Monitoring method and system for nursing old stroke patients at home

Abstract

The invention discloses a monitoring method and system for nursing old stroke patients at home, and belongs to the technical field of dynamic monitoring. Acquiring turning frequency, breathing frequency and heart rate data of the home old stroke patient sleeping at night; performing time window division on the night sleep duration; calculating a night sleep quality index in a single time window; calculating a breathing fluctuation variable quantity and a turning-over fluctuation variable quantity, and calculating a night sleep quality abnormal degree of the household senile cerebral apoplexy patient in the next time window; presetting a night sleep quality abnormality threshold value, and analyzing and reminding medical staff to perform nursing early warning. According to the invention, by calculating the abnormal degree of the sleep quality and combining the breathing and turning-over fluctuation change of the patient, the abnormal situation of sleep is accurately identified, and the medical staff is timely notified to carry out intervention, so that the accuracy of data and the continuity of monitoring are improved, and the safety and response efficiency of home care are improved.

Description

Technical Field

[0001] The present invention relates to the field of dynamic monitoring technology, and in particular to a monitoring method and system for caring for elderly stroke patients at home. Background Art

[0002] In recent years, the rapid development of intelligent medical monitoring technology has led to the increasingly widespread application of non-contact monitoring equipment, wearable devices and artificial intelligence algorithms in clinical care. Among them, sleep monitoring methods based on physiological signals have gradually replaced the traditional methods that rely on medical staff observation and patient subjective feedback, improving the objectivity and real-time nature of the data. At present, there are sleep monitoring systems for the general population on the market, such as wearable devices based on photoplethysmography (PPG) or electrocardiogram (ECG) signals, and non-contact monitoring devices using radar or infrared sensing technology. However, these technologies are mostly used for healthy individuals or patients with mild sleep disorders. For stroke patients, especially those living at home, the changes in their physiological parameters are more complex. Existing technologies often find it difficult to accurately capture early signs of abnormalities during sleep at night, resulting in delayed warnings and affecting rehabilitation effects.

[0003] Traditional wearable devices are easily affected by wearing habits, device slippage and patient compliance, resulting in incomplete monitoring data or large errors; secondly, although non-contact monitoring improves comfort, its ability to identify patients' tiny physiological fluctuations is still limited, especially during long-term monitoring, and is easily disturbed by environmental noise; existing sleep monitoring methods mostly rely on a single physiological parameter, such as only monitoring heart rate or respiratory rate, and fail to comprehensively analyze the correlation between turning frequency, respiratory fluctuations and heart rate changes, resulting in inaccurate judgment of abnormal situations, and it is difficult to meet the special needs of stroke patients for sleep quality monitoring; especially in clinical practice, abnormal sleep at night in stroke patients is often an early sign of deteriorating health status. Existing technologies lack quantitative analysis and early warning mechanisms for sleep abnormalities, making it difficult to provide targeted care recommendations. Summary of the invention

[0004] The object of the present invention is to provide a monitoring method and system for caring for elderly stroke patients at home, so as to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A monitoring method for caring for elderly stroke patients at home, the method comprising the following steps: step S1: obtaining the turning over frequency, breathing frequency and heart rate data of the elderly stroke patients at home during sleep at night; step S2: dividing the nighttime sleep duration of the elderly stroke patients at home into time windows; based on the turning over frequency, breathing frequency and heart rate data, calculating the nighttime sleep quality index within a single time window; step S3: calculating the breathing fluctuation change amount and the turning over fluctuation change amount of the elderly stroke patients at home between adjacent time windows; based on the nighttime sleep quality index, breathing fluctuation change amount and turning over fluctuation change amount, calculating the nighttime sleep quality abnormality of the elderly stroke patients at home in the next time window; step S4: presetting a threshold value for the abnormality of nighttime sleep quality, analyzing and reminding medical staff to provide care warning.

[0007] As a preferred embodiment of the monitoring method for caring for elderly stroke patients at home described in the present invention, after authorization by the patient, a non-contact monitoring device is deployed in the bed area of ​​the elderly stroke patient at home. The non-contact monitoring device is used to monitor the nighttime sleep movement pattern of the elderly stroke patient at home, and the nighttime sleep movement pattern includes turning over frequency and breathing frequency.

