Apparatus for predicting disease based on sleep analysis installed in senior house

Abstract

The present invention relates to a sleep analysis-based disease prediction device installed in a home for the elderly. A sleep analysis-based disease prediction device according to one embodiment of the present invention may be installed in a home for the elderly and may include: a biosignal sensor installed in a bedroom to remotely measure the heart rate or respiration of a sleeper and generate biosignal data; and a sleep analysis unit that analyzes the sleeper's sleep pattern based on the biosignal data and generates an alarm message when a suspected disease pattern is determined from the sleep pattern.

Description

Technology Field

[0001] The present invention relates to a sleep analysis-based disease prediction device installed in an elderly person's home that predicts diseases that may occur in the elderly based on sleep analysis, enabling early diagnosis. Background Technology

[0003] The pace of domestic aging is progressing very rapidly, and it is expected to enter a super-aged society in 2025. Currently, the population of people aged 65 or older is expected to reach 10 million in 2025, five years from now, and over 15 million in 2036. The proportion of the elderly population is projected to exceed 20% in 2025 from 16.1% in 2020, and surpass 30% in 2035.

[0004] In addition, the number of single-person households is increasing, and according to the Ministry of the Interior and Safety, the number of elderly people living alone increased by 30.9% from 1,275,316 in 2016 to 1,670,416 in 2021. In particular, among the deaths from loneliness that occurred from 2016 to June 2020, nearly half (43%) were elderly people aged 65 or older, and as of June 2020, 388 people (42%) were elderly people aged 65 or older; the reality is that deaths from loneliness among the elderly aged 65 or older account for more than 40% every year.

[0005] To address these issues, elderly care services are being implemented to ensure a stable life in old age, maintain the functions and health of the elderly, and prevent deterioration by providing appropriate care to vulnerable seniors who have difficulty carrying out daily activities. However, due to the increase in the number of recipients of care services and the consequent rise in welfare costs, various research and development efforts regarding IoT-based smart care systems are underway. Prior art literature

[0007] Republic of Korea Registered Patent Publication No. 10-1990118 The problem to be solved

[0008] The present invention aims to provide a sleep analysis-based disease prediction device installed in the home of the elderly, which predicts diseases that may occur in the elderly based on sleep analysis, thereby enabling early diagnosis of the disease.

[0009] The present invention aims to provide a sleep analysis-based disease prediction device installed in an elderly person's home that can analyze sleep patterns without disturbing the sleeper by remotely measuring the sleeper's respiration or heart rate.

[0010] The present invention aims to provide a sleep analysis-based disease prediction device installed in an elderly home that can accurately identify suspected disease patterns corresponding to a target disease based on artificial intelligence.

[0011] The present invention aims to provide a sleep analysis-based disease prediction device installed in an elderly person's home that can eliminate noise from biosignal data for sleep analysis by determining when the sleeper is not sleeping. means of solving the problem

[0013] A sleep analysis-based disease prediction device installed in a home for the elderly according to one embodiment of the present invention may include: a biosignal sensor installed in a bedroom that remotely measures the heart rate or respiration of a sleeper to generate biosignal data; and a sleep analysis unit that analyzes the sleeper's sleep pattern based on the biosignal data and generates an alarm message when a suspected disease pattern is determined from the sleep pattern.

[0014] Here, the biosignal sensor may be installed on the ceiling or side wall of the bedroom to irradiate a radar signal to the sleeper and receive the reflected radar signal to measure the sleeper's heart rate or respiratory rate.

[0015] Here, the sleep analysis unit collects the biosignal data during a preset sleep time and analyzes the sleep pattern of the sleeper, wherein the sleep time may be set based on the sleeper's set value, changes in illumination within the bedroom, and the on / off operation of the light within the bedroom.

[0016] Here, the sleep analysis unit may determine a suspected disease pattern for at least one of Alzheimer's disease, Parkinson's disease, stroke, and myocardial infarction from the sleep pattern.

[0017] Here, the sleep analysis unit may include an analysis model generated by learning the sleep patterns of patients having at least one of Alzheimer's disease, Parkinson's disease, stroke, and myocardial infarction as training data.

