CN111374641B - Sleep characteristic event identification method, apparatus, computer device and storage medium - Google Patents
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Abstract
The embodiment of the invention provides a sleep characteristic event identification method, a sleep characteristic event identification device, computer equipment and a storage medium. The sleep characteristic event identification method comprises the following steps: acquiring an echo signal of a target user to be identified based on a radar; judging whether the target user has a sleep characteristic event according to the echo signal; when the target user generates a sleep characteristic event, acquiring difference characteristics before and after the target user generates the sleep characteristic event; and determining a target characteristic event according to the difference characteristic, wherein the target characteristic event is one of a plurality of sleep characteristic events. The effect of identifying specific sleep characteristic events is achieved.
Description
Technical Field
The embodiment of the invention relates to the technical field of sleep identification, in particular to a sleep characteristic event identification method, a sleep characteristic event identification device, computer equipment and a storage medium.
Background
With the attention of people to personal health status, various miniaturized medical devices are also put into home daily life, such as blood pressure meters, blood glucose meters and the like, and are more and more convenient for self-monitoring of individuals. According to medical research, the quality of human sleep is related to the degree of human health, and the quality of sleep and the sleeping efficiency are related to human characteristic events (turning over, getting off from bed, lying on bed, sitting up and lying down) in sleep, so how to detect human sleep characteristic events becomes an important thing in sleep monitoring.
However, until now, there are no miniaturized apparatuses capable of conveniently monitoring night sleep activities of human bodies, and the conventional professional night sleep monitoring method uses Polysomnography (PSG) technology. Miniaturized sleep monitoring products, such as wrist watches, employ photoplethysmography technology. By adopting the sleep monitoring method, only the sleep state of the human body can be roughly identified, specific sleep characteristic events can not be specifically identified, and the overall sleep monitoring is greatly influenced.
Disclosure of Invention
The embodiment of the invention provides a sleep characteristic event identification method, a sleep characteristic event identification device, computer equipment and a storage medium, so as to realize the effect of identifying specific sleep characteristic events.
In a first aspect, an embodiment of the present invention provides a method for identifying a sleep feature event, including:
acquiring an echo signal of a target user to be identified based on a radar;
judging whether the target user has a sleep characteristic event according to the echo signal;
when the target user generates a sleep characteristic event, acquiring difference characteristics before and after the target user generates the sleep characteristic event;
and determining a target characteristic event according to the difference characteristic, wherein the target characteristic event is one of a plurality of sleep characteristic events.
Optionally, the acquiring, based on the radar, the echo signal of the target user to be identified includes:
transmitting a preset waveform to the target user through the radar;
and receiving an echo signal returned by the target user based on the preset waveform through the radar.
Optionally, the determining, according to the echo signal, whether the target user has a sleep characteristic event includes:
filtering the echo signals;
performing time-frequency conversion on the filtered echo signals to obtain an energy burst curve;
detecting a sleep state of the target user according to the energy burst curve, wherein the sleep state comprises a first event and a second event;
constructing a binary hypothesis, reducing the false alarm rate of the detection of the first event, and improving the detection probability of the second event;
energy burst for first event and second eventTransforming the line, determining the conditional probability density distribution of the first event and the second event, and fitting the data by matlab, wherein the conditional probability density function of the first event is represented as P (Z|H 0 ) The conditional probability density function of the second event is denoted as P (Z|H 1 ),H 0 For the first event, H 1 Is a second event;
and carrying out constant false alarm detection on the numerical value of the energy burst curve to determine whether the target user has sleep characteristic events or not.
Optionally, before the determining whether the target user has a sleep characteristic event according to the echo signal, the method includes:
preprocessing the echo signals to obtain suppressed echo signals;
correspondingly, the judging whether the target user generates the sleep characteristic event according to the echo signal comprises the following steps:
judging whether the target user has sleep characteristic events or not according to the suppressed echo signals.
Optionally, the determining the target feature event according to the difference feature includes:
inputting the difference characteristics into a trained neural network model;
and determining the target characteristic event based on the output result of the neural network model.
Optionally, the difference features include one or more of an energy difference feature, an amplitude difference feature, an entropy difference feature, a target size difference feature, and a position difference feature.