[0008] The home-based elderly stroke patient wears a wearable device on the wrist and ankle, and the wearable device has a built-in heart rate detection sensor; the heart rate detection sensor is used to collect heart rate data of the home-based elderly stroke patient when sleeping at night.

[0009] As a preferred embodiment of the monitoring method for caring for elderly stroke patients at home described in the present invention, the nighttime sleep duration of the elderly stroke patients at home is evenly divided into N time windows, and the turning frequency data, respiratory frequency data and heart rate data in a single time window are obtained, and the turning frequency data, respiratory frequency data and heart rate data obtained in the ath time window are respectively recorded as TOF a ,RF a and HF a .

[0010] Based on turning frequency data TOF a , respiratory rate data RF a and heart rate data HF a , calculate the nighttime sleep quality index of the elderly stroke patients at home in the ath time window, and the calculation formula is as follows:

[0011]

[0012] Among them, SQI arepresents the nighttime sleep quality index of the elderly stroke patient at home in the ath time window, α represents the preset turning over influence weight coefficient, β represents the preset turning over sensitivity weight coefficient, δ represents the preset breathing rate weight coefficient, and μ represents the preset heart rate data weight coefficient.

[0013] It should be noted that in this formula Turning frequency TOF a As an independent variable, it is used to quantify the impact of turning frequency on sleep quality; turning frequency that is too high or too low may reflect poor sleep quality, and this function can convert it into a suitable numerical range; log(1+RF a ) Using the logarithmic function, the respiratory frequency RF a As the independent variable, the stability of respiratory frequency is closely related to sleep quality. The logarithmic function can appropriately scale and transform the changes in respiratory frequency to reflect its impact on sleep quality; μ×HF a Indicates heart rate data HF a Impact on sleep quality: Heart rate usually remains relatively stable during sleep. Abnormal heart rate fluctuations may indicate sleep problems. Here, the negative correlation reflects its negative effect on sleep quality. Sleep quality is affected by many physiological factors. Turning over frequency, breathing rate and heart rate are all important factors. Taking these factors into consideration can more comprehensively evaluate sleep quality. Different function forms are used to process each factor separately because they have different characteristics of affecting sleep quality. Logistic function, logarithmic function, etc. can better capture these characteristics.

[0014] This step can comprehensively and quantitatively evaluate sleep quality by integrating multi-dimensional physiological data. Compared with single indicator evaluation, it is more scientific and accurate, and provides a reliable quantitative basis for subsequent sleep state analysis and abnormal judgment. At the same time, by setting the weight coefficient, the influence of each factor can be flexibly adjusted according to the actual situation and research focus.

[0015] As a preferred embodiment of the monitoring method for caring for elderly stroke patients at home described in the present invention, the respiratory fluctuation change amount and the turning fluctuation change amount of the elderly stroke patients at home between adjacent time windows are calculated, and the calculation formula is as follows:

[0016] ΔRF a+1 =|RF a+1 -RF a |;

[0017] ΔTOF a+1 =|TOF a+1 -TOF a |;

[0018] Among them, ΔRFa+1 Indicates the amount of respiratory fluctuation, RF a+1 Represents the respiratory rate data obtained in the a+1th time window, ΔTOF a+1 Indicates the amount of fluctuation, TOF a+1 Represents the turning frequency data obtained in the a+1th time window.

[0019] It should be noted that these two formulas measure the dynamic changes of breathing and turning over frequency during sleep by calculating the absolute value of the difference between the breathing frequency and turning over frequency in adjacent time windows. The absolute value ensures that the change is non-negative and reflects the magnitude of the change. Since the sleep process is not static, the breathing and turning over frequencies will change over time. These changes may reflect the patient's sleep state transition, physical discomfort or other abnormal conditions. By calculating the change in adjacent time windows, these dynamic change information can be captured, providing clues for judging whether sleep is normal, and simply and intuitively reflecting the dynamic changes of breathing and turning over frequencies, providing dynamic data support for the subsequent calculation of sleep quality abnormality, and being able to promptly detect sudden changes in breathing and turning over frequencies during sleep, which helps to more accurately judge sleep abnormalities.