[0018] Here, the sleep analysis unit may determine that the sleeper has left the room if the biosignal data is measured to be below a threshold value for a set period of time or longer, and may process the biosignal data measured during the sleeper's departure as noise.

[0019] The above-mentioned bedroom further includes a posture detection sensor installed therein to remotely measure the posture of the sleeper and generate posture data, and the sleep analysis unit may determine that the sleeper has left the room if the posture data corresponds to a posture other than a sleeping posture within a preset sleep time, and may process the biosignal data measured during the sleeper's departure as noise.

[0020] Here, the sleep analysis unit may transmit the alarm message to a wall pad installed in the elderly home or to a pre-registered mobile communication terminal.

[0021] In addition, the means for solving the above-mentioned problem do not enumerate all the features of the present invention. Various features of the present invention and the advantages and effects derived therefrom can be understood in more detail by referring to the specific embodiments below. Effects of the invention

[0023] According to a sleep analysis-based disease prediction device installed in a senior citizen's home according to one embodiment of the present invention, since diseases that may occur in the elderly can be predicted based on sleep analysis, it is possible to induce the elderly to receive a rapid diagnosis for the relevant diseases.

[0024] According to a sleep analysis-based disease prediction device installed in a senior citizen's home according to one embodiment of the present invention, diseases can be predicted based on the sleeper's respiration or heart rate measured remotely. In other words, since the sleeper does not need to wear separate equipment, it is possible to analyze accurate sleep patterns without disturbing the sleeper's sleep.

[0025] According to a sleep analysis-based disease prediction device installed in an elderly person's home according to one embodiment of the present invention, it is possible to accurately identify suspected disease patterns corresponding to a target disease based on artificial intelligence and distinguish cases where the sleeper is not sleeping, thereby providing more accurate disease prediction results.

[0026] However, the effects that can be achieved by the sleep analysis-based disease prediction device installed in the elderly's home according to the embodiments of the present invention are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art to which the present invention belongs from the description below. Brief explanation of the drawing

[0028] FIG. 1 is a schematic diagram showing a sleep analysis-based disease prediction system installed in an elderly person's home according to one embodiment of the present invention. FIG. 2 is a block diagram showing a disease prediction device according to one embodiment of the present invention. FIG. 3 is a schematic diagram showing the operation of a posture detection sensor according to one embodiment of the present invention. FIG. 4 is an exemplary diagram showing biometric data and posture data of a sleeper according to an embodiment of the present invention. FIG. 5 is a block diagram showing a disease prediction device according to another embodiment of the present invention. Specific details for implementing the invention

[0029] Hereinafter, embodiments disclosed in this specification will be described in detail with reference to the attached drawings. Identical or similar components regardless of drawing symbols will be assigned the same reference number, and redundant descriptions thereof will be omitted. The suffixes "module" and "part" for components used in the following description are assigned or used interchangeably solely for the ease of drafting the specification and do not inherently possess distinct meanings or roles. That is, the term "part" used in this invention refers to a hardware component such as software, FPGA, or ASIC, and the "part" performs certain roles. However, the meaning of "part" is not limited to software or hardware. The "part" may be configured to reside in an addressable storage medium or may be configured to operate one or more processors. Accordingly, as an example, a 'part' includes components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functionality provided within the components and 'parts' may be combined into a smaller number of components and 'parts' or further separated into additional components and 'parts'.

[0030] In addition, when describing the embodiments disclosed in this specification, if it is determined that a detailed description of related prior art may obscure the essence of the embodiments disclosed in this specification, such detailed description is omitted. Furthermore, the attached drawings are intended only to facilitate understanding of the embodiments disclosed in this specification, and the technical concept disclosed in this specification is not limited by the attached drawings; it should be understood that they include all modifications, equivalents, and substitutions that fall within the spirit and technical scope of the present invention.

[0032] FIG. 1 is a schematic diagram showing a sleep analysis-based disease prediction system installed in an elderly person's home according to one embodiment of the present invention.

