Non-inductive sleep monitoring system, non-inductive sleep monitoring method, equipment and medium

Abstract

The invention provides a non-inductive sleep monitoring system, a non-inductive sleep monitoring method, non-inductive sleep monitoring equipment and a medium. The non-inductive sleep monitoring system comprises a non-inductive sleep monitoring equipment end, a cloud server and a non-inductive sleep monitoring display end, the non-inductive sleep monitoring equipment end is in communication connection with the cloud server, and the cloud server is in communication connection with the non-inductive sleep monitoring display end. According to the method, the BCG signal when the user sleeps is collected through the non-inductive sleep monitoring equipment end, body movement state detection of the user on the bed is achieved through the BCG signal, for example, the condition that the user is in the bed or out of the bed or the body movement state and the like can describe the overall activity condition in the bed is judged, and under the condition that the user is judged to be in the bed, the heart rate / respiration rate parameter of the user is detected through the BCG signal; according to the method and the device, the problem of poor sleep monitoring effect of the user in related technologies is solved, the monitoring accuracy is ensured while the user experience is ensured, and the sleep monitoring effect of the user is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of sleep monitoring, and in particular to a non-sensing sleep monitoring system and a non-sensing sleep monitoring method, equipment, and medium. Background Art

[0002] Sleep apnea syndrome (OSAS) is a disease characterized by obstructive apnea and hypopnea, which is caused by repeated collapse of the upper airway during sleep, leading to airway narrowing or occlusion. The main symptoms are loud snoring, fatigue and daytime sleepiness.

[0003] Sleep monitoring is a standard method for diagnosing the severity of obstructive sleep apnea (OSA, commonly known as "snoring"). Medical-grade monitoring equipment can be used to complete sleep monitoring at home and classify OSA. Through all-night sleep monitoring, the number of apneas and hypopneas that occur during sleep, as well as the lowest blood oxygen saturation at night, sleep time and sleep efficiency, can be detected. OSA can also be diagnosed and its severity determined by calculating the Apnea-Hypopnea Index (AHI, a diagnostic indicator for OSA in adults).

[0004] Current technologies typically use patch-type and wearable sleep monitors for nighttime monitoring. However, prolonged pressure from electrodes and chest straps on human tissue can be harmful. Some sensitive individuals may experience insomnia due to stress when using these devices, making it difficult to effectively monitor the individual's true sleep state.

[0005] Furthermore, unconscious body movements such as turning over and moving during sleep can easily cause electrodes, chest straps, and other devices to fall off, affecting the accuracy of patch-type and wearable sleep monitoring results. Furthermore, patch-type and wearable sleep monitoring devices are expensive, have low utilization rates, and high abandonment rates, making them difficult to widely adopt and providing low sleep monitoring coverage for a large population.

[0006] Therefore, it is necessary to provide a new method to solve the above technical problems. Summary of the Invention

[0007] In order to achieve the above-mentioned purpose and other advantages of the present invention, the first purpose of the present invention is to provide a non-sense sleep monitoring system, including a non-sense sleep monitoring device end, a cloud server, and a non-sense sleep monitoring display end;

[0008] The senseless sleep monitoring device is communicatively connected to the cloud server, and the cloud server is communicatively connected to the senseless sleep monitoring display terminal;

[0009] The non-sensing sleep monitoring device is used to collect the BCG signal of the user while sleeping and send it to the cloud server;

[0010] The cloud server is used to process the received BCG signal, obtain physiological parameter information and send it to the non-sleep monitoring display terminal;

[0011] The non-sensing sleep monitoring display terminal is used to display the received physiological parameter information.

[0012] Furthermore, the non-sensing sleep monitoring device is configured as a smart mattress, and the smart mattress is used to collect BCG signals.

[0013] Furthermore, the smart mattress is provided with a piezoelectric sensor, a main controller, an analog-to-digital converter, and a communication module. The piezoelectric sensor is connected to the main controller via the analog-to-digital converter, and the main controller is connected to the cloud server via the communication module. The piezoelectric sensor is used to collect weak body motion signals caused by the heartbeat and convert them into pseudo-voltage signals. The analog-to-digital converter is used to convert the pseudo-voltage signals output by the piezoelectric sensor into digital signals. The main controller processes the digital signals converted by the analog-to-digital converter to form BCG signals, and sends them to the cloud server via the communication module.