[0020] Based on the nighttime sleep quality index SQI of the elderly stroke patients at home in the ath time window a , respiratory fluctuation variation ΔRF a+1 and the turning fluctuation variation ΔTOF a+1 , calculate the abnormality of the nighttime sleep quality of the elderly stroke patients at home in the a+1th time window, and the calculation formula is as follows:

[0021]

[0022] Among them, ASQ a+1 SQI represents the abnormality of the nighttime sleep quality of the elderly stroke patients at home in the a+1th time window. a ref Indicates the preset reference value of the nighttime sleep quality index, ΔRF a+1 ref Indicates the preset reference value of respiratory fluctuation change, ΔTOF a+1 ref represents the preset reference value of the turning over fluctuation variation, γ, θ and τ represent the preset nighttime sleep quality index influencing factor, breathing fluctuation variation influencing factor and turning over fluctuation variation influencing factor respectively.

[0023] It should be noted that this formula is based on the sleep quality index SQI a , respiratory fluctuation variation ΔRF a+1 and the turning fluctuation variation ΔTOFa+1 , and are respectively compared with their respective preset reference values ​​SQIa ref , ΔRF a+1 ref , ΔTOF a+1 ref By comparison, the influence of the difference between each factor and the reference value is magnified through the square operation, and then the abnormality of sleep quality is comprehensively calculated through the preset influencing factors γ, θ and τ. Because abnormal sleep quality is not only related to the sleep quality at a certain moment, but also related to the changes in physiological parameters during sleep, combining the sleep quality index and the fluctuation change of physiological parameters and comparing them with the reference value can more comprehensively evaluate the abnormality of sleep. The square operation and the setting of influencing factors can highlight the influence of factors that deviate greatly from normal conditions. This step comprehensively considers the static and dynamic factors of sleep quality, and can more accurately judge whether sleep is abnormal. The comparison with the reference value makes the judgment have a clear standard, and by adjusting the influencing factors, the influence weight of each factor on the abnormality can be flexibly adjusted according to actual needs, which improves the adaptability and accuracy of the model to different situations and provides a reliable basis for timely warning.

[0024] As a preferred embodiment of the monitoring method for caring for elderly stroke patients at home, based on the abnormal sleep quality ASQ of the elderly stroke patients at home in the a+1 time window a+1 , preset the threshold value of abnormal sleep quality at night ω.

[0025] If the night sleep quality is abnormal ASQ a+1 >ω, it is determined that the elderly stroke patient at home has sleep abnormalities within the a+1th time window, and a care warning is issued to the medical staff through the APP.

[0026] Let a=a+1, obtain the abnormality of nighttime sleep quality in each time window in real time, and perform dynamic monitoring on the elderly stroke patients at home.

[0027] A monitoring system for caring for elderly stroke patients at home comprises: a data acquisition module, a sleep quality index calculation module, a change and abnormality calculation module and an analysis and early warning module.

[0028] The data acquisition module is used to acquire the turning frequency, breathing frequency and heart rate data of elderly stroke patients at home during their sleep at night.

[0029] The sleep quality index calculation module is used to divide the nighttime sleep duration of the elderly stroke patient at home into time windows; and to calculate the nighttime sleep quality index within a single time window based on the turning over frequency, breathing frequency and heart rate data.

[0030] The variation and abnormality calculation module calculates the respiratory fluctuation variation and the turning over fluctuation variation of the elderly stroke patient at home between adjacent time windows; and calculates the abnormality of the night sleep quality of the elderly stroke patient at home in the next time window based on the night sleep quality index, the respiratory fluctuation variation and the turning over fluctuation variation.

[0031] The analysis and warning module presets a threshold for abnormal sleep quality at night, analyzes and reminds medical staff to provide care warnings.

[0032] Furthermore, the data acquisition module includes a nighttime sleep body movement pattern acquisition unit and a heart rate data acquisition unit.

[0033] The nighttime sleep movement pattern acquisition unit: after authorization by the patient, a non-contact monitoring device is deployed in the bed area of ​​the elderly stroke patient at home, and the non-contact monitoring device is used to monitor the nighttime sleep movement pattern of the elderly stroke patient at home, and the nighttime sleep movement pattern includes turning over frequency and breathing frequency.

[0034] The heart rate data acquisition unit: a wearable device is worn on the wrists and ankles of the elderly stroke patient at home, and the wearable device has a built-in heart rate detection sensor; the heart rate detection sensor is used to collect the heart rate data of the elderly stroke patient at home when sleeping at night.

[0035] Furthermore, the sleep quality index calculation module includes a sleep quality index calculation unit.