[0033] Referring to FIG. 1, a sleep analysis-based disease prediction system installed in an elderly person's home according to one embodiment of the present invention may include a disease prediction device comprising a biosignal sensor (110), a posture detection sensor (120), and a sleep analysis unit (130), a wall pad (D1), and a mobile communication terminal (D2).

[0034] A disease prediction system according to one embodiment of the present invention will be described below with reference to FIG. 1.

[0035] The sleeper (1) may be an elderly person, an elderly person living alone, a patient, etc., and the sleeper (1) may reside in a housing for the elderly. A housing for the elderly may be a house built with consideration for the physical and situational characteristics of the elderly, based on the residence of an elderly person or a household of elderly people.

[0036] The elderly housing may include multiple sensors for detecting emergency situations such as falls, fires, and burglaries, and may also include a wall pad (D1) capable of integrated control to allow the elderly to conveniently adjust the lighting, temperature, and humidity inside the house. Here, the wall pad (D1) may integrate and manage data received from sensors installed within the elderly housing, and depending on the embodiment, it is also possible to implement the sleep analysis unit (130) of the disease prediction device (100) within the wall pad (D1).

[0037] The disease prediction device (100) can collect biosignal data and posture data of a sleeper (1) based on a biosignal sensor (110) and a posture detection sensor (120) installed in the elderly person's home, and can analyze the sleep pattern of the sleeper (1) based on this to determine diseases that may occur in the sleeper (1). Here, if it is determined that a disease has occurred in the sleeper (1), an alarm message can be transmitted to a wall pad (D1) or a pre-registered mobile communication terminal (D2).

[0038] Here, the disease prediction device (100) can perform communication with the biosignal sensor (110) and the posture detection sensor (120) through a network. The method of communication between the disease prediction device (100), the biosignal sensor (110), and the posture detection sensor (120) is not limited, and may include not only communication methods utilizing a communication network that the network may include (e.g., mobile communication network, wired internet, wireless internet, broadcasting network, satellite network, etc.) but also short-range wireless communication between devices. For example, the network may include one or more arbitrary networks among networks such as PAN (personal area network), LAN (local area network), CAN (campus area network), MAN (metropolitan area network), WAN (wide area network), BBN (broadband network), and the Internet.

[0039] Specifically, referring to FIG. 2, a disease prediction device (100) according to one embodiment of the present invention may include a biosignal sensor (110), a posture detection sensor (120), and a sleep analysis unit (130).

[0040] The biosignal sensor (110) can be installed in a bedroom within an elderly person's home. That is, it can be installed on the ceiling or side wall of the bedroom where the sleeper (1) sleeps, and after irradiating a radar signal to the sleeper (1), the reflected radar signal can be received to measure the sleeper's (1) heart rate or respiratory rate. Here, the biosignal sensor (110) can generate biosignal data including the sleeper's (1) heart rate or respiratory rate and transmit it to the sleep analysis unit (130).

[0041] As illustrated in FIG. 1, the biosignal sensor (110) may be installed vertically above the chest of the sleeper (1) to increase the accuracy of the measurement results. Here, the biosignal sensor (110) may use a millimeter wave frequency band capable of measuring millimeter-unit changes in the chest of the sleeper (1), and may measure heart rate and respiratory rate, etc., from the movement of the chest of the sleeper (1).

[0042] The position of the bed (B) of the sleeper (1) can be determined according to the position of the biosignal sensor (110) installed in the elderly home, and the sleeper (1) can be guided to lie down so that their chest is positioned vertically below the biosignal sensor (110) while sleeping. Additionally, the sleeper (1) can be guided to assume a sleeping position lying upright in a direction facing the ceiling.

[0043] A posture detection sensor (120) is installed in the bedroom and can remotely measure the posture of the sleeper (1) to generate posture data. As shown in FIG. 1, the posture detection sensor (120) can be installed on the ceiling or side wall of the bedroom where the sleeper (1) sleeps, and can be installed in a direction facing the sleeper (1) located on the bed (B).

