Sleep analysis interval detection method and system

Abstract

The present invention relates to a sleep analysis period detection method and system, which is performed in a system including a layer unit, a sensor unit, a memory unit, and a data analysis unit, and includes the steps of: sensing a user's biological signal from the sensor unit provided in the layer unit; storing the user's biological signal sensed by the sensor unit in a memory unit; and analyzing the user's biological signal stored in the memory unit using the data analysis unit to detect the user's sleep analysis period.

Description

[Technical Field]

[0001] The present invention relates to a sleep analysis interval detection method and system, and more particularly to a sleep analysis interval detection method and system that collects biosignals of a user positioned on a mattress in a non-contact manner and detects an effective analysis interval required for sleep analysis of the user using the collected user biosignals. [Background technology]

[0002] Recently, as people's standard and quality of living have improved, interest in "good sleep" has increased, and in particular, industries offering various sleep guidance devices or services using the latest science and technology have grown significantly. In response to this, many devices and services for improving sleep quality have been commercialized, such as a sleep care service that provides advice on sleep environment, habits, posture, etc. through consultation with an expert, and a service that monitors sleep status by detecting the user's breathing sound when wearing a wearable device. Providing people with "good sleep" consulting may be accompanied by a polysomnography test to diagnose various normal or abnormal conditions during the user's sleep.

[0003] A polysomnography test is a test that collects a user's physical information using various devices and mechanisms during the user's sleep period and analyzes the user's comprehensive sleep state based on the collected physical information. That is, a polysomnography test may use various testing equipment to diagnose the user's sleep state. For example, an electroencephalogram (EEG) may be used to determine brain function, an electrooculogram (EOG) may be used to check eye movements, an electromyogram (EMG) may be used to check muscle conditions, an electrocardiogram (ECG) may be used to check cardiac rhythms, and video recording may be used to check the overall condition. The test may be conducted over a typical night's sleep.

[0004] That is, in a polysomnography test, a skilled specialist turns on and off the test equipment at the start and end of a user's sleep to determine a sleep analysis period called TRT (Total Recording Time), and the TRT is used to calculate parameters that analyze the user's sleep state. For example, the user's sleep efficiency can be calculated by dividing the user's total sleep time by the TRT, and the TRT value can be used to calculate various sleep parameters of the user.

[0005] However, conventional polysomnography tests have limitations in that they require an expert who is specialized in operating the testing equipment, and they also have to rely on the expert to determine the start and end points of the user's sleep for the analysis period in which the user's sleep state is to be analyzed. In other words, conventional technology has the disadvantage that it is difficult to determine the user's accurate TRT value in an ordinary home without an expert, making it impossible to analyze the sleep state, and the user must visit a specialized institution (such as a hospital or clinic) where an expert is present for the sleep state analysis.

[0006] The present invention has been proposed to overcome the drawbacks of the conventional technology, and provides a sleep analysis period detection method and system that can automatically detect a user's sleep analysis period and analyze a user's sleep state even when the user simply sleeps in sleep equipment capable of collecting the user's physical information, or in a general home environment without the need for an expert. Summary of the Invention [Problem to be solved by the invention]

[0007] The present invention aims to provide a sleep analysis period detection method and system that can collect a user's biosignals in a non-contact manner and detect an effective analysis period required for sleep analysis of the user using the collected biosignals.

[0008] Another object of the present invention is to provide a sleep analysis period detection method and system that can automatically detect a sleep analysis period required for analyzing a user's sleep state even in a general home environment without the help of an expert.

[0009] Another object of the present invention is to provide a sleep analysis period detection method and system that can automatically distinguish between sleep and non-sleep activities of a user and detect effective sleep analysis periods necessary for sleep state analysis.

[0010] Meanwhile, the technical problems of the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the following description. [Means for solving the problem]

[0011] The sleep analysis period detection method according to the present invention is a sleep analysis period detection method performed in a system including a layer unit, a sensing unit, a memory unit, and a data analysis unit, and includes the steps of sensing a user's biological signal from the sensing unit provided in the layer unit; storing the user's biological signal sensed by the sensing unit in a memory unit; and analyzing the user's biological signal stored in the memory unit from the data analysis unit to detect the user's sleep analysis period.

[0012] In addition, in the sleep analysis period detection method according to the present invention, the step of sensing the user's biological signal is characterized in that at least one of the user's weight, height, body proportions, identification information, movement information, heart rate, and respiratory status is sensed through a sensing unit including at least one sensor selected from the group consisting of a pressure sensor, a vibration sensor, a piezoelectric sensor, an acceleration sensor, an acoustic sensor, a PVDF (Polyvinylidene Film) sensor, an EMFi (ElectroMechanical Film) sensor, an FSR (Force Sensing Resistor) sensor, an infrared sensor, a motion sensor, and a face recognition sensor.

[0013] In addition, in the sleep analysis period detection method according to the present invention, the step of storing the user's biosignal in a memory unit is characterized in that the user's biosignal sensed by the sensing unit is mapped and stored for each user.

[0014] In addition, in the sleep analysis section detection method according to the present invention, the step of detecting the user's sleep analysis section from the data analysis unit may include a first sleep analysis section detection step of detecting a time when the user lies down on the layer unit and a time when the user stands up and leaves the layer unit.