[0036] The sleep quality index calculation unit: evenly divides the nighttime sleep duration of the elderly stroke patient living at home into N time windows, and obtains turning over frequency data, breathing frequency data and heart rate data within a single time window; based on the turning over frequency data, breathing frequency data and heart rate data, calculates the nighttime sleep quality index of the elderly stroke patient living at home within a single time window.

[0037] Furthermore, the variation and abnormality calculation module includes a variation calculation unit and an abnormality calculation unit.

[0038] The variation calculation unit is used to calculate the breathing fluctuation variation and turning fluctuation variation of the elderly stroke patient at home between adjacent time windows.

[0039] The abnormality calculation unit calculates the abnormality of the nighttime sleep quality of the elderly stroke patient at home in the next time window based on the nighttime sleep quality index, respiratory fluctuation change and turning fluctuation change of the elderly stroke patient at home in a single time window.

[0040] Furthermore, the analysis and warning module includes an analysis and warning unit.

[0041] The analysis and early warning unit: presets a threshold value for abnormal nighttime sleep quality based on the abnormal nighttime sleep quality of the elderly stroke patient at home in the next time window; if the abnormal nighttime sleep quality is greater than the threshold value for abnormal nighttime sleep quality, it is determined that the elderly stroke patient at home has sleep abnormalities in the next time window, and a care early warning is issued to medical staff through the APP; iterates the time window, obtains the abnormal nighttime sleep quality in each time window in real time, and dynamically monitors the elderly stroke patient at home.

[0042] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: in a monitoring method and system for caring for elderly stroke patients at home provided by the present invention, a non-contact monitoring device is combined with a wearable device to realize the continuous collection of sleep data of elderly stroke patients at home at night, and the sleep quality is finely evaluated through time window division, and the abnormal degree of sleep quality is further calculated, and the abnormal sleep condition is accurately identified in combination with the patient's breathing and turning fluctuations. Finally, the system triggers an intelligent early warning mechanism based on a preset threshold, and promptly notifies medical staff to intervene. This method not only improves the accuracy of the data and the continuity of monitoring, but also effectively identifies sudden health risks and improves the safety and response efficiency of home care. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0044] Figure 1 It is a schematic diagram of the steps of a monitoring method for caring for elderly stroke patients at home according to the present invention;

[0045] Figure 2 It is a structural schematic diagram of a monitoring system for caring for elderly stroke patients at home according to the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] See also Figure 1 In the first embodiment, a monitoring method for caring for elderly stroke patients at home is provided, the method comprising the following steps:

[0048] Step S1: Obtain the turning frequency, breathing rate and heart rate data of elderly stroke patients at home during sleep at night.

[0049] Specifically, after authorization by the patient, a non-contact monitoring device is deployed in the bed area of ​​the elderly stroke patient at home. The non-contact monitoring device is used to monitor the nighttime sleep movement pattern of the elderly stroke patient at home, and the nighttime sleep movement pattern includes turning over frequency and breathing frequency.

[0050] Furthermore, the elderly stroke patient at home wears a wearable device on his wrist and ankle, and the wearable device has a built-in heart rate detection sensor; the heart rate detection sensor is used to collect heart rate data of the elderly stroke patient at home when he sleeps at night.

[0051] Step S2: dividing the nighttime sleep duration of the elderly stroke patient at home into time windows; and calculating the nighttime sleep quality index within a single time window based on the turning over frequency, breathing frequency and heart rate data.

[0052] Specifically, the nighttime sleep duration of the elderly stroke patient at home is evenly divided into N time windows, and the turning frequency data, respiratory frequency data, and heart rate data in a single time window are obtained, and the turning frequency data, respiratory frequency data, and heart rate data obtained in the ath time window are respectively recorded as TOF a ,RF a and HF a .

[0053] Further, based on the turning frequency data TOF a , respiratory rate data RF a and heart rate data HF a , calculate the nighttime sleep quality index of the elderly stroke patients at home in the ath time window, and the calculation formula is as follows:

[0054]

[0055] Among them, SIQ a represents the nighttime sleep quality index of the elderly stroke patient at home in the ath time window, α represents the preset turning over influence weight coefficient, β represents the preset turning over sensitivity weight coefficient, δ represents the preset breathing rate weight coefficient, and μ represents the preset heart rate data weight coefficient.