Optionally, the plurality of sleep characteristic events are at least two of sitting up, lying down, getting out of bed, getting in bed, sleeping, and turning over.
In a second aspect, an embodiment of the present invention provides a device for identifying a sleep characteristic event, including:
the echo signal acquisition module is used for acquiring echo signals of target users to be identified based on the radar;
the judging module is used for judging whether the target user has sleep characteristic events according to the echo signals;
the difference feature acquisition module is used for acquiring difference features before and after the sleep feature event occurs to the target user when the sleep feature event occurs to the target user;
and the target characteristic event determining module is used for determining a target characteristic event according to the difference characteristic, wherein the target characteristic event is one of a plurality of sleep characteristic events.
In a third aspect, an embodiment of the present invention provides a computer apparatus, including:
one or more processors;
storage means for storing one or more programs,
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of identifying sleep characteristic events as described in any of the embodiments of the present invention.
In a fourth aspect, embodiments of the present invention provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method for identifying sleep characteristic events according to any embodiment of the present invention.
According to the embodiment of the invention, the echo signal of the target user to be identified is obtained based on the radar; judging whether the target user has a sleep characteristic event according to the echo signal; when the target user generates a sleep characteristic event, acquiring difference characteristics before and after the target user generates the sleep characteristic event; the target characteristic event is determined according to the difference characteristic, and is one of a plurality of sleep characteristic events, so that the problem that the existing sleep monitoring method can only roughly identify what sleep state a human body is in and cannot specifically identify specific sleep characteristic events, and the overall sleep monitoring is greatly influenced is solved, and the effect of identifying specific sleep characteristic events is achieved.
Drawings
Fig. 1 is a flowchart of a method for identifying sleep characteristic events according to a first embodiment of the present invention;
fig. 2 is a flow chart of a method for identifying sleep characteristic events according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a sleep characteristic event recognition device according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart depicts steps as a sequential process, many of the steps may be implemented in parallel, concurrently, or with other steps. Furthermore, the order of the steps may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figures. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Furthermore, the terms "first," "second," and the like, may be used herein to describe various directions, acts, steps, or elements, etc., but these directions, acts, steps, or elements are not limited by these terms. These terms are only used to distinguish one direction, action, step or element from another direction, action, step or element. For example, the first information may be referred to as second information, and similarly, the second information may be referred to as first information, without departing from the scope of the present application. Both the first information and the second information are information, but they are not the same information. The terms "first," "second," and the like, are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
Example 1
Fig. 1 is a flow chart of a sleep characteristic event recognition method according to an embodiment of the present invention, which is applicable to recognizing a specific sleep characteristic event scene.
As shown in fig. 1, a method for identifying sleep characteristic events according to an embodiment of the present invention includes:
s110, acquiring an echo signal of a target user to be identified based on radar.
The echo signal refers to a signal reflected by a target user after the radar transmits a preset waveform. Specifically, the echo signal is received by the radar receiver. Optionally, the echo signal is in the form of Wherein->Representing the echo component of the target user,echo component representing clutter, +.>Representing the echo component of the radar receiver thermal noise. Specifically, the radar may include a portion of energy leakage caused by the radar antenna when receiving the echo signal, which may also include various environmental conditionsThe signals of the species of object, but some are interfering, thereby affecting subsequent detection.
In an alternative embodiment, acquiring echo signals of a target user to be identified based on radar includes:
transmitting a preset waveform to the target user through the radar; and receiving an echo signal returned by the target user based on the preset waveform through the radar.
In this step, the preset waveform is transmitted through the radar transmitting end, and the preset waveform is reflected by the target user to form an echo signal, which is received by the radar receiving end.
S120, judging whether the target user has sleep characteristic events according to the echo signals.
The sleep characteristic event refers to a special sleep event occurring in the sleep events. Specifically, a sleep event refers to a state of deep sleep, and a special sleep event refers to a change in sleep posture. Optionally, sleep characterization events include, but are not limited to, sitting up, lying down, getting out of bed, getting in bed sleep, and turning over, without specific limitation herein. In this step, it is merely determined whether or not the sleep characteristic event has occurred in the target user, and it is impossible to determine which sleep characteristic event is specifically. Specifically, the present embodiment does not limit how to determine whether the target user has a sleep characteristic event according to the echo signal.