[0014] Furthermore, Socket communication is adopted between the non-sleep monitoring device and the cloud server, and between the cloud server and the non-sleep monitoring display terminal.

[0015] Furthermore, the cloud server includes a cloud server body and a filter. The filter is communicatively connected to the non-sense sleep monitoring device. The cloud server body is connected to the filter. The filter is used to filter the BCG signal sent by the non-sense sleep monitoring device. The cloud server body is used to process the filtered BCG signal to obtain physiological parameter information.

[0016] A second object of the present invention is to provide a method for detecting sleep without a sense of sleep, based on the aforementioned system, comprising the following steps:

[0017] Get BCG signals;

[0018] Preprocessing the BCG signal;

[0019] Real-time state estimation, real-time respiratory rate estimation, and real-time heart rate estimation are performed based on the preprocessed BCG signal.

[0020] Furthermore, the step of preprocessing the BCG signal includes:

[0021] The BCG signal is filtered and normalized.

[0022] Furthermore, the step of performing real-time state estimation based on the preprocessed BCG signal includes:

[0023] Steady state estimation is performed using the BCG respiratory signal characteristic state model, the BCG respiratory signal waveform state model, the BCG heart rate signal characteristic state model, and the BCG heart rate signal waveform state model;

[0024] Short-term and cold start state estimation is performed through the BCG short-term waveform characteristic state model;

[0025] Other feature estimations are performed through the pressure signal state model;

[0026] For steady-state state estimation, the state estimation result is obtained based on the temporal relationship between voting and state;

[0027] The short-term and cold start state estimation results and the state estimation results are fed back and regulated with each other;

[0028] Based on the short-term, cold-start state estimation, other feature estimation results, and comprehensive decision-making of the state estimation results, the in-bed, body movement, or out-of-bed state is obtained.

[0029] Furthermore, the step of preprocessing the BCG signal includes:

[0030] The BCG signal is subjected to DC removal and standardization processing.

[0031] Furthermore, performing real-time estimation of respiratory rate based on the preprocessed BCG signal includes:

[0032] The pre-processed BCG signal is filtered by a bandpass filter bank, the envelope is obtained by Hilbert transform, and the spectrum is obtained by Fourier transform.

[0033] Calculate the spectrum confidence under different bandpass filter groups based on the spectrum sharpness;

[0034] Perform frequency band truncation and heart rate estimation based on spectrum confidence;

[0035] Abnormal heart rate detection and correction are performed based on the heart rate time series information to obtain the heart rate.

[0036] Furthermore, the step of preprocessing the BCG signal includes:

[0037] The BCG signal is subjected to DC removal processing.

[0038] Furthermore, performing real-time heart rate estimation based on the preprocessed BCG signal includes:

[0039] Calculate the autocorrelation function, low-pass filtering, and FFT spectrum estimation of the preprocessed BCG signal;

[0040] Spectrum truncation and respiratory rate estimation are performed based on the spectrum estimation results, and abnormality detection and correction are performed based on the respiratory rate time series information to obtain the respiratory rate.

[0041] A third object of the present invention is to provide a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0042] A fourth object of the present invention is to provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0043] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0044] The present invention provides a non-sensing sleep monitoring system and a non-sensing sleep monitoring method, device, and medium. The non-sensing sleep monitoring device collects BCG signals of a user while sleeping, and uses the BCG signals to detect the user's body movement status in bed. For example, the user's in-bed / out-of-bed status, body movement status, etc. can be judged to describe the overall activity situation in bed. When it is judged that the user is in bed, the user's heart rate / respiratory rate parameters are detected through the BCG signal, which solves the problem of poor sleep monitoring effect on the user in the related art. While ensuring user experience, it also ensures the accuracy of monitoring, thereby improving the sleep monitoring effect of the user.