[0056] Step S3: Calculate the respiratory fluctuation change and the turning over fluctuation change of the elderly stroke patient at home between adjacent time windows; calculate the abnormality of the nighttime sleep quality of the elderly stroke patient at home in the next time window based on the nighttime sleep quality index, the respiratory fluctuation change and the turning over fluctuation change.

[0057] Specifically, the breathing fluctuation change amount and the turning fluctuation change amount of the elderly stroke patient at home between adjacent time windows are calculated, and the calculation formula is as follows:

[0058] ΔRF a+1 =|RF a+1 -RF a |;

[0059] ΔTOF a+1 =|TOF a+1 -TOF a |;

[0060] Among them, ΔRF a+1 Indicates the amount of respiratory fluctuation, RF a+1 Represents the respiratory rate data obtained in the a+1th time window, ΔTOF a+1 Indicates the amount of fluctuation, TOF a+1 Represents the turning frequency data obtained in the a+1th time window.

[0061] Further, based on the nighttime sleep quality index SQI of the elderly stroke patients at home in the ath time window a , respiratory fluctuation variation ΔRF a+1 and the turning fluctuation variation ΔTOF a+1 , calculate the abnormality of the nighttime sleep quality of the elderly stroke patients at home in the a+1th time window, and the calculation formula is as follows:

[0062]

[0063] Among them, ASQ a+1 SQI represents the abnormality of the nighttime sleep quality of the elderly stroke patients at home in the a+1th time window. a ref Indicates the preset reference value of the nighttime sleep quality index, ΔRF a+1 ref Indicates the preset reference value of respiratory fluctuation change, ΔTOF a+1 ref represents the preset reference value of the turning over fluctuation variation, γ, θ and τ represent the preset nighttime sleep quality index influencing factor, breathing fluctuation variation influencing factor and turning over fluctuation variation influencing factor respectively.

[0064] Step S4: preset a threshold for abnormal sleep quality at night, analyze and remind medical staff to provide care warning.

[0065] Based on the abnormal sleep quality ASQ of the elderly stroke patients at home in the a+1 time window a+1 , preset the threshold value of abnormal sleep quality at night ω.

[0066] If the night sleep quality is abnormal ASQ a+1 >ω, it is determined that the elderly stroke patient at home has sleep abnormalities within the a+1th time window, and a care warning is issued to the medical staff through the APP.

[0067] Let a=a+1, obtain the abnormality of nighttime sleep quality in each time window in real time, and perform dynamic monitoring on the elderly stroke patients at home.

[0068] Specifically, as the trend of population aging becomes increasingly prominent, home care for elderly stroke patients has become a focus of social attention. The Brain Health Care Home Project actively introduces "a monitoring method for home care of elderly stroke patients" to effectively improve the quality of care and ensure the health of patients. Taking Mr. Zhang, a 65-year-old stroke patient living in a community in Xuzhou, as an example, the implementation process of this method is elaborated in detail:

[0069] In step S1, after obtaining full authorization from Mr. Zhang and his family, the Brain Health Care Home team carefully deployed advanced non-contact monitoring devices in Mr. Zhang's bed area according to the project plan. At the same time, Mr. Zhang was equipped with a smart wearable device independently developed by Brain Health Care Home, which integrates multiple functions and can accurately collect heart rate data. At night, the non-contact monitoring device begins to monitor Mr. Zhang's turning over frequency and breathing frequency in real time, and the smart wearable device synchronously collects his heart rate information, and quickly transmits this data to the cloud platform of Brain Health Care Home through a stable network connection. This process is like a digital platform collecting basic data from each functional module, providing a key basis for subsequent analysis.

[0070] Entering step S2, the cloud platform is like an intelligent data processing center. After receiving the data, it immediately uses the night sleep quality index formula for in-depth calculation. Assuming that in a certain night period, Mr. Zhang's turning frequency, breathing frequency and heart rate data are substituted into the formula, after calculation, the night sleep quality index of Mr. Zhang in that time period is obtained. If the value is lower than the normal range, it indicates that Mr. Zhang's sleep quality is poor during that period and there may be health risks. This is like the digital platform analyzing the data of each functional module and evaluating its operating status.

[0071] Next is step S3, where the cloud platform further uses its powerful data analysis capabilities to calculate the changes in breathing fluctuations and turning fluctuations based on the data in adjacent time windows. The abnormality of nighttime sleep quality is then calculated using the formula for abnormality of nighttime sleep quality. If the calculation result shows that Mr. Zhang's abnormality of nighttime sleep quality is higher than the preset threshold, it means that his sleep state has abnormal fluctuations and needs close attention. This is similar to the digital platform determining abnormal conditions from the network traffic fluctuation curve, providing a basis for subsequent decision-making.