[0015] In addition, in the sleep analysis period detection method according to the present invention, the first sleep analysis period detection step is performed using a user's biological signal sensed from at least one sensor of the sensing unit, including a pressure sensor, a vibration sensor, a piezoelectric sensor, an acceleration sensor, a PVDF (Polyvinylidene Film) sensor, an EMFi (ElectroMechanical Film) sensor, and an FSR (Force Sensing Resistor) sensor.

[0016] In addition, the sleep analysis period detecting method according to the present invention may further include, after the first sleep analysis period detecting step, a second sleep analysis period detecting step of excluding a non-sleep period of the user from the first sleep analysis period.

[0017] In addition, in the sleep analysis section detection method according to the present invention, the second sleep analysis section detection step is performed by determining a section in which at least one of the biosignals of the user's heart rate, respiratory status, and movement information sensed by the sensing unit exceeds a predetermined threshold value as a non-sleep section and excluding the section from the first sleep analysis section.

[0018] In addition, in the sleep analysis period detection method according to the present invention, the threshold value refers to a vital sign value at which the user is determined to be in a non-sleep state even though the user is lying on the floor, and is set for each user.

[0019] In addition, the sleep analysis period detecting method according to the present invention may further include, after the second sleep analysis period detecting step, detecting a third sleep analysis period, excluding a user activity period from the second sleep analysis period.

[0020] In addition, in the sleep analysis section detection method according to the present invention, the third sleep analysis section detection step is performed by determining a section in which a signal value sensed from the pressure sensor of the sensing unit remains lower than a predetermined critical value for a predetermined time or longer as a user activity section, and excluding the section from the second sleep analysis section.

[0021] In the sleep analysis period detection method according to the present invention, the threshold value refers to a vital sign value at which it is determined that the user has stood up and left the layer unit, and is set for each user.

[0022] In addition, the sleep analysis period detection method according to the present invention may further include, after the third sleep analysis period detection step, if there are at least two or more independent sleep analysis periods within the third sleep analysis period, adding up the multiple sleep analysis periods.

[0023] In addition, in the sleep analysis section detection method according to the present invention, the step of sensing the user's biological signal from the sensing unit is performed from a pre-set analysis start point to a pre-set analysis end point, or from a point a predetermined time before the pre-set analysis start point to a point a predetermined time after the pre-set analysis end point.

[0024] In addition, in the sleep analysis section detection method according to the present invention, the time point a predetermined time before the previously set analysis start time point and the time point a predetermined time after the previously set analysis end time point are automatically adjusted based on accumulated user bio-signals or user bio-signals currently being sensed.

[0025] The sleep analysis period detecting method according to the present invention may further include storing the detected user's sleep analysis period in the memory unit after detecting the user's sleep analysis period.

[0026] In addition, in the sleep analysis period detection method according to the present invention, the step of storing the detected sleep analysis period of the user in the memory unit is characterized in that the sleep analysis period is mapped and stored for each user.

[0027] A sleep analysis period detection system according to an embodiment of the present invention includes at least one layer unit having a plurality of components; a sensing unit provided within the layer unit and configured to sense a user's biological signal; a memory unit in which the user's biological signal sensed by the sensing unit is stored; and a data analysis unit that analyzes the user's biological signal stored in the memory unit and detects the user's sleep analysis period.

[0028] In addition, in the sleep analysis period detection system according to the present invention, the sensing unit includes at least one sensor selected from the group consisting of a pressure sensor, a vibration sensor, a piezoelectric sensor, an acceleration sensor, an acoustic sensor, a PVDF (Polyvinylidene Film) sensor, an EMFi (ElectroMechanical Film) sensor, an FSR (Force Sensing Resistor) sensor, an infrared sensor, a motion sensor, and a face recognition sensor.

[0029] In addition, in the sleep analysis period detection system according to the present invention, the memory unit is characterized in that at least one of the user's weight, height, body proportion, identification information, movement information, heart rate, respiratory status, set sleep time, sleep analysis period, and threshold value is mapped and stored for each user.

[0030] In addition, in the sleep analysis period detection system according to the present invention, the user's sleep analysis period detected by the data analysis unit is characterized in that a second sleep analysis period is detected by excluding the user's non-sleep period from a first sleep analysis period detected by sensing a time when the user lies down on the layer unit and a time when the user stands up and leaves the layer unit, and a third sleep analysis period is detected by excluding the user's activity period from the second sleep analysis period.

[0031] In addition, in the sleep analysis period detection system according to the present invention, the user's sleep analysis period detected by the data analysis unit is characterized in that it is a sleep analysis period obtained by combining at least two or more independent sleep analysis periods that exist within the third sleep analysis period. [Effects of the Invention]

[0032] According to the present invention, a biosignal of a user can be collected in a contactless manner, and an effective analysis period required for sleep analysis of the user can be automatically detected using the collected biosignal.

[0033] In addition, according to the present invention, the sleep analysis interval necessary for analyzing a user's sleep state can be automatically detected even in a general home environment, thereby allowing the user to easily detect the sleep analysis interval without the help of an expert or specialized institution.

[0034] In addition, according to the present invention, the user's sleep activity and non-sleep activity can be automatically classified into those without manually monitoring the user's sleep time, and the effective sleep analysis period required for sleep state analysis can be detected more accurately.