[0045] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and to implement it according to the contents of the description, the following preferred embodiments of the present invention are described in detail with reference to the accompanying drawings. The specific implementation methods of the present invention are given in detail by the following embodiments and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0047] Figure 1 This is a schematic diagram of the sensorless sleep monitoring system;

[0048] Figure 2 This is a schematic diagram of the sensorless sleep monitoring device;

[0049] Figure 3 This is a schematic diagram of a cloud server;

[0050] Figure 4 This is the overall architecture diagram of the sensorless sleep monitoring system;

[0051] Figure 5 This is a schematic diagram of BCG signal;

[0052] Figure 6 This is the overall architecture diagram of the non-sensing sleep monitoring method;

[0053] Figure 7 is the state estimation flow chart;

[0054] Figure 8 Flowchart for real-time estimation of respiratory rate;

[0055] Figure 9 Flowchart for real-time heart rate estimation;

[0056] Figure 10 Schematic diagram of autocorrelation calculation;

[0057] Figure 11 It is a schematic diagram of low-pass filtering;

[0058] Figure 12 is the respiratory spectrum;

[0059] Figure 13 Schematic diagram of the first band-pass filter;

[0060] Figure 14 Schematic diagram of the second band-pass filter;

[0061] Figure 15 This is an indication of the results of the unconscious sleep monitoring. Figure 1 ;

[0062] Figure 16 This is an indication of the results of the unconscious sleep monitoring. Figure 2 ;

[0063] Figure 17 This is an indication of the results of the unconscious sleep monitoring. Figure 3 ;

[0064] Figure 18 It is a schematic diagram of computer equipment;

[0065] Figure 19 A schematic diagram of a computer-readable storage medium. DETAILED DESCRIPTION

[0066] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. It should be noted that, without conflict, the embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0067] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of the present invention.

[0068] The figure numbers in this application are only used to distinguish the various steps in the scheme and are not used to limit the execution order of the various steps. The specific execution order is subject to the description in the specification.

[0069] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0070] Example 1

[0071] A non-sensing sleep monitoring system 100, such as Figure 1 、 Figure 4 As shown, it includes a non-sense sleep monitoring device end 110, a cloud server 120, and a non-sense sleep monitoring display end 130;

[0072] The senseless sleep monitoring device is communicatively connected to the cloud server, and the cloud server is communicatively connected to the senseless sleep monitoring display terminal;

[0073] The non-sensing sleep monitoring device is used to collect the BCG signal of the user when sleeping and send it to the cloud server; the BCG signal is as follows: Figure 5 shown.

[0074] The cloud server is used to process the received BCG signal, obtain status information, heart rate, respiration, blood oxygen and other physiological parameter information, and send it to the non-sensing sleep monitoring display terminal;

[0075] The non-sensing sleep monitoring display terminal is used to display the received physiological parameter information.

[0076] In some embodiments, the non-sensing sleep monitoring device is configured as a smart mattress, and the smart mattress is used to collect BCG signals.

[0077] Furthermore, if Figure 2 As shown, the smart mattress is equipped with a piezoelectric sensor 111, a main controller 112, an analog-to-digital converter 113, and a communication module 114. The piezoelectric sensor can be a piezoelectric ceramic sensor, a piezoelectric film sensor, or the like. The piezoelectric sensor is connected to the main controller via the analog-to-digital converter, and the main controller is connected to the cloud server via the communication module. The piezoelectric sensor is used to collect weak body motion signals caused by heartbeats and convert them into pseudo-voltage signals. The analog-to-digital converter is used to convert the pseudo-voltage signals output by the piezoelectric sensor into digital signals. The main controller processes the digital signals converted by the analog-to-digital converter to form BCG signals, which are then sent to the cloud server via the communication module.

[0078] In some embodiments, socket communication is used between the sleep monitoring device and the cloud server, and between the cloud server and the sleep monitoring display. Sockets are system API interfaces that encapsulate the TCP / IP protocol suite. This allows programmers to directly use the socket interface to communicate with processes on different hosts without having to worry about the protocol itself.

[0079] In some embodiments, as Figure 3 As shown, the cloud server 120 includes a cloud server body 121 and a filter 122. The filter is communicatively connected to the non-sense sleep monitoring device, and the cloud server body is connected to the filter. The filter is used to filter the BCG signal sent by the non-sense sleep monitoring device, and the cloud server body is used to process the filtered BCG signal to obtain physiological parameter information.