[0072] Finally, in step S4, when the abnormality of nighttime sleep quality is greater than the abnormality threshold of nighttime sleep quality, the Brain Health Care Home Cloud Platform quickly starts the early warning mechanism and sends an alarm message to the medical team responsible for Mr. Zhang through a dedicated APP. After receiving the early warning, the medical team will check Mr. Zhang's detailed sleep data and abnormal situation analysis report as soon as possible. If it is found that Mr. Zhang has abnormal sleep quality for several consecutive nights, the medical team will communicate in depth with Mr. Zhang's family according to the service process of Brain Health Care Home. It is recommended to adjust Mr. Zhang's sleeping environment, such as changing to a more comfortable mattress, adjusting the temperature and humidity of the bedroom, etc. At the same time, arrange professional medical staff to visit the door to further evaluate Mr. Zhang's physical condition and formulate personalized care plans according to his specific situation. This series of operations is like a digital platform that focuses on maintaining popular functions and optimizing and integrating unpopular functions to ensure the efficient operation of the entire system, effectively improve the level of home care for elderly stroke patients, and promote the healthy recovery of patients.

[0073] See also Figure 2 In the second embodiment of the present invention, a monitoring system for caring for elderly stroke patients at home is provided, the system comprising: a data acquisition module, a sleep quality index calculation module, a change and abnormality calculation module and an analysis and early warning module.

[0074] The data acquisition module is used to acquire the turning frequency, breathing frequency and heart rate data of elderly stroke patients at home during their sleep at night.

[0075] The sleep quality index calculation module is used to divide the nighttime sleep duration of the elderly stroke patient at home into time windows; and to calculate the nighttime sleep quality index within a single time window based on the turning over frequency, breathing frequency and heart rate data.

[0076] The variation and abnormality calculation module calculates the respiratory fluctuation variation and the turning over fluctuation variation of the elderly stroke patient at home between adjacent time windows; and calculates the abnormality of the night sleep quality of the elderly stroke patient at home in the next time window based on the night sleep quality index, the respiratory fluctuation variation and the turning over fluctuation variation.

[0077] The analysis and warning module presets a threshold for abnormal sleep quality at night, analyzes and reminds medical staff to provide care warnings.

[0078] Furthermore, the data acquisition module includes a nighttime sleep body movement pattern acquisition unit and a heart rate data acquisition unit.

[0079] The nighttime sleep movement pattern acquisition unit: after authorization by the patient, a non-contact monitoring device is deployed in the bed area of ​​the elderly stroke patient at home, and the non-contact monitoring device is used to monitor the nighttime sleep movement pattern of the elderly stroke patient at home, and the nighttime sleep movement pattern includes turning over frequency and breathing frequency.

[0080] The heart rate data acquisition unit: a wearable device is worn on the wrists and ankles of the elderly stroke patient at home, and the wearable device has a built-in heart rate detection sensor; the heart rate detection sensor is used to collect the heart rate data of the elderly stroke patient at home when sleeping at night.

[0081] Furthermore, the sleep quality index calculation module includes a sleep quality index calculation unit.

[0082] The sleep quality index calculation unit: evenly divides the nighttime sleep duration of the elderly stroke patient living at home into N time windows, and obtains turning over frequency data, breathing frequency data and heart rate data within a single time window; based on the turning over frequency data, breathing frequency data and heart rate data, calculates the nighttime sleep quality index of the elderly stroke patient living at home within a single time window.

[0083] Furthermore, the variation and abnormality calculation module includes a variation calculation unit and an abnormality calculation unit.

[0084] The variation calculation unit is used to calculate the breathing fluctuation variation and turning fluctuation variation of the elderly stroke patient at home between adjacent time windows.

[0085] The abnormality calculation unit calculates the abnormality of the nighttime sleep quality of the elderly stroke patient at home in the next time window based on the nighttime sleep quality index, respiratory fluctuation change and turning fluctuation change of the elderly stroke patient at home in a single time window.

[0086] Furthermore, the analysis and warning module includes an analysis and warning unit.