In an alternative embodiment, determining whether the target user has a sleep characteristic event according to the echo signal includes:
filtering the echo signals; performing time-frequency conversion on the filtered echo signals to obtain an energy burst curve; detecting a sleep state of the target user according to the energy burst curve, wherein the sleep state comprises a first event and a second event; constructing a binary hypothesis, reducing the false alarm rate of the detection of the first event, and improving the detection probability of the second event; transforming the energy burst curves of the first event and the second event, determining the conditional probability density distribution of the first event and the second event, and fitting the data by matlab, wherein the first eventThe conditional probability density functions of (2) are respectively expressed as P (Z|H 0 ) The conditional probability density function of the second event is denoted as P (Z|H 1 ),H 0 For the first event, H 1 Is a second event; and carrying out constant false alarm detection on the numerical value of the energy burst curve to determine whether the target user has sleep characteristic events or not.
Optionally, the constant false alarm detection mode for the numerical value of the energy burst curve is selected from the self-adaptive logarithmic dual-threshold constant false alarm detector, and the specific steps are as follows:
adaptively estimating the mean and variance of the energy burst curve; wherein adaptively estimating the mean and variance of the energy burst curve comprises: taking the logarithm of the natural number as the base for Z (n), the following formula (10) is expressed:
Z′(n)＝ln|Z(n)|(10)
wherein Z (n) is the value of the nth energy burst curve element; the mean value and variance of the nth logarithm energy burst curve element are obtained through a self-adaptive Gaussian mean algorithm, and the mean value mu (n) is represented by the following formula (11); variance sigma 2 (n) is represented by the following formula (12):
μ(n)＝W·μ(n-1)+(1-W)(βμ(n-1)+(1-β)Z′(n))(11)
σ 2 (n)＝W·σ 2 (n-1)+(1-W)(βσ(n-1) 2 +(1-β)(Z′(n)-μ(n)) 2 ) (12) wherein μ (n-1), σ 2 (n-1) is the mean and variance calculated last time, respectively, β is the weight, W is the binary value, the value of W at the occurrence of a sleep characteristic event is 1, and the value of W at the occurrence of a sleep event is 0, as a trade-off between stability and rapid update; selectively updating μ (n) and σ by W 2 (n)。
Detecting a primary threshold value, wherein the detection of the primary threshold value is used for judging whether the first event or the second event is detected by the detector; wherein the detection of the primary threshold comprises: a first threshold γ is set to check whether the value of each energy burst curve element meets the following formula:
wherein the first threshold gamma represents the threshold of the adaptive logarithmic dual-threshold constant false alarm detector;indicating that when Z' (n) -mu (n) is larger than gamma sigma (n), a second event is detected, wherein the second event is a sleep characteristic event; when Z' (n) -mu (n) is less than or equal to gamma sigma (n), detecting a first event, wherein the first event is a sleep event; n is the number of frames.
Detecting a secondary threshold of duration, wherein the secondary threshold detection is used for reducing the false alarm value of the primary threshold detection; wherein the duration secondary threshold detection comprises: setting a second threshold T, recording T1 when a sleep characteristic element is detected for the first time in one-time threshold detection, recording the duration as T, recording the detection result as nt, recording the sleep characteristic element as 1, recording the sleep element as 0, recording the sum of the values of the sleep characteristic element and the sleep element in a t1+t time period as T1, and comparing the sum with the second threshold T, wherein when T1 is more than or equal to T, a sleep characteristic event occurs; when T1 < T, a sleep event occurs.
S130, when the target user generates a sleep characteristic event, acquiring difference characteristics before and after the target user generates the sleep characteristic event.
Wherein, the difference feature refers to feature values before and after the occurrence of the sleep feature event. Illustratively, if the time at which a sleep characteristic event is detected to occur is 9:00, the extracted characteristics A1 before 9:00 and after 9:00 constitute difference characteristics. In this step, when it is confirmed that the sleep characteristic event occurs, the difference characteristics before and after the sleep characteristic event occurs are acquired.
In an alternative embodiment, the difference features include one or more of an energy difference feature, an amplitude difference feature, an entropy difference feature, a target size difference feature, and a position difference feature.