[0080] The filter includes a bandpass filter, a low-pass filter, etc. For example, when performing heart rate estimation, the BCG signal is filtered by a bandpass filter group, and the original BCG signal is as follows: Figure 5 As shown, the signal after bandpass filtering is Figure 13 、 Figure 14 When the respiratory rate is estimated, the BCG signal is filtered by a low-pass filter group. The original BCG signal is as shown in Figure 5 As shown, the signal after low-pass filtering is Figure 11 shown.

[0081] like Figure 6-Figure 9 As shown, the above-mentioned non-sensing sleep monitoring method of the non-sensing sleep monitoring system includes:

[0082] Get BCG signal; the original BCG signal is as follows Figure 5 shown.

[0083] The BCG signal is pre-processed; for example, the BCG signal may be filtered, normalized, etc.

[0084] Real-time state estimation, real-time respiratory rate estimation, and real-time heart rate estimation are performed based on the preprocessed BCG signal.

[0085] This embodiment adopts a multi-model integrated state estimation algorithm to obtain a state estimation result based on the temporal relationship between voting and state, and then estimates the respiratory rate, heart rate, etc.

[0086] In some embodiments, when performing real-time state estimation, such as Figure 7 As shown, the step of preprocessing the BCG signal includes:

[0087] The BCG signal is subjected to processing such as filtering and standardization.

[0088] Furthermore, the step of performing real-time state estimation based on the preprocessed BCG signal includes:

[0089] Steady state estimation is performed using the BCG respiratory signal characteristic state model, the BCG respiratory signal waveform state model, the BCG heart rate signal characteristic state model, and the BCG heart rate signal waveform state model;

[0090] Short-term and cold start state estimation is performed through the BCG short-term waveform characteristic state model;

[0091] Other feature estimations are performed through the pressure signal state model;

[0092] For steady-state state estimation, the state estimation result is obtained based on the temporal relationship between voting and state;

[0093] The short-term and cold start state estimation results and the state estimation results are fed back and regulated with each other;

[0094] Based on the short-term, cold-start state estimation, other feature estimation results, and comprehensive decision-making of the state estimation results, the in-bed, body movement, or out-of-bed state is obtained.

[0095] In some embodiments, when performing real-time heart rate estimation, Figure 8 As shown, the step of preprocessing the BCG signal includes:

[0096] The BCG signal is subjected to DC removal and standardization processing.

[0097] Furthermore, performing real-time estimation of respiratory rate based on the preprocessed BCG signal includes:

[0098] The pre-processed BCG signal is filtered by a bandpass filter group, the envelope is obtained by Hilbert transform, and the spectrum is obtained by Fourier transform. Figure 13 、 Figure 14 As shown, the spectrum diagram is Figure 12 shown.

[0099] Calculate the spectrum confidence under different bandpass filter groups based on the spectrum sharpness;

[0100] Perform frequency band truncation and heart rate estimation based on spectrum confidence;

[0101] Abnormal heart rate detection and correction are performed based on the heart rate time series information to obtain the heart rate.

[0102] In some embodiments, when performing real-time estimation of respiratory rate, such as Figure 9As shown, the step of preprocessing the BCG signal includes:

[0103] The BCG signal is subjected to DC removal processing.

[0104] Furthermore, performing real-time heart rate estimation based on the preprocessed BCG signal includes:

[0105] The autocorrelation function, low-pass filtering, and FFT spectrum estimation are calculated for the pre-processed BCG signal; the autocorrelation calculation results are as follows: Figure 10 As shown, the signal after low-pass filtering is Figure 11 shown.

[0106] Spectrum truncation and respiratory rate estimation are performed based on the spectrum estimation results, and abnormality detection and correction are performed based on the respiratory rate time series information to obtain the respiratory rate.

[0107] The results of the sleep monitoring are as follows: Figure 15-17 As shown, Figure 15 This is a diagram of getting out of bed. The respiratory rate and heart rate are both 0, and the status is also 0, indicating getting out of bed. Figure 16 This is a diagram of body movement. The respiratory rate and heart rate are both 0, and the state is also 1, indicating body movement. Figure 17 This is a schematic diagram of being in bed. The respiratory rate is not 0, for example, the monitored respiratory rate is 9, the heart rate is not 0, for example, the monitored heart rate is 65, and the state is 3, indicating being in bed.