[0087] The analysis and early warning unit: presets a threshold value for abnormal nighttime sleep quality based on the abnormal nighttime sleep quality of the elderly stroke patient at home in the next time window; if the abnormal nighttime sleep quality is greater than the threshold value for abnormal nighttime sleep quality, it is determined that the elderly stroke patient at home has sleep abnormalities in the next time window, and a care early warning is issued to medical staff through the APP; iterates the time window, obtains the abnormal nighttime sleep quality in each time window in real time, and dynamically monitors the elderly stroke patient at home.

[0088] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0089] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A monitoring method for caring for elderly stroke patients at home, characterized in that: The method comprises the following steps: Step S1: Obtaining data on turning over frequency, breathing rate and heart rate of elderly stroke patients at home during sleep at night; Step S2: dividing the nighttime sleep duration of the elderly stroke patient at home into time windows; and calculating the nighttime sleep quality index within a single time window based on the turning over frequency, breathing frequency and heart rate data; Step S3: calculating the respiratory fluctuation variation and the turning over fluctuation variation of the elderly stroke patient at home between adjacent time windows; calculating the abnormality of the nighttime sleep quality of the elderly stroke patient at home in the next time window based on the nighttime sleep quality index, the respiratory fluctuation variation and the turning over fluctuation variation; Step S4: preset a threshold for abnormal sleep quality at night, analyze and remind medical staff to provide care warning.

2. A monitoring method for caring for elderly stroke patients at home according to claim 1, characterized in that: The specific implementation process of step S1 includes: After authorization by the patient, a non-contact monitoring device is deployed in the bed area of ​​the elderly stroke patient at home, and the non-contact monitoring device is used to monitor the nighttime sleep movement pattern of the elderly stroke patient at home, and the nighttime sleep movement pattern includes turning over frequency and breathing frequency; The home-based elderly stroke patient wears a wearable device on the wrist and ankle, and the wearable device has a built-in heart rate detection sensor; the heart rate detection sensor is used to collect heart rate data of the home-based elderly stroke patient when sleeping at night.

3. A monitoring method for caring for elderly stroke patients at home according to claim 2, characterized in that: The specific implementation process of step S2 includes: The nighttime sleep duration of the elderly stroke patient at home is evenly divided into N time windows, and the turning frequency data, respiratory frequency data and heart rate data in a single time window are obtained. The turning frequency data, respiratory frequency data and heart rate data obtained in the ath time window are respectively recorded as TOF a ,RF a and HF a ; Based on turning frequency data TOF a , respiratory rate data RF a and heart rate data HF a , calculate the nighttime sleep quality index of the elderly stroke patients at home in the ath time window, and the calculation formula is as follows: Among them, SQI a represents the nighttime sleep quality index of the elderly stroke patient at home in the ath time window, α represents the preset turning over influence weight coefficient, β represents the preset turning over sensitivity weight coefficient, δ represents the preset breathing rate weight coefficient, and μ represents the preset heart rate data weight coefficient.

4. A monitoring method for caring for elderly stroke patients at home according to claim 3, characterized in that: The specific implementation process of step S3 includes: The breathing fluctuation change amount and the turning fluctuation change amount of the elderly stroke patient at home between adjacent time windows are calculated, and the calculation formula is as follows: ΔRF a+1 =|RF a+1 -RF a |; ΔTOF a+1 =|TOF a+1 -TOF a |; Among them, ΔRF a+1 Indicates the amount of respiratory fluctuation, RF a+1 Represents the respiratory rate data obtained in the a+1th time window, ΔTOF a+1 Indicates the amount of fluctuation, TOF a+1 represents the turning frequency data obtained in the a+1th time window; Based on the nighttime sleep quality index SQI of the elderly stroke patients at home in the ath time window a , respiratory fluctuation variation ΔRF a+1 and the turning fluctuation variation ΔTOF a+1 , calculate the abnormality of the nighttime sleep quality of the elderly stroke patients at home in the a+1th time window, and the calculation formula is as follows: Among them, ASQ a+1 ASQ represents the abnormality of the nighttime sleep quality of the elderly stroke patients at home in the a+1th time window. a ref Indicates the preset reference value of the nighttime sleep quality index, ΔRF a+1 ref Indicates the preset reference value of respiratory fluctuation change, ΔTOF a+1 ref represents the preset reference value of the turning over fluctuation variation, γ, θ and τ represent the preset nighttime sleep quality index influencing factor, breathing fluctuation variation influencing factor and turning over fluctuation variation influencing factor respectively.