In this embodiment, the difference feature may be one of an energy difference feature, an amplitude difference feature, an entropy difference feature, a target size difference feature, and a position difference feature, or may be a plurality of energy difference features, amplitude difference features, entropy difference features, target size difference features, and position difference features. Specifically, each difference feature can accurately distinguish between a plurality of sleep feature events. Taking the difference feature as an example of the energy difference feature, the amplitude difference feature, the entropy difference feature, the target size difference feature and the position difference feature, determining a target feature event according to the number of times of confirmation of the sleep feature event. For example, the sitting up is determined from the energy difference feature, the amplitude difference feature, and the entropy difference feature, while the target size difference feature is determined to be out of bed, the position difference feature is determined to be lying down, and the target feature event is sitting up.
Alternatively, the energy difference feature may be calculated by a first formula. Specifically, the first formula is:
Z(n)＝∑ f∈Ψ |X(n,f)|Ψ＝{|f|＞η}；
wherein, X (n, f) is represented as the nth signal, i.eThe frequency value after Fourier transformation, eta, is one of 0.3Hz-0.8Hz, preferably, eta is 0.5Hz.
Alternatively, the amplitude difference characteristic may be calculated by a second formula. Specifically, the second formula is:
the second formula can be regarded as that after the echo signals of the radar are summed in the fast time dimension, the average value of the slow time is calculated.
Optionally, the entropy difference feature may be calculated by a third formula, specifically, the third formula;
in the above formula |X (n, f) k ) What is denoted by I is lightningUp to the kth frequency component of the echo data after fourier transformation in the slow time dimension, Σ f∈Ψ The value range of ψ is-7.5-7.5, namely negative 7.5-positive 7.5.
Optionally, the target size difference feature refers to a change in the size of the target user caused before and after the occurrence of the sleep feature event, and the sleep feature event is identified by extracting the change in the size of the target user before and after the occurrence of the sleep feature event. In particular, specific sleep characteristic events may be determined by determining a change in the altitude of the target user.
Alternatively, the location difference feature refers to a location change difference of the target user. In particular, the difference in position change may be determined by the distance from the target user to the radar.
S140, determining a target characteristic event according to the difference characteristic, wherein the target characteristic event is one of a plurality of sleep characteristic events.
Wherein, the target characteristic event refers to a specific written characteristic event. Optionally, the plurality of sleep events includes, but is not limited to, at least two of sitting up, lying down, getting out of bed, getting in bed, sleeping, and turning over. Preferably, the plurality of sleep characteristic events includes sitting up, lying down, getting out of bed, getting in bed, sleeping and turning over at the same time. The target characteristic event is one of a plurality of sleep characteristic events. For example, when the plurality of sleep characteristic events includes sitting up, lying down, getting out of bed, getting in bed sleep, and turning over at the same time, then the target characteristic event is sitting up, lying down, getting out of bed, getting in bed sleep, or turning over; for another example, when the plurality of sleep characteristic times includes sitting up and lying down, then the target characteristic event is sitting up or lying down without specific limitation herein.
In an alternative embodiment, determining the target feature event from the difference feature includes:
inputting the difference characteristics into a trained neural network model; and determining the target characteristic event based on the output result of the neural network model.
In this embodiment, the neural network model is a complex network system formed by a large number of simple processing units (neurons) widely interconnected, reflecting many basic features of human brain functions, and is a highly complex nonlinear power learning system. Specifically, the neural network model in the present embodiment is trained in a supervised manner. Taking a plurality of sleep characteristic events as sitting up, lying down, leaving from bed, getting to bed for sleeping and turning over as an example, marking training data during training, and respectively marking the training data as sitting up, lying down, leaving from bed, getting to bed for sleeping or turning over for training. Alternatively, the neural network model in this embodiment is composed of 1 input layer, 3 hidden layers, and one output layer. The first hidden layer is composed of 70 neurons, a ReLU activation function is adopted, the second hidden layer and the third hidden layer are respectively composed of 100 neurons and 50 neurons, the ReLU activation function is adopted, and finally, the output layer of the function is activated through a softmax.