[0108] This embodiment provides a non-sensing sleep monitoring system, which collects BCG signals of the user while sleeping through the non-sensing sleep monitoring device, and detects the user's body movement status in bed through the BCG signals. For example, the user's in-bed / out-of-bed status, body movement status, etc. can be judged to describe the overall activity situation in bed. When it is judged that the user is in bed, the user's heart rate / respiratory rate parameters are detected through the BCG signal, which solves the problem of poor sleep monitoring effect of the user in the related art. While ensuring user experience, it also ensures the accuracy of monitoring, thereby improving the sleep monitoring effect of the user.

[0109] Example 2

[0110] A non-sense sleep monitoring method is based on the above-mentioned non-sense sleep monitoring system. For a detailed description of the non-sense sleep monitoring system, please refer to the corresponding description in the above-mentioned non-sense sleep monitoring system embodiment, which will not be repeated here. Figure 6-Figure 9 As shown, the method includes the following steps:

[0111] Get BCG signal; the original BCG signal is as follows Figure 5 shown.

[0112] The BCG signal is pre-processed; for example, the BCG signal may be filtered, normalized, etc.

[0113] Real-time state estimation, real-time respiratory rate estimation, and real-time heart rate estimation are performed based on the preprocessed BCG signal.

[0114] This embodiment adopts a multi-model integrated state estimation algorithm to obtain a state estimation result based on the temporal relationship between voting and state, and then estimates the respiratory rate, heart rate, etc.

[0115] In some embodiments, when performing real-time state estimation, such as Figure 7 As shown, the step of preprocessing the BCG signal includes:

[0116] The BCG signal is subjected to processing such as filtering and standardization.

[0117] Furthermore, the step of performing real-time state estimation based on the preprocessed BCG signal includes:

[0118] Steady state estimation is performed using the BCG respiratory signal characteristic state model, the BCG respiratory signal waveform state model, the BCG heart rate signal characteristic state model, and the BCG heart rate signal waveform state model;

[0119] Short-term and cold start state estimation is performed through the BCG short-term waveform characteristic state model;

[0120] Other feature estimations are performed through the pressure signal state model;

[0121] For steady-state state estimation, the state estimation result is obtained based on the temporal relationship between voting and state;

[0122] The short-term and cold start state estimation results and the state estimation results are fed back and regulated with each other;

[0123] Based on the short-term, cold-start state estimation, other feature estimation results, and comprehensive decision-making of the state estimation results, the in-bed, body movement, or out-of-bed state is obtained.

[0124] In some embodiments, when performing real-time heart rate estimation, Figure 8 As shown, the step of preprocessing the BCG signal includes:

[0125] The BCG signal is subjected to DC removal and standardization processing.

[0126] Furthermore, performing real-time estimation of respiratory rate based on the preprocessed BCG signal includes:

[0127] The pre-processed BCG signal is filtered by a bandpass filter group, the envelope is obtained by Hilbert transform, and the spectrum is obtained by Fourier transform. Figure 13 、 Figure 14As shown, the spectrum diagram is Figure 12 shown.

[0128] Calculate the spectrum confidence under different bandpass filter groups based on the spectrum sharpness;

[0129] Perform frequency band truncation and heart rate estimation based on spectrum confidence;

[0130] Abnormal heart rate detection and correction are performed based on the heart rate time series information to obtain the heart rate.

[0131] In some embodiments, when performing real-time estimation of respiratory rate, such as Figure 9 As shown, the step of preprocessing the BCG signal includes:

[0132] The BCG signal is subjected to DC removal processing.

[0133] Furthermore, performing real-time heart rate estimation based on the preprocessed BCG signal includes:

[0134] The autocorrelation function, low-pass filtering, and FFT spectrum estimation are calculated for the pre-processed BCG signal; the autocorrelation calculation results are as follows: Figure 10 As shown, the signal after low-pass filtering is Figure 11 shown.

