Lying position change reminding method and device, medium and electronic equipment

Abstract

The invention provides a recumbent position change reminding method and device, a medium and electronic equipment, and relates to the field of medical information processing.The method comprises the steps that in response to the fact that a to-be-reminded target is detected to start executing a target item, a physical sign data list set ZS and a physical state type list ZT are obtained once every a preset time length; according to the physical sign data list set ZS, the physical state type list ZT and an HMM model, obtaining a predicted physical state type sequence in the target time window; and if the number of the predicted body state types included in the Y is equal to or greater than a first preset number threshold value, sending out a clinostatism conversion prompt at the current time. According to the method and the device, the accuracy of clinostatism conversion reminding is improved, and meanwhile, when a certain body index of the user suddenly rises and clinostatism conversion needs to be carried out, unnecessary damage is caused to the body of the user due to the fact that the user does not perceive that the user needs to carry out clinostatism conversion. And manual regular reminding is not needed, so that manpower is saved.

Description

Background Art

[0002] In the medical field, for some patients who need oxygen, oxygen inhalation in the prone position has been proven to have a good effect. However, there are many problems in the actual application process. Many patients will experience physical discomfort after maintaining a prone position for a period of time, such as significant changes in vital signs, such as a rapid rise in blood pressure, which has a negative impact on the patient's physical indicators and thus increases the risk of illness. At present, for such situations, the clinic usually adopts the measure of turning the patient over every two hours to alleviate the discomfort caused by long-term prone position. However, this method has obvious defects. Medical staff need to keep a close eye on the patient during the turning process. Due to limited human resources and the complexity of the actual nursing scene, it is difficult to ensure that the patient can be discovered and handled in time when the patient is in an uncomfortable state. This not only increases the workload of medical staff, but also may cause the patient to face greater health threats due to untimely treatment. Therefore, there is an urgent need for a technical solution that can effectively solve the above problems to improve the quality and safety of care for patients who need oxygen inhalation in the prone position, reduce the work pressure of medical staff and reduce the risk of patients. Summary of the invention

[0003] In response to the above technical problems, the present application provides a lying position change reminder method, device, medium and electronic device, which at least partially solve the problems existing in the prior art.

[0004] In a first aspect of the present application, a lying position change reminder method is provided, the method comprising:

[0005] S100, in response to detecting that the target to be reminded starts to execute the target item, a physical sign data list set ZS and a physical state type list ZT are obtained once every preset time length; wherein ZS=(ZS1, ZS2, ..., ZS i , ..., ZS n ); i = 1, 2, ..., n; n is the number of acquisition time points from the preset time length to the current time; the time interval between any two adjacent acquisition time points is the same; ZS i is a list of vital sign data of the target to be reminded collected at the i-th collection time point before the preset time length to the current time; ZS i =(ZS i，1 , ZS i，2 , ..., ZS i，j , ..., ZS i，m ); j = 1, 2, ..., m; m is the number of vital sign data corresponding to the target to be reminded; ZS i，j is the jth physical feature data of the target to be reminded collected at the i-th collection time point before the preset time length to the current time; ZT = (ZT1, ZT2, ..., ZT i, ..., ZT n );ZT i The physical state type corresponding to the i-th collection time point of the target to be reminded from the preset time length to the current time; the lying position corresponding to the target to be reminded when executing the target item is the target lying position;

[0006] S200, according to the physical sign data list set ZS, the physical state type list ZT and the HMM model, obtain the predicted physical state type sequence Y=(Y1, Y2, ..., Y x , ..., Y y ); x = 1, 2, ..., y; where y is the number of predicted body state types obtained within the target time window; Y x is the predicted physical state type of the target to be reminded corresponding to the xth target time point in the target time window; the time interval between any two adjacent target time points is the same; the start time of the target time window is the current time; where ZS is the observation sequence of the HMM model; and ZT is the hidden sequence of the HMM model;

[0007] S300: If the number of predicted body state types included in Y that are target hidden state types is equal to or greater than a first preset number threshold, a lying position change reminder is issued at the current time.