According to the technical scheme, the echo signals of the target users to be identified are obtained based on the radar; judging whether the target user has a sleep characteristic event according to the echo signal; when the target user generates a sleep characteristic event, acquiring difference characteristics before and after the target user generates the sleep characteristic event; and determining a target feature event according to the difference feature, wherein the target feature event is one of a plurality of sleep feature events, and determining a specific sleep feature event through the difference feature after determining that the target user has the sleep feature event, so that the technical effect of identifying the specific sleep feature event is achieved.
Example two
Fig. 2 is a flow chart of a method for identifying sleep characteristic events according to a second embodiment of the present invention. The embodiment is further refined in the technical scheme, and is suitable for identifying the specific scene of the sleep characteristic event. The method may be performed by a sleep characteristic event recognition device, which may be implemented in software and/or hardware and may be integrated on a computer device.
As shown in fig. 2, the method for identifying sleep characteristic events according to the second embodiment of the present invention includes:
s210, acquiring an echo signal of a target user to be identified based on radar.
The echo signal refers to a signal reflected by a target user after the radar transmits a preset waveform. Specifically, the echo signal is received by the radar receiver. Optionally, the echo signal is in the form of Wherein->Representing the echo component of the target user,echo component representing clutter, +.>Representing the echo component of the radar receiver thermal noise. Specifically, when receiving an echo signal, the radar may include a part of energy leakage caused by the radar antenna, and the echo signal may further include signals of various objects in the environment, but signals of some objects are interference, so that subsequent detection is affected.
S220, preprocessing the echo signals to obtain suppressed echo signals.
In this step, in particular, the signal preprocessing mainly includes clutter suppression. Since noise exists in the received signal, we improve the signal-to-noise ratio of the signal by reducing the noise; this may be achieved by using conventional clutter suppression methods, optionally linear phase Finite Impulse Response (FIR) filtering or adaptive average clutter suppression methods.
S230, judging whether the target user has sleep characteristic events according to the suppressed echo signals.
In the step, whether the target user has the sleep characteristic event or not is judged according to the suppressed echo signals, so that the judgment result is more accurate.
S240, when the target user generates a sleep characteristic event, acquiring difference characteristics before and after the target user generates the sleep characteristic event.
Wherein, the difference feature refers to feature values before and after the occurrence of the sleep feature event. Illustratively, if the time at which a sleep characteristic event is detected to occur is 9:00, the extracted characteristics A1 before 9:00 and after 9:00 constitute difference characteristics. In this step, when it is confirmed that the sleep characteristic event occurs, the difference characteristics before and after the sleep characteristic event occurs are acquired.
S250, determining a target characteristic event according to the difference characteristic, wherein the target characteristic event is one of a plurality of sleep characteristic events.
Wherein, the target characteristic event refers to a specific written characteristic event. Optionally, the plurality of sleep events includes, but is not limited to, at least two of sitting up, lying down, getting out of bed, getting in bed, sleeping, and turning over. Preferably, the plurality of sleep characteristic events includes sitting up, lying down, getting out of bed, getting in bed, sleeping and turning over at the same time. The target characteristic event is one of a plurality of sleep characteristic events. For example, when the plurality of sleep characteristic events includes sitting up, lying down, getting out of bed, getting into bed sleep, and turning over at the same time, then the target characteristic event is sitting up, lying down, getting out of bed, getting into bed sleep, or turning over, without specific limitation.
According to the technical scheme, the echo signals of the target users to be identified are obtained based on the radar; judging whether the target user has a sleep characteristic event according to the echo signal; when the target user generates a sleep characteristic event, acquiring difference characteristics before and after the target user generates the sleep characteristic event; and determining a target feature event according to the difference feature, wherein the target feature event is one of a plurality of sleep feature events, and determining a specific sleep feature event through the difference feature after determining that the target user has the sleep feature event, so that the technical effect of identifying the specific sleep feature event is achieved.
Example III
Fig. 3 is a schematic structural diagram of a sleep characteristic event recognition device according to a third embodiment of the present invention, where the embodiment is applicable to recognizing a specific sleep characteristic event scene, and the device may be implemented in a software and/or hardware manner and may be integrated on a computer device.