[0035] Meanwhile, the effects of the present invention are not limited to those mentioned above, and other technical effects not mentioned will be clearly understood by those skilled in the art from the following description. [Brief explanation of the drawings]

[0036] [Figure 1]1 is a diagram illustrating a sleep analysis period detection system according to an embodiment of the present invention; [Figure 2] 1 is a diagram illustrating a method for detecting a sleep analysis period according to an embodiment of the present invention; [Figure 3] 4A and 4B are diagrams illustrating a sleep analysis section detection process according to an embodiment of the present invention in more detail; [Figure 4] 1 is a diagram illustrating a first sleep analysis section detection step according to an embodiment of the present invention; [Figure 5] 10 is a diagram illustrating a second sleep analysis section detection step according to an embodiment of the present invention. [Figure 6] 10 is a diagram illustrating a third sleep analysis section detection step according to an embodiment of the present invention. [Figure 7] 10A and 10B are diagrams illustrating a method for detecting a sleep analysis period according to another embodiment of the present invention; [Figure 8] 10 is a diagram illustrating a memory unit of a sleep analysis period detection system according to another embodiment of the present invention; FIG. [Figure 9] 10A and 10B are diagrams illustrating a method for detecting a sleep analysis period according to another embodiment of the present invention; DETAILED DESCRIPTION OF THE INVENTION

[0037] The purpose, technical configuration, and effects of the present invention will be more clearly understood from the following detailed description based on the accompanying drawings. With reference to the accompanying drawings, the present invention will be described in detail.

[0038] The embodiments disclosed herein should not be construed or used to limit the scope of the present invention. It is obvious to those skilled in the art that the description including the embodiments of the present specification has various applications. Therefore, any embodiments described in the detailed description of the present invention are merely examples for better explaining the present invention, and the scope of the present invention is not limited to the embodiments.

[0039] The functional blocks shown in the drawings and described below are merely examples of possible implementations. In other implementations, other functional blocks may be used without departing from the spirit and scope of the detailed description. Also, although one or more functional blocks of the present invention are shown as individual blocks, one or more of the functional blocks of the present invention may be a combination of various hardware and software configurations that perform the same function.

[0040] Furthermore, a phrase "including a certain component" simply refers to the presence of the component as an "open" phrase and is not to be understood as excluding additional components. Furthermore, when a component is referred to as being "coupled" or "connected" to another component, it may be directly coupled or connected to the other component, but other components may be present in between.

[0041] FIG. 1 is a diagram illustrating a sleep analysis section detection system according to an embodiment of the present invention.

[0042] 1, a sleep analysis period detection system 100 according to the present invention includes at least one layer unit 110 having a plurality of components, a sensor unit 120 provided within the layer unit 110 and configured to sense a user's biosignal, a memory unit 130 storing the user's biosignal sensed by the sensor unit 120, and a data analysis unit 140 analyzing the user's biosignal stored in the memory unit 130 to detect the user's sleep analysis period. The sleep analysis period detection system 100 according to the present invention may further include a communication unit 150 connected to the system 100 via wired / wireless means and transmitting and receiving information such as data to an external device or server, a power supply unit (not shown) controlling the on / off and power of the system 100, and a control unit (not shown) controlling the configuration of the system 100.

[0043] The layer unit 110 can be understood as a member having a storage space in which the components described below can be placed. Given that the layer unit 110 has a storage space, there is no limitation on the material or shape of the layer unit 110. The layer unit 110 may be a space in which a user sleeps, such as a mattress, or any one of multiple surfaces constituting a mattress. The layer unit 110 may also be a mat placed on a mattress, or may be a member made of a fiber material other than cotton, such as wood or metal. Thus, given that the layer unit 110 has a certain storage space, there is no limitation on the material or shape of the layer unit 110. However, to facilitate understanding of the invention, the following detailed description will continue assuming that the layer unit 110 is a mattress.

[0044] The sensing unit 120 is provided on the layer unit 110 and configured to sense and acquire a biosignal from a user. The sensing unit 120 may include one of a plurality of types of sensors capable of sensing a user's biosignal, for example, a pressure sensor, a vibration sensor, a piezoelectric sensor, an acceleration sensor, an acoustic sensor, a PVDF (Polyvinylidene Film) sensor, an EMFi (ElectroMechanical Film) sensor, an FSR (Force Sensing Resistor) sensor, an infrared sensor, a motion sensor, and a face recognition sensor, or may be configured by a combination of at least one of the sensors.

[0045] Specifically, the sensing unit 120 can sense the user's movements of lying down or standing up on the layer unit 110 or the user's weight or position information through a pressure sensor, and can obtain status information such as heart rate, breathing status, and movement status by sensing the user's vibration signal through a vibration sensor. In addition, the sensing unit 120 can recognize the user's voice through an acoustic sensor, recognize the user's face through a facial recognition sensor, and receive feedback information by sensing the user's gestures through a motion sensor.

[0046] As such, the sensing unit 120 of the present invention is configured to sense various biosignals of the user located on the layer unit 110, and adjusting the type, number, layout, etc. of sensors used to sense more precise and specific user status information is not limited to the embodiments of the present invention.