[0008] In a second aspect of the present application, a lying position change reminder device is provided, the device comprising:

[0009] The list acquisition unit is used to acquire a physical sign data list set ZS and a physical state type list ZT once at a preset time interval in response to detecting that the target to be reminded starts to execute the target item; wherein ZS=(ZS1, ZS2, ..., ZS i , ..., ZS n ); i = 1, 2, ..., n; n is the number of acquisition time points from the preset time length to the current time; the time interval between any two adjacent acquisition time points is the same; ZS i is a list of vital sign data of the target to be reminded collected at the i-th collection time point before the preset time length to the current time; ZS i =(ZS i，1 , ZS i，2 , ..., ZS i，j , ..., ZS i，m ); j = 1, 2, ..., m; m is the number of vital sign data corresponding to the target to be reminded; ZS i，j is the jth physical feature data of the target to be reminded collected at the i-th collection time point before the preset time length to the current time; ZT = (ZT1, ZT2, ..., ZT i , ..., ZTn );ZT i The physical state type corresponding to the i-th collection time point of the target to be reminded from the preset time length to the current time; the lying position corresponding to the target to be reminded when executing the target item is the target lying position;

[0010] The prediction unit is used to obtain a predicted body state type sequence Y=(Y1, Y2, ..., Y3) within a target time window according to the physical sign data list set ZS, the body state type list ZT and the HMM model. x , ..., Y y ); x = 1, 2, ..., y; where y is the number of predicted body state types obtained within the target time window; Y x is the predicted physical state type of the target to be reminded corresponding to the xth target time point in the target time window; the time interval between any two adjacent target time points is the same; the start time of the target time window is the current time; where ZS is the observation sequence of the HMM model; and ZT is the hidden sequence of the HMM model;

[0011] The conversion unit is used to issue a lying position conversion reminder at the current time if the number of predicted body state types included in Y that are target hidden state types is equal to or greater than a first preset number threshold.

[0012] In a third aspect of the present application, a non-transitory computer-readable storage medium is provided, in which at least one instruction or at least one program is stored, and the at least one instruction or at least one program is loaded and executed by a processor to implement the aforementioned lying position change reminder method.

[0013] In a fourth aspect of the present application, an electronic device is provided, comprising a processor and the above-mentioned non-transitory computer-readable storage medium.

[0014] This application has at least the following beneficial effects:

[0015] The present application provides a method for reminding patients of changes in supine position. When a patient who is breathing oxygen is in a prone position for a long time, various physical signs of the body will change significantly, causing physical discomfort to the patient. In order to be able to issue a reminder in time, the present application first starts with the patient breathing oxygen in a prone position, and obtains the physical signs of the patient who is breathing oxygen and the supine position at each preset time interval, and then obtains the physical condition of the patient who is breathing oxygen in a prone position according to the changes in the physical signs at different times. At the same time, the corresponding physical state type at each acquisition time point is obtained. The physical state type includes stable and abnormal, wherein stable means that the current state of the patient who is breathing oxygen in a prone position is still good and does not need to turn over; on the contrary, abnormal means that the current state of the patient who is breathing oxygen in a prone position is not good and needs to turn over. Furthermore, the physical sign data list set ZS is used as the observation sequence of the HMM model, and the physical state type list ZT is used as the hidden sequence of the HMM model. Through the known physical sign data and the physical state type corresponding to the physical sign data collected at different times, the HMM model is used to predict backward, and the hidden state sequence after the current time is predicted, that is, the physical state type sequence is predicted. However, since the training set used by the pre-trained HMM model in this embodiment is a set of several groups of vital sign data lists and lists of body state types collected before the oxygen inhalation to remind the user, the training samples are relatively small, and the accuracy of a single predicted state obtained by the pre-trained HMM model may be low. Therefore, in this application, if the number of predicted body state types contained in Y as target hidden state types is equal to or greater than the first preset number threshold, it means that the HMM model predicts that the body state type is abnormal for a large number of times in the future. When it is equal to or greater than the first preset number threshold, within the target time window, the patient is very likely to feel unwell, or the corresponding vital sign data indicates that the patient's condition is not good and needs to turn over to relieve it. Therefore, a supine position change reminder is issued at the current time. This application improves the accuracy of the supine position change reminder. At the same time, when a certain physical indicator of the user suddenly increases and requires supine position change, unnecessary damage is caused to the user's body because the user himself is not aware of it. And there is no need for manual timed reminders, which saves manpower. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 A flow chart of a lying position change reminder method provided in an embodiment of the present application;