[0135] Spectrum truncation and respiratory rate estimation are performed based on the spectrum estimation results, and abnormality detection and correction are performed based on the respiratory rate time series information to obtain the respiratory rate.

[0136] The results of the sleep monitoring are as follows: Figure 15-17 As shown, Figure 15 This is a diagram of getting out of bed. The respiratory rate and heart rate are both 0, and the status is also 0, indicating getting out of bed. Figure 16 This is a diagram of body movement. The respiratory rate and heart rate are both 0, and the state is also 1, indicating body movement. Figure 17 This is a schematic diagram of being in bed. The respiratory rate is not 0, for example, the monitored respiratory rate is 9, the heart rate is not 0, for example, the monitored heart rate is 65, and the state is 3, indicating being in bed.

[0137] This embodiment provides a non-sensing sleep monitoring method, which is based on the non-sensing sleep monitoring system provided in Example 1. The non-sensing sleep monitoring device collects the BCG signal of the user while sleeping, and uses the BCG signal to detect the user's body movement status in bed. For example, the user's in-bed / out-of-bed status, body movement status, etc. can be judged to describe the overall activity situation in bed. When it is judged that the user is in bed, the user's heart rate / respiratory rate parameters are detected through the BCG signal, which solves the problem of poor sleep monitoring effect on the user in the related art. While ensuring the user experience, it also ensures the accuracy of monitoring, thereby improving the sleep monitoring effect of the user.

[0138] Example 3

[0139] A computer device 200, such as Figure 18 As shown, the present invention includes a memory 210, a processor 220, and a computer program 230 stored in the memory and executable on the processor. When the processor executes the computer program, the steps of a method for detecting sleep without a sense of sleep are implemented. For a detailed description of the method, please refer to the corresponding description in the above method embodiment, and will not be repeated here.

[0140] Example 4

[0141] A computer-readable storage medium such as Figure 19 As shown, a computer program is stored thereon, and when the computer program is executed by the processor, the steps of a method for detecting sleep without a sense of sleep are implemented. For a detailed description of the method, reference can be made to the corresponding description in the above method embodiment, and no further details will be given here.

[0142] The number of devices and processing scales described herein are intended to simplify the description of the present invention. Applications, modifications, and variations of the present invention will be readily apparent to those skilled in the art.

[0143] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the description and implementation methods. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and illustrations shown and described herein.

[0144] The apparatus, computer device, non-volatile computer storage medium, and method provided in the embodiments of this specification correspond to each other. Therefore, the apparatus, computer device, and non-volatile computer storage medium also have similar beneficial technical effects as the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the corresponding apparatus, computer device, and non-volatile computer storage medium will not be repeated here.

[0145] Those skilled in the art will also appreciate that, in addition to implementing the controller in pure computer-readable program code, it is entirely possible to implement the same functionality by programming the method steps logically, such as through logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be considered structures within the hardware component. Alternatively, the devices for implementing various functions can be considered both software units implementing the method and structures within the hardware component.

[0146] The systems, devices, or units described in the above embodiments can be implemented by computer chips or physical devices, or by products with certain functions. For ease of description, the above devices are described separately by function, with each unit described separately. Of course, when implementing one or more embodiments of this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware components.

[0147] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0148] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0149] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0150] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0151] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0152] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program units. Generally, program units include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program units may be located in local and remote computer storage media, including storage devices.

[0153] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0154] The foregoing is merely an example of the present invention and is not intended to limit the present invention to one or more embodiments. It will be apparent to those skilled in the art that various modifications and variations may be made to the present invention to one or more embodiments. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention to one or more embodiments shall be included within the scope of the claims of the present invention to one or more embodiments.

Claims

1. A non-sensing sleep monitoring system, characterized by: Including the non-sense sleep monitoring device, cloud server, and non-sense sleep monitoring display terminal; The senseless sleep monitoring device is communicatively connected to the cloud server, and the cloud server is communicatively connected to the senseless sleep monitoring display terminal; The non-sensing sleep monitoring device is used to collect the BCG signal of the user while sleeping and send it to the cloud server; The cloud server is used to process the received BCG signal, obtain physiological parameter information and send it to the non-sleep monitoring display terminal; The non-sensing sleep monitoring display terminal is used to display the received physiological parameter information.