As shown in fig. 3, the apparatus for identifying sleep characteristic events provided in this embodiment may include an echo signal acquisition module 310, a judgment module 320, a difference characteristic acquisition module 330, and a target characteristic event determination module 340, where:
an echo signal obtaining module 310, configured to obtain an echo signal of a target user to be identified based on radar;
a judging module 320, configured to judge whether a sleep characteristic event occurs to the target user according to the echo signal;
a difference feature obtaining module 330, configured to obtain, when the target user has a sleep feature event, difference features before and after the target user has the sleep feature event;
the target feature event determining module 340 is configured to determine a target feature event according to the difference feature, where the target feature event is one of a plurality of sleep feature events.
Optionally, the echo signal obtaining module 310 is specifically configured to transmit, by the radar, a preset waveform to the target user; and receiving an echo signal returned by the target user based on the preset waveform through the radar.
Optionally, the determining module 320 is specifically configured to filter the echo signal; performing time-frequency conversion on the filtered echo signals to obtain an energy burst curve; detecting a sleep state of the target user according to the energy burst curve, wherein the sleep state comprises a first event and a second event; constructing a binary hypothesis, reducing the false alarm rate of the detection of the first event, and improving the detection probability of the second event; transforming the energy burst curves of the first event and the second event, determining the conditional probability density distribution of the first event and the second event, and communicatingThe homolab fits the data, where the conditional probability density functions of the first event are denoted as P (Z|H, respectively 0 ) The conditional probability density function of the second event is denoted as P (Z|H 1 ),H 0 For the first event, H 1 Is a second event; and carrying out constant false alarm detection on the numerical value of the energy burst curve to determine whether the target user has sleep characteristic events or not.
Optionally, the module further comprises:
the preprocessing device is used for preprocessing the echo signals to obtain suppressed echo signals; correspondingly, the judging module 320 is specifically configured to judge whether the sleep characteristic event occurs to the target user according to the suppressed echo signal.
Optionally, the target feature event determining module 340 is specifically configured to input the difference feature into a trained neural network model; and determining the target characteristic event based on the output result of the neural network model.
Optionally, the difference features include one or more of an energy difference feature, an amplitude difference feature, an entropy difference feature, a target size difference feature, and a position difference feature.
Optionally, the plurality of sleep characteristic events are at least two of sitting up, lying down, getting out of bed, getting in bed, sleeping, and turning over.
The sleep characteristic event identification device provided by the embodiment of the invention can execute the sleep characteristic event identification method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. Reference may be made to the description of any method embodiment of the invention for details not explicitly described in this embodiment of the invention.
Example IV
Fig. 4 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention. Fig. 4 illustrates a block diagram of an exemplary computer device 612 suitable for use in implementing embodiments of the invention. The computer device 612 depicted in FIG. 4 is merely an example, and should not be taken as limiting the functionality and scope of use of embodiments of the present invention.
As shown in FIG. 4, computer device 612 is in the form of a general purpose computer device. Components of computer device 612 may include, but are not limited to: one or more processors 616, a memory device 628, and a bus 618 that connects the various system components, including the memory device 628 and the processor 616.
Bus 618 represents one or more of several types of bus structures, including a memory device bus or memory device controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include industry standard architecture (Industry Subversive Alliance, ISA) bus, micro channel architecture (Micro Channel Architecture, MAC) bus, enhanced ISA bus, video electronics standards association (Video Electronics Standards Association, VESA) local bus, and peripheral component interconnect (Peripheral Component Interconnect, PCI) bus.
Computer device 612 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by computer device 612 and includes both volatile and nonvolatile media, removable and non-removable media.
The storage 628 may include computer system readable media in the form of volatile memory, such as random access memory (Random Access Memory, RAM) 630 and/or cache memory 632. Terminal 612 can further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 634 can be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, commonly referred to as a "hard drive"). Although not shown in fig. 4, a magnetic disk drive for reading from and writing to a removable nonvolatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable nonvolatile optical disk such as a Read Only Memory (CD-ROM), digital versatile disk (Digital Video Disc-Read Only Memory, DVD-ROM), or other optical media, may be provided. In such cases, each drive may be coupled to bus 618 through one or more data medium interfaces. The storage 628 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of the embodiments of the present invention.