[0018] Figure 2 This is a structural block diagram of the lying position change reminder device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0020] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0021] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present application, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0022] Please refer to Figure 1 As shown, an embodiment of the present application provides a lying position change reminder method, the method comprising:

[0023] Step S100, in response to detecting that the target to be reminded starts to execute the target item, a set of physical sign data list ZS and a list of physical condition types ZT are obtained once every preset time length; wherein ZS=(ZS1, ZS2, ..., ZS i , ..., ZS n ); i = 1, 2, ..., n; n is the number of acquisition time points from the preset time length to the current time; the time interval between any two adjacent acquisition time points is the same; ZSi is a list of vital sign data of the target to be reminded collected at the i-th collection time point before the preset time length to the current time; ZS i =(ZS i，1 , ZS i，2 , ..., ZS i，j , ..., ZS i，m ); j = 1, 2, ..., m; m is the number of vital sign data corresponding to the target to be reminded; ZS i，j is the jth physical feature data of the target to be reminded collected at the i-th collection time point before the preset time length to the current time; ZT = (ZT1, ZT2, ..., ZT i , ..., ZT n );ZT i It is the physical state type corresponding to the i-th collection time point of the target to be reminded before the preset time length to the current time; when executing the target item, the lying position corresponding to the target to be reminded is the target lying position.

[0024] Specifically, in this embodiment, the target to be reminded may be a patient who needs oxygen inhalation. The target to be reminded starts to execute the target item, that is, the patient who needs oxygen inhalation starts to convert to the target lying position for oxygen inhalation. In this embodiment, the target lying position is the prone position. The non-target lying position may be the supine position, the lateral position, etc. The preset time length may be 10 minutes. The time interval between any two adjacent acquisition time points may be 1 minute, that is, in this embodiment, within ten minutes from the time when the patient who needs oxygen inhalation starts to inhale oxygen in the prone position, the vital sign data of the patient who needs oxygen inhalation is collected once every one minute. The vital sign data here may be collected according to fixed medical equipment, wearable medical equipment, etc., including equipment such as bracelets. The vital sign data include blood pressure, heart rate, respiratory rate, etc. And the physical state type of the patient who is inhaling oxygen in the prone position is obtained once every one minute. Here, the acquisition method may be to collect information input by the patient who is inhaling oxygen in the prone position; wherein, the input method may be voice input, etc. In this embodiment, the body state type includes stable and abnormal, wherein stable means that the patient who is breathing oxygen in the prone position is in good condition and does not need to turn over; conversely, abnormal means that the patient who is breathing oxygen in the prone position is in poor condition and needs to turn over.

[0025] It should be noted that when in a prone position for a long time, many physical signs of the body will change significantly and make people uncomfortable. In terms of breathing, limited chest movement leads to a decrease in tidal volume, a compensatory increase in respiratory rate, and an imbalance in the ratio of pulmonary ventilation / blood flow that affects oxygenation; in circulation, obstruction of venous return causes abnormal blood pressure, and poor peripheral circulation causes swelling and numbness of the limbs; in the nervous system, compression of nerves causes radiating pain in the upper limbs, facial paresthesia, etc., and changes in brain blood supply lead to dizziness and headaches; the digestive system is affected, gastrointestinal motility is obstructed, leading to abdominal distension and indigestion, and an increased risk of esophageal reflux; in terms of musculoskeletal system, muscle fatigue and pain, and changes in joint pressure accelerate wear, all of which indicate that a long-term prone position will bring many adverse effects to the body. Therefore, this application obtains the physical signs related to the lying position, and then obtains the physical condition of the patient who is breathing oxygen in the prone position according to the changes in the physical signs at different times.

[0026] Step S200: According to the physical sign data list set ZS, the physical state type list ZT and the HMM model, a predicted physical state type sequence Y=(Y1, Y2, ..., Y x , ..., Y y ); x = 1, 2, ..., y; where y is the number of predicted body state types obtained within the target time window; Y x is the predicted physical state type of the target to be reminded corresponding to the x-th target time point in the target time window; the time interval between any two adjacent target time points is the same; the start time of the target time window is the current time; among them, ZS is the observation sequence of the HMM model; ZT is the hidden sequence of the HMM model.