[0047] The memory unit 130 is configured to store a user's biosignal sensed by the sensing unit 120, and may be an external device that is embedded in the sleep analysis period detection system 100 or connected to the sleep analysis period detection system 100 via a communication method such as wired or wireless communication. The type and location of the memory unit 130 are not limited to those described in the embodiments of the present invention.

[0048] The sensed user biosignals may be mapped and stored for each user in the memory unit 130. For example, in addition to physical information such as weight, height, and body proportions of user A, information such as face information and voice information may be mapped and stored with identification information for identifying the user in the memory unit 130. Furthermore, more specific information such as user A's heart rate information, movement information, respiratory status, sleep analysis period, and threshold values ​​required for analyzing the sleep analysis period may be mapped and stored in the memory unit 130. In addition, the memory unit 130 may map and store a preset sleep time that is pre-set for each user, or the preset sleep time may be automatically or manually adjusted and stored based on the user information stored in the memory unit 130.

[0049] The data analysis unit 140 is configured to detect a user's sleep analysis period based on the user's biological signals stored in the memory unit 130. The data analysis unit 140 may also be understood as a central processing unit (CPU). The CPU may also be called a controller, microcontroller, microprocessor, or microcomputer. The CPU may be implemented using hardware, firmware, software, or a combination thereof. When implemented using hardware, the CPU may be an application specific integrated circuit (ASIC), digital signal processor (DSP), digital signal processing device (DSPD), programmable logic device (PLD), field programmable gate array (FPGA), or the like. When implemented using firmware or software, the firmware or software may be configured to include modules, procedures, or functions that perform the above-described functions or operations.

[0050] Here, the user's sleep analysis period detected by the data analysis unit 140 according to an embodiment of the present invention may include a first sleep analysis period detected by detecting when the user lies down on the layer unit 110 and when the user stands up and leaves the layer unit 110, a second sleep analysis period detected by excluding the user's non-sleep period from the first sleep analysis period, and a third sleep analysis period detected by excluding the user's activity period from the second sleep analysis period, and preferably, the last third sleep analysis period may be the sleep analysis period finally detected by the data analysis unit 140. The user's sleep analysis period detected by the data analysis unit 140 may also be a sleep analysis period obtained by combining at least two or more independent sleep analysis periods present within the third sleep analysis period.

[0051] Hereinafter, a method for detecting a user's sleep analysis period according to an embodiment of the present invention will be described with reference to FIGS.

[0052] FIG. 2 is a diagram illustrating a sleep analysis period detection method according to an embodiment of the present invention. Referring to FIG. 2, the sleep analysis period detection method is performed in a system including a layer unit, a sensing unit, a memory unit, and a data analysis unit. The sleep analysis period detection method includes step S100 of sensing a user's biosignal from the sensing unit provided in the layer unit, step S200 of storing the user's biosignal sensed by the sensing unit in a memory unit, and step S300 of analyzing the user's biosignal stored in the memory unit from the data analysis unit to detect the user's sleep analysis period.

[0053] The step S100 of sensing the user's bio-signal is performed to sense at least one of the user's weight, height, body proportions, identification information, movement information, heart rate, and respiratory status through a sensing unit including at least one sensor selected from the group consisting of a pressure sensor, a vibration sensor, a piezoelectric sensor, an acceleration sensor, an acoustic sensor, a PVDF (Polyvinylidene Film) sensor, an EMFi (ElectroMechanical Film) sensor, an FSR (Force Sensing Resistor) sensor, an infrared sensor, a motion sensor, and a face recognition sensor. The step S200 of storing the user's bio-signal in a memory unit is performed to map and store the user's bio-signal sensed by the sensing unit for each user.

[0054] That is, according to an embodiment of the present invention, the user's biosignals measured by the sensors of the sensing unit are mapped for each user and stored in the memory unit. Therefore, when the user's biosignal is sensed by the sensing unit, not only the user can be identified but also the user's state information (e.g., whether the user is asleep, not asleep, or active) can be determined based on the biosignal. As a result, the present invention can automatically detect the user's effective sleep analysis period.

[0055] FIG. 3 is a diagram illustrating a sleep analysis section detection process according to an embodiment of the present invention in more detail. Referring to FIG. 3, step S300 of detecting a user's sleep analysis section from the data analysis unit may include at least one of a first sleep analysis section detection step S310, a second sleep analysis section detection step S320, and a third sleep analysis section detection step S330.

[0056] The first sleep analysis period detection step S310 is performed by detecting the time when the user lies down on the layer unit and the time when the user stands up and leaves the layer unit, and detecting the first sleep analysis period with these as the start and end times, respectively. The time when the user lies down on the layer unit and the time when the user stands up and leaves the layer unit can be detected using user biological signals detected from at least one sensor of the detection unit, including a pressure sensor, a vibration sensor, a piezoelectric sensor, an acceleration sensor, a PVDF (Polyvinylidene Film) sensor, an EMFi (ElectroMechanical Film) sensor, and an FSR (Force Sensing Resistor) sensor.

[0057] That is, the present invention does not detect a user's sleep analysis period based solely on the start and end points previously set by the user or automatically / manually set by the system, but detects the user's primary sleep analysis period based on the user's biological signals sensed through the sensing unit, using the times when the actual user lies down or stands up in the layer unit as the start and end points of the sleep analysis period. This allows for more accurate detection of the sleep analysis period without any additional user operation.

