulse wave of the subject; identifying a first characteristic of the first signal; identifying a second characteristic of one of the plurality of second signals; determining a physiological parameter of the subject according to the first feature, the second feature and a calibration model, wherein the calibration model is determined based on calibration data; The calibration data includes one or a combination of stored calibration data, homogeneous data, empirical data, or collected calibration data related to the subject, wherein the homogeneous data is calibration data of a group of subjects having the same or similar characteristics; wherein one or more optimal calibration models are retrieved based on a combination of the subject's personal data, general data, and additional information in history; The device comprises a calibration unit, the collected calibration data comprises data collected in real time by the calibration unit, the calibration unit comprises a pneumatic device, a controller, a memory and a transceiver, wherein: The controller controls the pneumatic device to inflate and deflate the airbag according to the pressure detected by the pressure sensor, and when the pressure reaches or exceeds a predetermined value and / or remains at the predetermined value for a predetermined period of time, the controller stops inflating the airbag and then triggers deflation of the airbag; The memory is configured to store data, signals, information, calculation models, or calibration models recorded in the calibration unit, generated by the pneumatic device, generated by the controller after a calibration process, or input into the calibration unit from an external device, the subject, or a user other than the subject; The transceiver is configured to receive or transmit data, signals, information or instructions between the pneumatic device, the controller, the memory, the external device or the server; Wherein, a user interface is provided to allow the subject, other users or entities other than the subject to exchange information with the device; the device can be accessed by a login credential with access rights, and the access rights include one or more combinations of rights to access part or all of the information about the subject and rights to update part or all of the information about the subject; The device provides three levels of access rights, wherein the first level includes full access rights to subject information, allowing receipt and update of information about the subject, the second level includes partial access rights to subject information, allowing receipt and update of partial information about the subject, and the third level includes minimum access rights to subject information, allowing receipt or update of partial information about the subject.

2. The device according to claim 1, characterized in that The identifying a second characteristic of one of the plurality of second signals comprises: Based on a preset condition, a second signal is selected from the plurality of second signals, wherein: The preset condition includes one or a combination of intensity thresholds, emission light types, time parameters, environmental parameters or health conditions associated with the multiple second signals.

3. The device according to claim 1, characterized in that The calibration unit collects data based on pressure fluctuations.

4. The device according to claim 1, characterized in that The operations further include: determining the subject's physical condition; Based on the physical condition, the calibration data is determined.

5. The device according to claim 1, characterized in that The operations further include: One or more of an alarm, a suggestion or a prompt related to the physiological parameter is provided through a user interface.

6. The device according to claim 1, characterized in that The operations further include: The physiological parameters are transmitted to a third-party device related to the subject, wherein the third-party device includes an external device of a doctor, a healthcare worker, a medical institution, a research device, or a user who is a relative or friend of the subject.

7. The device according to claim 6, characterized in that The operations further include: The information or instructions fed back by the third-party device are displayed through the user interface.

8. The device according to claim 7, characterized in that The feedback information or instructions include suggestions for actions to be taken by the subject.

9. A method for monitoring physiological parameters according to the device of any one of claims 1 to 8, comprising: receiving a first signal representing a pulse wave, the first signal being associated with cardiac activity of the subject; receiving a plurality of second signals, the second signals representing time-varying information of the pulse wave of the subject; identifying a first characteristic of the first signal; identifying a second characteristic of one of the plurality of second signals; determining a physiological parameter of the subject according to the first feature, the second feature and a calibration model, wherein the calibration model is determined based on calibration data; The calibration data includes one or a combination of stored calibration data, homogeneous data, empirical data, or collected calibration data related to the subject, wherein the homogeneous data is calibration data of a group of subjects having the same or similar characteristics; wherein one or more optimal calibration models are retrieved based on a combination of the subject's personal data, general data, and additional information in history; The device comprises a calibration unit, the collected calibration data comprises data collected in real time by the calibration unit, the calibration unit comprises a pneumatic device, a controller, a memory and a transceiver, wherein: The controller controls the pneumatic device to inflate and deflate the airbag according to the pressure detected by the pressure sensor, and when the pressure reaches or exceeds a predetermined value and / or remains at the predetermined value for a predetermined period of time, the controller stops inflating the airbag and then triggers deflation of the airbag; The memory is configured to store data, signals, information, calculation models, or calibration models recorded in the calibration unit, generated by the pneumatic device, generated by the controller after a calibration process, or input into the calibration unit from an external device, the subject, or a user other than the subject; The transceiver is configured to receive or transmit data, signals, information or instructions between the pneumatic device, the controller, the memory, the external device or the server; Wherein, a user interface is provided to allow the subject, other users or entities other than the subject to exchange information with the device; the device can be accessed by a login credential with access rights, and the access rights include one or more combinations of rights to access part or all of the information about the subject and rights to update part or all of the information about the subject; The device provides three levels of access rights, wherein the first level includes full access rights to subject information, allowing receipt and update of information about the subject, the second level includes partial access rights to subject information, allowing receipt and update of partial information about the subject, and the third level includes minimum access rights to subject information, allowing receipt or update of partial information about the subject.

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