[0027] Specifically, in this embodiment, the vital sign data list set ZS is used as the observation sequence of the HMM model, and the body state type list ZT is used as the hidden sequence of the HMM model. Through the known vital sign data and the body state types corresponding to the vital sign data collected at different times, the HMM model is used to perform backward prediction to predict the hidden state sequence after the current time, that is, to predict the body state type sequence.

[0028] It should be noted that the HMM model in this application is a pre-trained HMM model, and the corresponding training set is a set of several groups of vital sign data lists and physical state type lists collected by the user to be reminded before this oxygen inhalation. As an example, one of the training sets is the historical vital sign data list and the corresponding historical physical state type list set corresponding to the first historical time window as the expected input in the training set; the historical physical state type list corresponding to the second historical time window is the expected output in the training set; wherein the time length of the first historical time window is equal to the preset time length; the time length of the second historical time window is equal to the length of the target time window. In this way, the state transfer matrix, observation probability matrix and initial state probability vector corresponding to the HMM model are obtained. The end time of the first historical time window is the start time of the second historical time window, and the end time of the second historical time window is before this oxygen inhalation.

[0029] As an example: the target time window may be 30 minutes, and the time interval between any two adjacent target time points may be 1 minute.

[0030] Step S300: If the number of predicted body state types included in Y that are target hidden state types is equal to or greater than a first preset number threshold, a lying position change reminder is issued at the current time.

[0031] Specifically, since the training set used by the pre-trained HMM model in this embodiment is a set of several groups of vital sign data lists and body state type lists collected before the oxygen inhalation to remind the user, there are fewer training samples, and the accuracy of a single predicted state obtained by the pre-trained HMM model may be low. Therefore, in this embodiment, if the number of predicted body state types contained in Y that are target hidden state types is equal to or greater than the first preset number threshold, it means that the HMM model predicts that the body state type is abnormal a large number of times in the future. When it is equal to or greater than the first preset number threshold, then within the target time window, the patient is very likely to feel unwell, or the corresponding vital sign data indicates that the patient is in a bad condition and needs to turn over to relieve it. Therefore, a supine position change reminder is issued at the current time.

[0032] It should be noted that the lying position change reminder can be used to remind patients as well as doctors.

[0033] In this embodiment, when the patient who is breathing oxygen is in a prone position for a long time, various physical signs of the body will change significantly, causing the patient to feel uncomfortable. In order to issue a reminder in time, this embodiment first starts from the patient breathing oxygen in a prone position, and obtains the physical signs of the patient who is breathing oxygen in a prone position at each preset time interval, and then obtains the physical condition of the patient who is breathing oxygen in a prone position according to the changes in physical signs at different times. At the same time, the corresponding physical state type at each acquisition time point is obtained. The physical state type includes stable and abnormal, wherein stable means that the current state of the patient who is breathing oxygen in a prone position is good and does not need to turn over; on the contrary, abnormal means that the current state of the patient who is breathing oxygen in a prone position is not good and needs to turn over. Then, the physical sign data list set ZS is used as the observation sequence of the HMM model, and the physical state type list ZT is used as the hidden sequence of the HMM model. Through the known physical sign data and the physical state types corresponding to the physical sign data collected at different times, the HMM model is used for backward prediction to predict the hidden state sequence after the current time, that is, to predict the physical state type sequence. However, since the training set used by the pre-trained HMM model in this embodiment is a set of several groups of vital sign data lists and lists of body state types collected before the oxygen inhalation to remind the user, the training samples are relatively small, and the accuracy of a single predicted state obtained by the pre-trained HMM model may be low. Therefore, in this embodiment, if the number of predicted body state types contained in Y as target hidden state types is equal to or greater than the first preset number threshold, it means that the HMM model predicts that the body state type is abnormal for a large number of times in the future. When it is equal to or greater than the first preset number threshold, within the target time window, the patient is very likely to feel unwell, or the corresponding vital sign data indicates that the patient's condition is not good and needs to turn over to relieve it. Therefore, a lying position change reminder is issued at the current time. This embodiment improves the accuracy of the lying position change reminder. At the same time, when a certain physical indicator of the user suddenly increases and requires lying position change, unnecessary damage is caused to the user's body because the user himself is not aware of it. And there is no need for manual timed reminders, which saves manpower.

[0034] In an exemplary embodiment of the present application, after step S200, the method further includes:

[0035] S400, if the number of continuous predicted body state types contained in Y that are target hidden state types is equal to or greater than a second preset number threshold, then at the current time, the target to be reminded is reminded to switch from the target lying position to the non-target lying position; wherein the second preset number threshold is less than the first preset number threshold.