[0044] Referring to FIG. 3, the posture detection sensor (120) can be divided into a posture sensor (121) and a fall sensor (122). Here, the posture sensor (121) is installed to face the bed (B) and can detect the posture of the sleeper (1) on the bed (B). For example, the posture sensor (121) can distinguish and recognize the sleeping posture of the sleeper (1) lying on the bed (B), the sitting posture of the sleeper (1) on the bed (B), the standing posture of the sleeper (1) on the bed (B), etc. Additionally, the fall sensor (122) is installed to face a pre-set fall risk area (A) around the bed (B) and can detect a fall of the sleeper (1) falling from the bed (B). The posture detection sensor (120) can generate posture data that detects the posture or fall of the sleeper (1) and transmit it to the sleep analysis unit (130).

[0045] The sleep analysis unit (130) can analyze the sleep pattern of the sleeper (1) based on biosignal data, and if a suspected disease pattern is found in the sleep pattern of the sleeper (1), it can generate an alarm message.

[0046] Since the sleep analysis unit (130) analyzes the sleep pattern from biosignal data while the sleeper (1) is in a sleeping state, it is necessary to determine whether the sleeper (1) is currently sleeping. However, since the biosignal sensor (110) measures the biosignal of the sleeper (1) based on radar signals, it may be difficult to determine whether the sleeper (1) is sleeping. Therefore, the sleep analysis unit (130) can collect biosignal data during a pre-set sleep time and analyze the sleep pattern of biosignal data, such as respiration or heart rate, that appears during sleep based on the biosignal data collected during the sleep time.

[0047] Here, the sleep time may be a value that the sleeper (1) has set as their own sleep time in advance. For example, if the sleeper (1) has set their sleep time from 9 PM to 4 AM, the sleep analysis unit (130) can collect biosignal data between 9 PM and 4 AM and analyze the sleeper's (1) sleep pattern based on this.

[0048] In addition, according to the embodiment, it is possible to measure changes in illuminance within the bedroom using an illuminance sensor provided in the bedroom and estimate the sleep time of the sleeper (1) based on the changes in illuminance, or to estimate the sleep time based on the on / off operation of the light in the bedroom. That is, since the light in the bedroom is turned off before falling asleep and the light in the bedroom is turned on after waking up, the time between can be estimated as the sleep time.

[0049] In addition, the sleep time of the sleeper (1) can be estimated in various ways, such as by estimating the sleep time based on the charging time and uncharging time of the mobile communication terminal (D2) of the sleeper (1), or by combining the sleep time estimation methods described above.

[0050] Meanwhile, the sleep analysis unit (130) can determine whether there are suspected disease patterns corresponding to various target diseases such as Alzheimer's disease, Parkinson's disease, stroke, and myocardial infarction from the sleep pattern of the sleeper (1).

[0051] According to an embodiment, the sleep analysis unit (130) can find a regular pattern by analyzing the sleep pattern of the sleeper (1) in a time-series manner, and can determine that a suspected disease pattern exists when an irregular pattern deviating from the regular pattern appears. For example, the sleep analysis unit (130) can find a regular pattern through a time-series analysis of the sleep pattern of the sleeper (1) for three months, and can determine that a suspected disease pattern exists if an irregular pattern deviating from the regular pattern persists for a set period (e.g., one week or more). Here, since the regular pattern may appear differently for each sleeper (1), the sleep analysis unit (130) can recognize a personalized regular pattern for each sleeper (1).

[0052] Additionally, according to the embodiment, an analysis model may be included within the sleep analysis unit (130), and the analysis model may be generated by learning the sleep patterns of multiple patients with the target disease as training data. That is, if the sleep patterns of patients who already have the target disease are set as correct answer data and the analysis model is trained, then when the sleep pattern of the sleeper (1) is input into the analysis model, it can determine and provide which target disease the sleep pattern corresponds to. For example, when a sleep pattern is input into the analysis model, it is possible to calculate and provide a score indicating how similar the sleep pattern is to the sleep pattern corresponding to each target disease. In this case, if there is a target disease for which the score is greater than or equal to a set value, it can be determined that the sleeper (1) is highly likely to have the target disease. Here, the analysis model can be implemented based on various types of machine learning models, deep learning models, neural network models, etc.