2. The non-sensing sleep monitoring system according to claim 1, characterized in that: The non-sensing sleep monitoring device is configured as a smart mattress, and the smart mattress is used to collect BCG signals.

3. The non-sensing sleep monitoring system according to claim 2, characterized in that: The smart mattress is equipped with a piezoelectric sensor, a main controller, an analog-to-digital converter, and a communication module. The piezoelectric sensor is connected to the main controller via the analog-to-digital converter, and the main controller is connected to the cloud server via the communication module. The piezoelectric sensor is used to collect weak body motion signals caused by heartbeats and convert them into pseudo-voltage signals. The analog-to-digital converter is used to convert the pseudo-voltage signals output by the piezoelectric sensor into digital signals. The main controller processes the digital signals converted by the analog-to-digital converter to form BCG signals, and sends them to the cloud server via the communication module.

4. The non-sensing sleep monitoring system according to claim 1, characterized in that: Socket communication is used between the non-sleep monitoring device and the cloud server, and between the cloud server and the non-sleep monitoring display terminal.

5. The non-sensing sleep monitoring system according to claim 1, characterized in that: The cloud server includes a cloud server body and a filter. The filter is communicatively connected to the non-sense sleep monitoring device. The cloud server body is connected to the filter. The filter is used to filter the BCG signal sent by the non-sense sleep monitoring device. The cloud server body is used to process the filtered BCG signal to obtain physiological parameter information.

6. A method for detecting sleep without any sense of sleep, based on the system for detecting sleep without any sense of sleep according to any one of claims 1 to 5, characterized in that: The following steps are involved: Get BCG signals; Preprocessing the BCG signal; Real-time state estimation, real-time respiratory rate estimation, and real-time heart rate estimation are performed based on the preprocessed BCG signal.

7. The method for monitoring sleep without feeling according to claim 6, wherein: The step of pre-processing the BCG signal comprises: The BCG signal is filtered and normalized.

8. The method for detecting sleep without feeling according to claim 7, wherein: The step of performing real-time state estimation based on the pre-processed BCG signal comprises: Steady state estimation is performed using the BCG respiratory signal characteristic state model, the BCG respiratory signal waveform state model, the BCG heart rate signal characteristic state model, and the BCG heart rate signal waveform state model; Short-term and cold start state estimation is performed through the BCG short-term waveform characteristic state model; Other feature estimations are performed through the pressure signal state model; For steady-state state estimation, the state estimation result is obtained based on the temporal relationship between voting and state; The short-term and cold start state estimation results and the state estimation results are fed back and regulated with each other; Based on the short-term, cold-start state estimation, other feature estimation results, and comprehensive decision-making of the state estimation results, the in-bed, body movement, or out-of-bed state is obtained.

9. The method for monitoring sleep without feeling according to claim 6, wherein: The step of pre-processing the BCG signal comprises: The BCG signal is subjected to DC removal and standardization processing.

10. The method for monitoring sleep without feeling according to claim 6, wherein: Real-time estimation of respiratory rate based on preprocessed BCG signal includes: The pre-processed BCG signal is filtered by a bandpass filter bank, the envelope is obtained by Hilbert transform, and the spectrum is obtained by Fourier transform. Calculate the spectrum confidence under different bandpass filter groups based on the spectrum sharpness; Perform frequency band truncation and heart rate estimation based on spectrum confidence; Abnormal heart rate detection and correction are performed based on the heart rate time series information to obtain the heart rate.

11. The method for detecting sleep without feeling according to claim 6, wherein: The step of pre-processing the BCG signal comprises: The BCG signal is subjected to DC removal processing.

12. The method for detecting sleep without feeling according to claim 11, wherein: Real-time heart rate estimation based on preprocessed BCG signals includes: Calculate the autocorrelation function, low-pass filtering, and FFT spectrum estimation of the preprocessed BCG signal; Spectrum truncation and respiratory rate estimation are performed based on the spectrum estimation results, and abnormality detection and correction are performed based on the respiratory rate time series information to obtain the respiratory rate.

13. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 6 to 12 are implemented.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 6 to 12 are implemented.

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