[0036] In this embodiment, if the number of continuous predicted physical state types contained in Y as the target hidden state type is equal to or greater than the second preset number threshold, it means that the HMM model predicts that the physical state type is abnormal continuously in the future. In the target time window, the patient is very likely to feel unwell, or the corresponding physical sign data shows that the patient is in a bad state and needs to turn over to relieve it. Therefore, a lying position change reminder is issued at the current time. In this embodiment, the second preset number threshold is less than the first preset number threshold, that is, if the HMM model predicts that the physical state type is abnormal for a continuous period of time, the probability that the patient feels unwell is relatively large. This embodiment improves the accuracy of the lying position change reminder. At the same time, when a certain physical indicator of the user suddenly increases and requires a lying position change, unnecessary damage is caused to the user's body because the user himself is not aware of it. And there is no need for manual timing reminders, which saves manpower.

[0037] In an exemplary embodiment of the present application, after step S300, the method further includes:

[0038] S500: If the target to be reminded has not switched to a supine position, obtain a key vital sign data list set G = (G1, G2, ..., G i , ..., G n ), where G i is a list of vital sign data of the target to be reminded collected at the i-th collection time point within the key time window; G i =(G i，1 , G i，2 , ..., G i，j , ..., G i，m );G i，j is the jth individual feature data of the target to be reminded collected at the i-th collection time point within the key time window; the end time of the key time window is the time when the target to be reminded is detected to start executing the target item.

[0039] Specifically, if the target to be reminded has not switched to a supine position, that is, the patient who is breathing oxygen in a prone position believes that he is in good physical condition and does not need to turn over after being reminded by the medical staff. At this time, the key vital sign data list set G is obtained. That is, the vital sign data of the patient before the target to be reminded starts to perform the target task is obtained. The time length of the key time window is equal to the preset time length.

[0040] S600, according to G and the first classification model, a first predicted lying position conversion time range is obtained; the first predicted lying position conversion time range is one of several preset lying position conversion time ranges; each preset lying position conversion time range has a corresponding weight.

[0041] Specifically, based on the vital sign data of the patient who is inhaling oxygen in the prone position before starting to inhale oxygen in the prone position, and the first classification model, a first predicted lying position conversion time range is obtained. Here, the first predicted lying position conversion time range represents the predicted time range that the user needs to convert from the target lying position to the non-target lying position. As an example: the first predicted lying position conversion time range can be 0.5-1 hour. And several preset lying position conversion time ranges can be 0.5-1 hour, 1-2 hours, 2-3 hours, etc.

[0042] It should be noted that the specific model selection of the first classification model can be selected by those skilled in the art according to the function of the first classification model, and no specific limitation is made at this time. The first classification model is a pre-trained classification model, and the corresponding training set is a list of lying position conversion times and vital sign data before the start of prone position oxygen inhalation for several patients who need prone position oxygen inhalation in history.

[0043] S700, obtaining a second predicted lying position conversion time range according to ZS and the second classification model; wherein the longest time length of the second predicted lying position conversion time range is shorter than the shortest time length of the first predicted lying position conversion time range.

[0044] S800: Obtain a target predicted lying position conversion time range H according to the weight corresponding to the first predicted lying position conversion time range and the second predicted lying position conversion time range, wherein H meets the following conditions:

[0045] H=[h min ,h max ];

[0046] Among them, h min is the shortest time length corresponding to H; h max is the longest time length corresponding to H; h min =e min ×α;h max =e max ×α;e min is the shortest time length corresponding to the second predicted lying position conversion time range E; max It is the longest time length corresponding to the second predicted lying position conversion time range E.

[0047] S900, based on H, issues a lying position change reminder.