[0053] Meanwhile, the sleep analysis unit (130) indicates that there is a high risk of the target disease occurring, but it may not actually diagnose the disease. That is, it predicts that there is a high probability of the target disease and recommends that the sleeper (1) visit a hospital or similar facility to receive a diagnosis for the target disease.

[0054] Additionally, the sleeper (1) may not remain in a continuous sleep state during the sleep period and may get up or leave their seat during sleep. For example, they may move to go to the bathroom or drink water. In this case, noise may be included in the collected biosignal data, so it is necessary to remove noise corresponding to a non-sleep state.

[0055] According to an embodiment, if the biosignal data is measured to be below a threshold value for a set period of time or longer, the sleep analysis unit (130) can determine that the sleeper (1) has left the place. That is, when the sleeper (1) leaves the place, the heart rate or respiratory rate measured by the biosignal sensor (110) may show values ​​close to 0. Since there is no case where a person's heart rate and respiratory rate suddenly drop below a threshold value at the same time, the sleep analysis unit (130) determines in this case that the sleeper (1) has left the place, and the biosignal data measured during the leave can be treated as noise.

[0056] In addition, according to the embodiment, it is also possible to determine whether the sleeper (1) has left the position based on the position data generated by the position detection sensor (120). That is, the sleep analysis unit (130) can determine that the sleeper (1) has left the position if the position data of the sleeper (1) within the sleep time corresponds to a position other than a sleeping position.

[0057] As illustrated in FIG. 4, the posture detection sensor (120) can classify the posture of the sleeper (1) into a sitting posture (P1), a sleeping posture (P2), a standing posture (P3), etc. Here, if the posture of the sleeper (1) is detected as a sitting posture (P1) or a standing posture (P3) other than the sleeping posture (P2) during the sleep time, it can be determined that the sleeper (1) is not sleeping. Therefore, all biosignal data measured in postures other than the sleeping posture (P2) can be treated as noise. That is, although FIG. 4 shows that the respiration rate and heart rate, etc., were measured in the sitting posture (P1) or the standing posture (P3) other than the sleeping posture (P2), the respiration rate and heart rate, etc., measured in the sitting posture (P1) or the standing posture (P3) can all be treated as noise.

[0058] Subsequently, the sleep analysis unit (130) can transmit an alarm message to a wall pad (D1) installed in the elderly's home or to a pre-registered mobile communication terminal (D2). That is, if a suspected disease pattern is identified, an alarm message can be generated to notify this, and the sleep analysis unit (130) can transmit the alarm message in various ways. According to an embodiment, it can be displayed on the wall pad (D1) installed in the elderly's home so that the sleeper (1) or guardian can easily check it. In addition, the phone numbers of the mobile communication terminals (D2) of related persons, such as the sleeper, guardian, and attending physician, may be pre-registered within the disease prediction device (100). Therefore, the sleep analysis unit (130) can transmit the corresponding alarm message to the registered phone number to notify the sleeper (1) to receive a medical examination, such as visiting a hospital, regarding the target disease.

[0060] FIG. 5 is a block diagram illustrating a computing environment (10) suitable for use in exemplary embodiments. In the illustrated embodiments, each component may have different functions and capabilities in addition to those described below, and may include additional components in addition to those described below.

[0061] The illustrated computing environment (10) includes a computing device (12). In one embodiment, the computing device (12) may be a disease prediction device (100) or a sleep analysis unit (130).

[0062] The computing device (12) includes at least one processor (14), a computer-readable storage medium (16), and a communication bus (18). The processor (14) can cause the computing device (12) to operate according to the exemplary embodiment described above. For example, the processor (14) can execute one or more programs stored in the computer-readable storage medium (16). The one or more programs may include one or more computer-executable instructions, and the computer-executable instructions may be configured to cause the computing device (12) to perform operations according to the exemplary embodiment when executed by the processor (14).