5. A monitoring method for caring for elderly stroke patients at home according to claim 4, characterized in that: The specific implementation process of step S4 includes: Based on the abnormal sleep quality ASQ of the elderly stroke patients at home in the a+1 time window a+1 , preset the threshold value of abnormal sleep quality at night ω; If the night sleep quality is abnormal ASQ a+1 >ω, it is determined that the elderly stroke patient at home has abnormal sleep in the a+1th time window, and a care warning is issued to the medical staff through the APP; Let a=a+1, obtain the abnormality of nighttime sleep quality in each time window in real time, and perform dynamic monitoring on the elderly stroke patients at home.

6. A monitoring system for caring for elderly stroke patients at home, executing a monitoring method for caring for elderly stroke patients at home as described in any one of claims 1 to 5, characterized in that: The system includes: a data acquisition module, a sleep quality index calculation module, a change and abnormality calculation module and an analysis and warning module; The data acquisition module is used to acquire the turning frequency, breathing rate and heart rate data of elderly stroke patients at home during sleep at night; The sleep quality index calculation module is used to divide the nighttime sleep duration of the elderly stroke patient at home into time windows; and to calculate the nighttime sleep quality index within a single time window based on the turning over frequency, breathing frequency and heart rate data; The variation and abnormality calculation module is used to calculate the respiratory fluctuation variation and the turning fluctuation variation of the elderly stroke patient at home between adjacent time windows; based on the night sleep quality index, the respiratory fluctuation variation and the turning fluctuation variation, calculate the abnormality of the night sleep quality of the elderly stroke patient at home in the next time window; The analysis and warning module presets a threshold for abnormal sleep quality at night, analyzes and reminds medical staff to provide care warnings.

7. A monitoring system for caring for elderly stroke patients at home according to claim 6, characterized in that: The data acquisition module includes a nighttime sleep body movement pattern acquisition unit and a heart rate data acquisition unit; The nighttime sleep body movement pattern acquisition unit: after authorization by the patient, deploys a non-contact monitoring device in the bed area of ​​the elderly stroke patient at home, and the non-contact monitoring device is used to monitor the nighttime sleep body movement pattern of the elderly stroke patient at home, and the nighttime sleep body movement pattern includes turning over frequency and breathing frequency; The heart rate data acquisition unit: a wearable device is worn on the wrists and ankles of the elderly stroke patient at home, and the wearable device has a built-in heart rate detection sensor; the heart rate detection sensor is used to collect the heart rate data of the elderly stroke patient at home when sleeping at night.

8. A monitoring system for caring for elderly stroke patients at home according to claim 7, characterized in that: The sleep quality index calculation module includes a sleep quality index calculation unit; The sleep quality index calculation unit: evenly divides the nighttime sleep duration of the elderly stroke patient living at home into N time windows, and obtains turning over frequency data, breathing frequency data and heart rate data within a single time window; based on the turning over frequency data, breathing frequency data and heart rate data, calculates the nighttime sleep quality index of the elderly stroke patient living at home within a single time window.

9. A monitoring system for caring for elderly stroke patients at home according to claim 8, characterized in that: The variation and abnormality calculation module includes a variation calculation unit and an abnormality calculation unit; The variation calculation unit is used to calculate the breathing fluctuation variation and the turning fluctuation variation of the elderly stroke patient at home between adjacent time windows; The abnormality calculation unit calculates the abnormality of the nighttime sleep quality of the elderly stroke patient at home in the next time window based on the nighttime sleep quality index, respiratory fluctuation change and turning fluctuation change of the elderly stroke patient at home in a single time window.

10. A monitoring system for home care of elderly stroke patients according to claim 9, characterized in that: The analysis and early warning module includes an analysis and early warning unit; The analysis and early warning unit: presets a threshold value of abnormal sleep quality at night based on the abnormal sleep quality at night of the elderly stroke patient at home in the next time window; If the abnormality of nighttime sleep quality is greater than the abnormality threshold of nighttime sleep quality, it is determined that the elderly stroke patient at home has abnormal sleep in the next time window, and a care warning is issued to the medical staff through the APP; The time window is iterated to obtain the abnormality of nighttime sleep quality in each time window in real time, and the elderly stroke patients at home are dynamically monitored.

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