[0048] Specifically, in this embodiment, first, according to the first predicted supine position conversion time range corresponding to the patient receiving oxygen inhalation in the prone position obtained by the first classification model, the second predicted supine position conversion time range is adjusted according to the weight corresponding to the first predicted supine position conversion time range. In order to obtain the final target predicted supine position conversion time range H. The lower the upper and lower limits corresponding to the first predicted supine position conversion time range, the higher the corresponding weight. As an example: if the first predicted supine position conversion time range can be 0.5-1, the corresponding weight is 1.3; if the first predicted supine position conversion time range can be 1-2, the corresponding weight is 1.1. Here, it is because the first predicted lying position conversion time range is the lying position conversion time range predicted based on the patient's physical sign data in the non-target lying position, that is, the non-prone position. The larger the corresponding upper and lower limits, that is, the longer the predicted time in the target lying position, then when reminding, the patient's need to turn over when reminding is greater than the corresponding predicted preset lying position conversion time range with a shorter time in the target lying position. Therefore, the lower the upper and lower limits corresponding to the first predicted lying position conversion time range, the greater the impact on the subsequent time of continuing to inhale oxygen in the prone position, and therefore, the greater the corresponding weight. Then adjust the upper and lower limits of the second predicted lying position conversion time range according to the weight. Make the obtained time of continuing to inhale oxygen in the prone position more in line with the actual physical condition of the user's body.

[0049] In an exemplary embodiment of the present application, after step S500, the method further includes:

[0050] S900, obtain key target data list M=(M1, M2, . . . , M g , ..., M h ); g = 1, 2, ..., h; where h is the number of key target data; M g is the g-th target data corresponding to the target to be reminded; the target data is used to describe the basic physiological information of the target to be reminded.

[0051] S1000, obtaining a first predicted lying position conversion time range according to G, M and the third classification model; and jumping to step S800.

[0052] Specifically, in this embodiment, the first predicted lying position conversion time range is obtained based on the key target data list M and the key vital sign data list set. The key target data in this embodiment may include the height, weight, gender, disease type, etc. of the target to be reminded. It is characteristic data that describes the basic physiological information of the reminder target. In this embodiment, the corresponding third classification model is set according to the personalized information of the patient, and the key target data list M and the key vital sign data list set are used as input to obtain the first predicted lying position conversion time range. Compared with the previous embodiment, the obtained first predicted lying position conversion time range is more accurate and more targeted.

[0053] In an exemplary embodiment of the present application, after step S300, the method further includes:

[0054] S1100, if the number of predicted body state types contained in Y that are target hidden state types is less than a preset number threshold, jump to "obtain a vital sign data list set ZS and a body state type list ZT once every preset time length."

[0055] Specifically, in this embodiment, if the number of predicted body state types included in Y that are target hidden state types is less than the preset number threshold, it means that the HMM model predicts that the body state types are mostly stable in the future period after the current time. In the target time window, the patient is less likely to feel unwell or have abnormal indicators, and does not need to turn over to relieve it temporarily. Therefore, continue to execute "obtain the vital sign data list set ZS and the body state type list ZT once every preset time length" to continue to monitor the reminder target.

[0056] Please refer to Figure 2 As shown, an embodiment of the present application provides a lying position change reminder device 100, the device comprising:

[0057] The list acquisition unit 110 is used to acquire a physical sign data list set ZS and a physical state type list ZT once at a preset time interval in response to detecting that the target to be reminded starts to execute the target item; wherein ZS=(ZS1, ZS2, ..., ZS i , ..., ZS n ); i = 1, 2, ..., n; n is the number of acquisition time points from the preset time length to the current time; the time interval between any two adjacent acquisition time points is the same; ZS i is a list of vital sign data of the target to be reminded collected at the i-th collection time point before the preset time length to the current time; ZS i =(ZS i，1 , ZS i，2 , ..., ZS i，j, ..., ZS i，m ); j = 1, 2, ..., m; m is the number of vital sign data corresponding to the target to be reminded; ZS i，j is the jth physical feature data of the target to be reminded collected at the i-th collection time point before the preset time length to the current time; ZT = (ZT1, ZT2, ..., ZT i , ..., ZT n );ZT i It is the physical state type corresponding to the i-th collection time point of the target to be reminded before the preset time length to the current time; when executing the target item, the lying position corresponding to the target to be reminded is the target lying position.

[0058] The prediction unit 120 is used to obtain a predicted body state type sequence Y=(Y1, Y2, ..., Y x , ..., Y y ); x = 1, 2, ..., y; where y is the number of predicted body state types obtained within the target time window; Y x is the predicted physical state type of the target to be reminded corresponding to the x-th target time point in the target time window; the time interval between any two adjacent target time points is the same; the start time of the target time window is the current time; among them, ZS is the observation sequence of the HMM model; ZT is the hidden sequence of the HMM model.