[0058] Next, after the first sleep analysis section detection step S310, a second sleep analysis section detection step S320 is performed. The second sleep analysis section detection step S320 may be performed by excluding the user's non-sleep section from the first sleep analysis section. Here, the user's non-sleep section refers to a section in which the user engages in activities unrelated to sleep analysis even after lying down on the bed, such as looking at a smartphone, reading, or talking on the phone because they cannot fall asleep while lying down on the bed. The non-sleep section may be determined based on user biosignal information, such as the user's heart rate, breathing state, movement information, and voice information, sensed by the sensing unit. The present invention is characterized in that such non-sleep sections are automatically excluded from the first sleep analysis section, thereby enabling more accurate detection of the second sleep analysis section.

[0059] Next, after the second sleep analysis period detecting step S320, a third sleep analysis period detecting step S330 may be performed to exclude a user's activity period from the second sleep analysis period. Here, the user's activity period refers to a period during which the user gets up before or during a sleep state to go to the toilet or to temporarily deviate from sleep activities, and the activity period may be determined based on a pressure signal or a vibration signal sensed by a pressure sensor of the sensing unit. The present invention is characterized in that such a user's activity period is automatically excluded from the second sleep analysis period, thereby enabling more accurate detection of the third sleep analysis period.

[0060] Hereinafter, the first to third sleep analysis period detecting steps according to an embodiment of the present invention will be described in more detail with reference to FIGS.

[0061] FIG. 4 is a diagram illustrating a first sleep analysis section detection step according to an embodiment of the present invention.

[0062] Referring to FIG. 4, the first sleep analysis section detection step is performed using user bio-signals sensed from at least one of the pressure sensor, vibration sensor, piezoelectric sensor, acceleration sensor, PVDF (Polyvinylidene Film) sensor, EMFi (ElectroMechanical Film) sensor, and FSR (Force Sensing Resistor) sensor of the sensing unit, and the start point is the time when the user lies down on the layer unit and the end point is the time when the user stands up and leaves the layer unit.

[0063] As shown in FIG. 4, the sleep analysis period detection system of the present invention determines the time when a signal value of a pressure sensor or piezoelectric sensor, for example, an FSR (Force Sensing Resistor) sensor or a PVDF sensor, sensed by the sensing unit of the layer unit exceeds a critical value, for example, “0,” and is sensed as a value corresponding to the user’s weight, as the time when the user lies down on the layer unit, and defines this as the “start time.” When the signal value again becomes a critical value, for example, “0,” or falls below “0,” and is sensed as a value corresponding to the user’s absence, it determines the time when the user stands up and leaves the layer unit, and defines this as the “end time.” The period from the “start time” to the “end time” is detected as a primary sleep analysis period.

[0064] That is, according to the present invention, the start and end points of the primary sleep analysis section can be automatically set by determining whether the user has lay down in the layer section or stood up from the layer section based on the user's biological signals sensed by the sensor, without the user having to directly set the start and end of the sleep analysis detection section or an expert having to operate the equipment.

[0065] FIG. 5 is a diagram illustrating a second sleep analysis section detection step according to an embodiment of the present invention.

[0066] The second sleep analysis period detection step may be performed by excluding the user's "non-sleep period" from the first sleep analysis period in Fig. 4. Here, the user's "non-sleep period" refers to a period in which the user engages in activities unrelated to sleep analysis even after lying down on the layer unit, for example, a non-sleep period such as looking at a smartphone, reading, or talking on the phone without falling asleep even after lying down on the bed, and the "non-sleep period" may be determined based on user biosignal information such as the user's heart rate, breathing state, movement information, and voice information sensed by the sensing unit.

[0067] Specifically, a section in which at least one of the biosignals of the user's heart rate, breathing status, and movement information sensed by the sensing unit exceeds a pre-set critical value can be determined as a "non-sleep section." The critical value refers to the biosignal value at which the user is judged to be in a "non-sleep state" even when lying on the layer, and can be set for each user.

[0068] That is, as shown in FIG. 5, during the user's primary sleep analysis period, based on an indicator of where the period of the biosignal value or the user's movement occurrence period (which can be inferred from the biosignal value) begins to lengthen, if the user's breathing is fast or irregular, or if the heart rate is fast or irregular, or if the user's movement occurs frequently or periodically, it can be determined to be a "non-sleep period." This can also be determined to be a "non-sleep period" where the user is engaged in other activities before entering a sleep state if it is detected to be above a predetermined critical value set for each user based on the user's biosignal value stored in the memory unit.

[0069] For example, a sleep state threshold value for user A can be set in a range of 5,000 to 5,500 based on the biosignal value of user A stored in the memory unit, and a period in which the user's movement index (or an index value calculated from at least two or more indexes) sensed by the sensing unit becomes longer than the critical value is determined to be the start of the user's "non-sleep period," and a point in which the user's movement index value falls within the range of the critical value and it is determined that no user movement occurs for a while is determined to be the end of the "non-sleep period," and this "non-sleep period" can be excluded from the user's primary sleep analysis period and detected as a secondary sleep analysis period.

[0070] The critical value used to define such a "non-sleep section" refers to a biosignal value at which a user is determined to be in a non-sleep state regardless of whether the user is lying down on the floor or not, and may be set differently for each user. In addition, such a critical value may be set based on biosignal values ​​of the user's past sleep or non-sleep states, and may be automatically adjusted by accumulating and storing the user's biosignals and periodically / non-periodically updating them, or may be manually readjusted by the user.