A program/utility 640 having a set (at least one) of program modules 642 may be stored, for example, in the storage 628, such program modules 642 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 642 generally perform the functions and/or methods of the described embodiments of the present invention.
The computer device 612 may also communicate with one or more external devices 614 (e.g., keyboard, pointing terminal, display 624, etc.), one or more terminals that enable a user to interact with the computer device 612, and/or any terminals (e.g., network card, modem, etc.) that enable the computer device 612 to communicate with one or more other computing terminals. Such communication may occur through an input/output (I/O) interface 622. Moreover, the computer device 612 may also communicate with one or more networks such as a local area network (Local Area Network, LAN), a wide area network (Wide Area Network, WAN) and/or a public network such as the internet via the network adapter 620. As shown in FIG. 4, network adapter 620 communicates with other modules of computer device 612 over bus 618. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with computer device 612, including, but not limited to: microcode, end drives, redundant processors, external disk drive arrays, disk array (Redundant Arrays of Independent Disks, RAID) systems, tape drives, data backup storage systems, and the like.
Processor 616 executes various functional applications and data processing by running programs stored in storage 628, such as implementing a sleep profile recognition method provided by any embodiment of the present invention, the method may include:
acquiring an echo signal of a target user to be identified based on a radar;
judging whether the target user has a sleep characteristic event according to the echo signal;
when the target user generates a sleep characteristic event, acquiring difference characteristics before and after the target user generates the sleep characteristic event;
and determining a target characteristic event according to the difference characteristic, wherein the target characteristic event is one of a plurality of sleep characteristic events.
According to the technical scheme, the echo signals of the target users to be identified are obtained based on the radar; judging whether the target user has a sleep characteristic event according to the echo signal; when the target user generates a sleep characteristic event, acquiring difference characteristics before and after the target user generates the sleep characteristic event; and determining a target feature event according to the difference feature, wherein the target feature event is one of a plurality of sleep feature events, and determining a specific sleep feature event through the difference feature after determining that the target user has the sleep feature event, so that the technical effect of identifying the specific sleep feature event is achieved.
Example five
A fifth embodiment of the present invention further provides a computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements a sleep characteristic event recognition method as provided in any embodiment of the present invention, the method may include:
acquiring an echo signal of a target user to be identified based on a radar;
judging whether the target user has a sleep characteristic event according to the echo signal;
when the target user generates a sleep characteristic event, acquiring difference characteristics before and after the target user generates the sleep characteristic event;
and determining a target characteristic event according to the difference characteristic, wherein the target characteristic event is one of a plurality of sleep characteristic events.
The computer-readable storage media of embodiments of the present invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or terminal. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
According to the technical scheme, the echo signals of the target users to be identified are obtained based on the radar; judging whether the target user has a sleep characteristic event according to the echo signal; when the target user generates a sleep characteristic event, acquiring difference characteristics before and after the target user generates the sleep characteristic event; and determining a target feature event according to the difference feature, wherein the target feature event is one of a plurality of sleep feature events, and determining a specific sleep feature event through the difference feature after determining that the target user has the sleep feature event, so that the technical effect of identifying the specific sleep feature event is achieved.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (9)
1. A method for identifying sleep characteristic events, comprising:
acquiring an echo signal of a target user to be identified based on a radar;
judging whether the target user has a sleep characteristic event according to the echo signal;
when the target user generates a sleep characteristic event, acquiring difference characteristics before and after the target user generates the sleep characteristic event;
determining a target feature event according to the difference feature, wherein the target feature event is one of a plurality of sleep feature events;
wherein the difference features include one or more of an energy difference feature, an amplitude difference feature, an entropy difference feature, a target size difference feature, and a position difference feature; wherein the location discrepancy feature is a location discrepancy of the target user; the position change difference is determined by the distance between the target user and the radar;
wherein the energy difference characteristic is determined by the following formula:
Z(n)＝∑ f∈Ψ |X(n,f)|Ψ＝{|f|＞η}；
wherein, X (n, f) is represented as the nth signal, i.eTaking the frequency value after Fourier transformation, wherein eta is one of 0.3Hz-0.8 Hz;
wherein the amplitude difference characteristic is determined by the following formula:
the amplitude difference characteristic determining formula is used for calculating the average value of slow time after the echo signals of the radar are summed in the fast time dimension;
wherein the entropy difference characteristic is determined by the following formula:
wherein, |X (n, f) k ) What is denoted by i is the kth frequency component, Σ, of the radar echo data after fourier transformation in the slow time dimension f∈Ψ The value range of ψ is-7.5-7.5, namely negative 7.5-positive 7.5.