[0063] A computer-readable storage medium (16) is configured to store computer-executable instructions or program code, program data and / or other suitable forms of information. A program (20) stored in the computer-readable storage medium (16) includes a set of instructions executable by a processor (14). In one embodiment, the computer-readable storage medium (16) may be memory (volatile memory such as random access memory, non-volatile memory, or a suitable combination thereof), one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other forms of storage media that are accessed by a computing device (12) and capable of storing desired information, or a suitable combination thereof.

[0064] The communication bus (18) interconnects various other components of the computing device (12), including the processor (14) and the computer-readable storage medium (16).

[0065] The computing device (12) may also include one or more input / output interfaces (22) and one or more network communication interfaces (26) that provide interfaces for one or more input / output devices (24). The input / output interfaces (22) and the network communication interfaces (26) are connected to a communication bus (18). The input / output devices (24) may be connected to other components of the computing device (12) through the input / output interfaces (22). An exemplary input / output device (24) may include an input device such as a pointing device (such as a mouse or trackpad), a keyboard, a touch input device (such as a touchpad or touchscreen), a voice or sound input device, various types of sensor devices and / or imaging devices, and / or an output device such as a display device, a printer, a speaker and / or a network card. An exemplary input / output device (24) may be included inside the computing device (12) as a component constituting the computing device (12), or it may be connected to the computing device (12) as a separate device distinct from the computing device (12).

[0067] The present invention is not limited by the aforementioned embodiments and attached drawings. It will be obvious to those skilled in the art that the components according to the present invention can be substituted, modified, and changed within the scope of the technical concept of the present invention without departing from the spirit of the invention. Explanation of the symbols

[0069] 1: Sleeper 100: Disease prediction device 110: Biosignal sensor 120: Posture detection sensor 130: Sleep Analysis Department

Claims

Claim 1 A sleep analysis-based disease prediction device installed in a home for the elderly, comprising: a biosignal sensor installed in a bedroom to remotely measure the heart rate or respiration of a sleeper and generate biosignal data; and a posture detection sensor installed in the bedroom to remotely measure the posture of a sleeper and generate posture data. The sleep analysis unit includes a sleep analysis unit that analyzes the sleep pattern of the sleeper based on the biosignal data and posture data, and generates an alarm message via a wall pad installed in the elderly home when a suspected disease pattern is identified from the sleep pattern; the biosignal sensor is installed on the ceiling or side wall of the bedroom to irradiate a radar signal to the sleeper and receives the reflected radar signal to measure the sleeper's heart rate or respiratory rate, using a millimeter wave frequency band; the sleep analysis unit determines that the sleeper has left the room if the posture data corresponds to a posture other than a sleeping posture within a preset sleep time, and processes the biosignal data measured during the sleeper's departure as noise; the sleep analysis unit analyzes the sleep pattern chronologically to find a rule pattern personalized for each sleeper, and determines that a suspected disease pattern exists if an irregular pattern deviating from the rule pattern appears; the sleep analysis unit utilizes an analysis model generated by learning the sleep patterns of patients having at least one of Alzheimer's disease, Parkinson's disease, stroke, and myocardial infarction as training data, and A sleep analysis-based disease prediction device that identifies a suspected disease pattern for at least one of Alzheimer's disease, Parkinson's disease, stroke, and myocardial infarction from a sleep pattern, and determines the position of the sleeper's bed within the elderly residence according to the position of the biosignal sensor. Claim 2 delete Claim 3 A sleep analysis-based disease prediction device according to claim 1, wherein the sleep analysis unit collects biosignal data during a preset sleep time and analyzes the sleep pattern of the sleeper, wherein the sleep time is set based on the sleeper's preset value, changes in illumination within the bedroom, and the on / off operation of the light within the bedroom. Claim 4 delete Claim 5 delete Claim 6 A sleep analysis-based disease prediction device according to claim 1, wherein the sleep analysis unit determines that the sleeper has left the room if the biosignal data is measured to be below a threshold value for a set time or longer, and processes the biosignal data measured during the sleeper's departure as noise. Claim 7 delete Claim 8 A sleep analysis-based disease prediction device according to claim 1, wherein the sleep analysis unit transmits the alarm message to a wall pad installed in the elderly home or to a pre-registered mobile communication terminal.

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