[0071] Therefore, the present invention is characterized in that it does not simply detect the entire time that the user is lying in bed as a sleep analysis section, but automatically excludes from the sleep analysis section a "non-sleep section" that is determined to be a non-sleep state even though the user is lying in bed, such as looking at a smartphone or reading a book, thereby detecting a secondary sleep analysis section that can be more effectively used to analyze the user's actual sleep state.

[0072] FIG. 6 is a diagram illustrating a third sleep analysis section detection step according to an embodiment of the present invention.

[0073] 6, the third sleep analysis period detection step may be performed by excluding the user's "active period" from the second sleep analysis period of FIG 5. Although not shown, the third sleep analysis period detection step may also be performed by excluding the user's "active period" from the first sleep analysis period of FIG 4.

[0074] Here, the user's "activity period" refers to a period during which the user is away from sleep activities for a while, such as when the user stands up before or during sleep to go to the toilet or watch TV, and whether or not to exclude the corresponding "activity period" from the sleep analysis period can be determined based on a pressure signal or a vibration signal sensed by a pressure sensor of the sensing unit.

[0075] Specifically, the portion where the signal value sensed by the pressure sensor of the sensing unit falls below a predetermined critical value is determined to be a state where the user has left the layer unit, and the section where the state where the signal value falls below a predetermined critical value is maintained for a predetermined time or longer can be determined to be a user "activity section."

[0076] When the signal value sensed from the pressure sensor of the sensing unit is below a critical value, for example, "0," or falls to a value less than "0," it is determined that the user has stood up from the layer unit. If this state is maintained within a predetermined critical time (for example, 30 to 60 seconds), it can be defined as an activity that is not significantly disturbing a sleep state, such as the user going to the toilet for a while, and can be included in the sleep analysis section. If this state is maintained for a predetermined critical time (for example, 60 seconds or more), it can be defined as an "activity section" that is unrelated to a sleep state, such as the user waking up from a sleep state and going to the toilet for a long time or watching TV in the living room, and can be excluded from the sleep analysis section.

[0077] The critical value used to define such an "active period" refers to a biosignal value at which it is determined that the user has woken up from a sleep state when standing up from the sleep section, and can be set differently for each user. In addition, such a critical value can be set based on the biosignal values ​​of the user's past sleep state or non-sleep state, and can be automatically adjusted by accumulating and storing the user's biosignals and periodically / non-periodically updating them, or can be manually readjusted by the user.

[0078] Therefore, the present invention is characterized by being able to detect a third sleep analysis section that can be more effectively used for analyzing the actual sleep state of a user by automatically excluding from the sleep analysis section an "activity section" in which the user gets out of bed during sleep and is disturbed by the sleep state, or an "activity section" that is unrelated to the sleep state.

[0079] Also, although not shown, if there are at least two or more independent sleep analysis periods within the detected third sleep analysis period after the third sleep analysis period detection step, it is also possible to detect the final sleep analysis period by adding up the multiple sleep analysis periods.

[0080] That is, in the process of detecting a user's sleep analysis interval, each independent sleep interval may be divided and included before or after the interval defined as an interval that is included or excluded in the sleep analysis interval. However, if there are multiple independent sleep intervals within the entire sleep analysis interval, the final sleep analysis interval may be detected by adding up each independent sleep interval. Alternatively, the sleep analysis interval detection process may be performed again by calling up all the pressure sensor and vibration sensor data from the start of the first independent sleep to the end of the last independent sleep.

[0081] In this way, it is possible to analyze the sleep state by looking at the data of the entire sleep analysis section, and to analyze the sleep state in a way that reflects the continuity of sleep. For example, in the entire sleep analysis section, the proportion of deep sleep may be concentrated mainly from the start point of the sleep analysis section to the first third point. In this way, when analyzing by looking at the data of the entire sleep analysis section, it is possible to add up each independent sleep and detect the final sleep analysis section, thereby improving the accuracy of the data.

[0082] As described above, the present invention is characterized in that, in detecting the sleep analysis intervals required for analyzing a user's sleep state, 1) the time when the user lies down in bed or stands up is automatically detected without any separate operation by the user, 2) the non-sleep intervals in which the user is engaged in activities unrelated to sleep even when lying down in bed are automatically excluded, and 3) the intervals in which the user is out of bed for a predetermined time and engages in activities that disturb the sleep state are automatically excluded, and the sleep analysis interval detection process can be performed automatically by detecting the user's biological signals without any manual operation by the user.

[0083] Therefore, even if a user simply lies down and sleeps in a bed on which the sleep analysis section detection method according to the present invention is performed, the sleep analysis section can be automatically detected by automatically detecting the start and end points of the user's sleep. In addition, the non-sleep section on the bed before sleep and the activity section outside the bed during sleep can be automatically excluded from the sleep analysis section. This has the advantage that an effective sleep analysis section required for analyzing a user's sleep state can be automatically detected even in a general home environment where no expert or specialized equipment is present.

[0084] In particular, the present invention can identify a user simply by lying down on a bed, and can automatically determine the user's non-sleep and activity periods by utilizing a threshold value set based on the user's biological signal values ​​in the sleep and non-sleep states, thereby achieving the effect of automatically detecting the user's sleep analysis period more accurately and easily without requiring any additional user operation or setting.