2. The method for recognizing sleep characteristic events according to claim 1, wherein the radar-based acquisition of echo signals of a target user to be recognized comprises:
transmitting a preset waveform to the target user through the radar;
and receiving an echo signal returned by the target user based on the preset waveform through the radar.
3. The method for identifying sleep characteristic events according to claim 1, wherein said determining whether the target user has a sleep characteristic event based on the echo signal comprises:
filtering the echo signals;
performing time-frequency conversion on the filtered echo signals to obtain an energy burst curve;
detecting a sleep state of the target user according to the energy burst curve, wherein the sleep state comprises a first event and a second event;
constructing a binary hypothesis, reducing the false alarm rate of the detection of the first event, and improving the detection probability of the second event;
transforming the energy burst curves of the first event and the second event, determining the conditional probability density distribution of the first event and the second event, and fitting the data by matlab, wherein the conditional probability density function of the first event is represented as P (Z|H 0 ) The conditional probability density function of the second event is denoted as P (Z|H 1 ),H 0 For the first event, H 1 Is a second event;
and carrying out constant false alarm detection on the numerical value of the energy burst curve to determine whether the target user has sleep characteristic events or not.
4. The method for identifying sleep characteristic events according to claim 1, comprising, before said determining whether the target user has a sleep characteristic event based on the echo signal:
preprocessing the echo signals to obtain suppressed echo signals;
correspondingly, the judging whether the target user generates the sleep characteristic event according to the echo signal comprises the following steps:
judging whether the target user has sleep characteristic events or not according to the suppressed echo signals.
5. The method for identifying sleep characteristic events according to claim 1, wherein said determining a target characteristic event from said difference characteristic comprises:
inputting the difference characteristics into a trained neural network model;
and determining the target characteristic event based on the output result of the neural network model.
6. The method of identifying sleep characteristic events according to claim 1, wherein the plurality of sleep characteristic events are at least two of sitting up, lying down, getting out of bed, getting in bed, sleeping, and turning over.
7. A sleep characteristic event recognition device is characterized in that,
the echo signal acquisition module is used for acquiring echo signals of target users to be identified based on the radar;
the judging module is used for judging whether the target user has sleep characteristic events according to the echo signals;
the difference feature acquisition module is used for acquiring difference features before and after the sleep feature event occurs to the target user when the sleep feature event occurs to the target user;
the target feature event determining module is used for determining a target feature event according to the difference feature, wherein the target feature event is one of a plurality of sleep feature events;
wherein the difference features include one or more of an energy difference feature, an amplitude difference feature, an entropy difference feature, a target size difference feature, and a position difference feature; wherein the location discrepancy feature is a location discrepancy of the target user; the position change difference is determined by the distance between the target user and the radar;
wherein the energy difference characteristic is determined by the following formula:
Z(n)＝∑ f∈Ψ |X(n,f)|Ψ＝{|f|＞η}；
wherein, X (n, f) is represented as the nth signal, i.eTaking the frequency value after Fourier transformation, wherein eta is one of 0.3Hz-0.8 Hz;
wherein the amplitude difference characteristic is determined by the following formula:
the amplitude difference characteristic determining formula is used for calculating the average value of slow time after the echo signals of the radar are summed in the fast time dimension;
wherein the entropy difference characteristic is determined by the following formula:
wherein, |X (n, f) k ) What is denoted by i is the kth frequency component, Σ, of the radar echo data after fourier transformation in the slow time dimension f∈Ψ The value range of ψ is-7.5-7.5, namely negative 7.5-positive 7.5.
8. A computer device, comprising:
one or more processors;
a storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of identifying sleep characteristic events as claimed in any of claims 1-6.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements a method of identifying sleep characteristic events as claimed in any one of claims 1-6.
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