[0085] To achieve this, a process is required in which the user's biological signals and the detected sleep analysis period are updated and cumulatively stored in a memory unit. Hereinafter, a method for detecting a sleep analysis period according to another embodiment of the present invention will be described with reference to FIG. 7.

[0086] As shown in FIG. 7, a sleep analysis period detection method according to another embodiment of the present invention includes step S100 of detecting a user's biometric signal from a sensor provided in a layer unit, step S200 of storing the user's biometric signal detected by the sensor in a memory unit, step S300 of analyzing the user's biometric signal stored in the memory unit from the data analysis unit to detect the user's sleep analysis period, and step S400 of storing the detected user's sleep analysis period in the memory unit.

[0087] That is, according to the present invention, not only are user biosignals sensed by a sensing unit mapped for each user and stored in a memory unit, but biosignal threshold values ​​for each user state (sleep state, non-sleep state, active state, etc.) defined based on the user's biosignal and the detected user's sleep analysis period are also mapped for the corresponding user and stored in the memory unit, so that the information stored in the memory unit can be used to determine the corresponding user's sleep / non-sleep state and active state or to detect the corresponding user's effective sleep analysis period. Therefore, the present invention has the advantage that the user's sleep analysis period can be detected based on the user information cumulatively stored in the memory unit, thereby enabling more accurate sleep analysis period detection that is suited to the user's condition.

[0088] FIG. 8 is a diagram illustrating a memory unit of a sleep analysis period detection system according to another embodiment of the present invention. As shown in FIG. 8, the memory unit 130 of the sleep analysis period detection system according to the present invention can store at least one or more pieces of user information.

[0089] The memory unit 130 may store information of multiple users mapped to each user, for example, information mapped to user A may include identification information for identifying user A, biometric signals of user A, a set time of user A, a sleep analysis period of user A, and a critical value of user A. Such information values ​​may also be mapped and stored for other users B, C, and multiple users. Here, the critical value of user A refers to a critical value used as an index for determining a sleeping state, non-sleeping state, and activity state of a corresponding user based on the user's biometric signal value, and the set time of user A refers to a set time that the user can specify as the start and end of a sleep analysis period.

[0090] Therefore, according to the present invention, the user's sleep state, non-sleep state, active state, etc. can be determined more accurately based on the information values ​​mapped and stored for each user in the memory unit 130, and thereby the substantially effective sleep analysis period required for sleep analysis can be automatically and more accurately detected within the user's sleep period.

[0091] Furthermore, as another embodiment of the present invention, even if there is a sleep analysis set time specified by the user or automatically set by the system, the set time can be adjusted by sensing the user's biological signals.

[0092] FIG. 9 is a diagram illustrating a method for detecting a sleep analysis period according to another embodiment of the present invention. Hereinafter, a set time adjusting process according to another embodiment of the present invention will be described in more detail with reference to FIG.

[0093] The "set time" refers to a period during which data is measured when detecting a user's sleep analysis period, and may be a period that the user directly sets in advance to be used for detecting the user's sleep analysis period, or may be a period that is automatically set by the system based on the user's information stored in the memory unit. That is, although the "set time" is basically used when detecting a user's sleep analysis period, in another embodiment of the present invention, even if a specified "set time" exists, the "set time" may be reasonably adjusted based on the user's biological signals sensed by the sensor unit.

[0094] In other words, if there is a section determined to be an activity section before, after, or in the middle of the "set time," it can be excluded when detecting the user's sleep analysis section even if the corresponding activity section is included in the "set time," and if there is a section determined to be a sleep section before, after, or in the middle of the "set time," it can be included in the user's sleep analysis section and detected even if it is not included in the "set time." That is, even if the "set time" for a given user has already been specified, the process of detecting the user's sleep analysis section can be performed from several minutes to several tens of minutes before the start of the first independent sleep or from several minutes to several tens of minutes after the end of the last independent sleep.

[0095] For example, assuming that a user's "set time" is set from 9:00 PM to 7:00 AM the next day, the user's biological signals can be sensed before or after the "set time" to determine the user's sleep state, non-sleep state, and activity state. Therefore, a sleep period may exist from 8:30 PM to 1:30 AM the next day, and a sleep period may also exist from 2:30 AM to 7:30 AM. In this case, according to a sleep analysis period detection method according to another embodiment of the present invention, sleep may be analyzed and transmitted primarily at 1:30+m minutes, and sleep may be analyzed and transmitted secondarily at 7:30+m minutes. Meanwhile, according to another embodiment of the present invention, the data values ​​transmitted primarily and secondarily may be added to or overwritten on the user's biological signal values ​​sensed during the previously specified "set time" and used to detect the user's sleep analysis period.

[0096] In this case, the periods 20:30 to 21:00 and 07:30 to 08:00 can be included in the sleep analysis period of the "set time" (21:00 to 07:00 the next day) and analyzed, even though they are periods before the start point (21:00) and after the end point (07:00) of the specified "set time." In effect, the entire period in which the user's sleep can be analyzed is 20:30 to 07:30 the next day.

[0097] Therefore, according to another embodiment of the present invention, even if a "set time" for detecting a sleep analysis section is specified by a user or a system, sensing of the user's biosignal may be performed from a start point of the previously set "set time" to a previously set end point of the analysis, or from a point a predetermined time before the start point of the previously set "set time" to a point a predetermined time after the end point of the previously set "set time." Furthermore, according to another embodiment of the present invention, the "set time" may be automatically adjusted to a point a predetermined time before the start point of the previously set "set time" and a point a predetermined time after the end point of the previously set "set time" based on the accumulated user's biosignals or the user's biosignals being sensed.

[0098] Meanwhile, although not shown, biosignals can be continuously sensed while the user is lying on the layer unit, and if a sleep section other than the specified "set time" exists, it can be stored in the memory unit as a separate, independent sleep. For example, if the user takes a nap and an independent sleep section determined to be asleep is detected between 4:00 PM and 5:00 PM, this can be stored as a separate sleep section not included in the specified "set time" or as a sleep section added to the sleep analysis section detected during the "set time," and can be used as a biosignal indicator required to detect the user's sleep analysis section.

[0099] While the sleep analysis period detection method and system according to the present invention have been described above, the present invention is not limited to the specific embodiments and applications described above, and various modifications may be made by a person skilled in the art without departing from the spirit and scope of the present invention, and such modifications are not distinguishable from the technical idea or scope of the present invention.

[0100] In particular, the configurations implementing the technical features of the present invention contained in the block diagrams and flowcharts shown in the accompanying drawings of this specification imply logical boundaries between the configurations. However, in software or hardware embodiments, the configurations and functions shown may be implemented in the form of stand-alone software modules, monolithic software structures, code, services, or a combination thereof, and the functions may be embodied by being stored on a computer-executable medium having a processor capable of executing stored program code, instructions, etc., and therefore all such embodiments are within the scope of the present invention.

[0101] Therefore, although the accompanying drawings and the associated techniques illustrate the technical features of the present invention, the specific arrangement of software for realizing such technical features should not be inferred unless it is clearly stated. In other words, various embodiments as described above may exist, and since such embodiments may be partially modified while having the same technical features as the present invention, they also fall within the scope of the present invention.

[0102] Also, while flowcharts depict operations in the figures in a particular order, this is done to obtain the most suitable results and should not be understood as necessarily performing such operations in the particular order shown or in a sequential order, or as necessarily performing all of the operations shown. In certain cases, multitasking and parallel processing may be advantageous. Additionally, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments; the program components and systems described may generally be integrated together in a single software product or packaged in multiple software products.

Claims

1. A sleep analysis section detection method performed in a system including a layer unit, a sensing unit, a memory unit, and a data analysis unit, sensing a biosignal of a user from a sensing unit provided in the layer unit; storing the user's biological signal sensed by the sensing unit in a memory unit; and a step of analyzing the user's biological signals stored in the memory unit by the data analysis unit and detecting a sleep analysis period of the user; Contains A sleep analysis section detection method comprising:

2. The step of sensing the user's biological signal includes: At least one of the user's weight, height, body proportions, identification information, movement information, heart rate, and breathing status is sensed through a sensing unit including at least one sensor selected from the group consisting of a pressure sensor, a vibration sensor, a piezoelectric sensor, an acceleration sensor, an acoustic sensor, a PVDF (Polyvinylidene Film) sensor, an EMFi (ElectroMechanical Film) sensor, an FSR (Force Sensing Resistor) sensor, an infrared sensor, a motion sensor, and a face recognition sensor. The sleep analysis section detection method according to claim 1 .

3. The step of detecting a sleep analysis period of the user from the data analysis unit includes: A first sleep analysis section detection step is included, which detects when the user lies down on the layer section and when the user stands up and leaves the layer section. The sleep analysis section detection method according to claim 1 .

4. After the first sleep analysis section detection step, The method further includes detecting a second sleep analysis period, excluding a non-sleep period of the user from the first sleep analysis period. The sleep analysis section detection method according to claim 3 .

5. The second sleep analysis section detection step includes: A section in which at least one of the biosignals of the user's heart rate, breathing state, and movement information sensed by the sensing unit exceeds a predetermined threshold value is determined as a non-sleep section, and is excluded from the primary sleep analysis section. The sleep analysis section detection method according to claim 4 .

6. After the second sleep analysis section detection step, The method further includes detecting a third sleep analysis period, excluding a user activity period from the second sleep analysis period. The sleep analysis section detection method according to claim 4 .

7. The third sleep analysis section detection step includes: A section in which the signal value detected by the pressure sensor of the sensing unit remains lower than a predetermined threshold value for a predetermined time or longer is determined as a user activity section, and is excluded from the secondary sleep analysis section. The sleep analysis section detection method according to claim 6 .

8. After the third sleep analysis section detection step, If the third sleep analysis period includes at least two or more independent sleep analysis periods, the third sleep analysis period may further include adding up the plurality of sleep analysis periods. The sleep analysis section detection method according to claim 1 .

9. At least one layer portion having a plurality of structures; a sensing unit provided in the layer unit for sensing a user's biological signal; a memory unit in which a user's biological signal sensed by the sensing unit is stored; and a data analysis unit that analyzes the user's biological signals stored in the memory unit and detects a sleep analysis period of the user; Contains A sleep analysis interval detection system comprising:

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