[0059] The conversion unit 130 is configured to issue a lying position conversion reminder at the current time if the number of predicted body state types included in Y that are target hidden state types is equal to or greater than a first preset number threshold.

[0060] An embodiment of the present application further provides a computer program product, which includes program code. When the program product is run on an electronic device, the program code is used to enable the electronic device to execute the steps of the method according to various exemplary embodiments of the present application described above in this specification.

[0061] In addition, although the steps of the method in the present application are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0062] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the implementation methods of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the implementation methods of the present application.

[0063] In an exemplary embodiment of the present application, an electronic device capable of implementing the above method is also provided.

[0064] Those skilled in the art will appreciate that various aspects of the present application may be implemented as a system, method or program product. Therefore, various aspects of the present application may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to as "circuit", "module" or "system" herein.

[0065] The electronic device according to this embodiment of the present application is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0066] The electronic device is presented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: the at least one processor mentioned above, the at least one storage device mentioned above, and a bus connecting different system components (including storage devices and processors).

[0067] The storage stores program codes, which can be executed by the processor, so that the processor executes the steps described in the above “Exemplary Method” section of this specification according to various exemplary embodiments of the present application.

[0068] The memory may include readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read only memory (ROM).

[0069] The storage may also include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include the implementation of a network environment.

[0070] The bus may represent one or more of several types of bus structures including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.

[0071] The electronic device can also communicate with one or more external devices (such as keyboards, pointing devices, Bluetooth devices, etc.), and can also communicate with one or more devices that enable users to interact with the electronic device, and / or communicate with any device (such as routers, modems, etc.) that enables the electronic device to communicate with one or more other computing devices. This communication can be carried out through an input / output (I / O) interface. In addition, the electronic device can also communicate with one or more networks (such as local area networks (LANs), wide area networks (WANs) and / or public networks, such as the Internet) through a network adapter. As shown in the figure, the network adapter communicates with other modules of the electronic device through a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0072] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the implementation methods of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation methods of the present application.

[0073] In an exemplary embodiment of the present application, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present application can also be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary implementations of the present application described in the above "Exemplary Method" section of the present specification.

[0074] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0075] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0076] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0077] Program code for performing the operations of the present application may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0078] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present application, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0079] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0080] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for reminding a person of a change in lying position, characterized in that: The method comprises: S100, in response to detecting that the target to be reminded starts to perform the target event, every time a preset time length is passed, a physical sign data list set ZS and a physical condition type list ZT are obtained; wherein ZS=(ZS1, ZS2, ..., ZS i ,...,ZS n ); i = 1, 2, ..., n; n is the number of acquisition time points from the preset time length to the current time; the time interval between any two adjacent acquisition time points is the same; ZS i is a list of vital sign data of the target to be reminded collected from the preset time length to the i-th collection time point in the current time; ZS i =(ZS i，1 , ZS i，2 ,...,ZS i，j ,...,ZS i，m ); j = 1, 2, ..., m; m is the number of vital sign data corresponding to the target to be reminded; ZS i，j is the jth physical feature data of the target to be reminded collected at the i-th collection time point before the preset time length to the current time; ZT=(ZT1, ZT2, ..., ZT i ,...,ZT n );ZT i The physical state type corresponding to the i-th collection time point of the target to be reminded from the preset time length to the current time; the lying position corresponding to the target to be reminded when executing the target item is the target lying position; S200, according to the physical sign data list set ZS, the physical state type list ZT and the HMM model, obtain the predicted physical state type sequence Y=(Y1, Y2, ..., Y x ,...,Y y ); x = 1, 2, ..., y; where y is the number of predicted body state types obtained within the target time window; Y x is the predicted physical state type of the target to be reminded corresponding to the xth target time point in the target time window; the time interval between any two adjacent target time points is the same; the start time of the target time window is the current time; where ZS is the observation sequence of the HMM model; ZT is the hidden sequence of the HMM model; S300: If the number of predicted body state types included in Y that are target hidden state types is equal to or greater than a first preset number threshold, a lying position change reminder is issued at the current time.

2. The lying position change reminder method according to claim 1, characterized in that: After step S200, the method further includes: S400, if the number of continuous predicted body state types contained in Y that are target hidden state types is equal to or greater than a second preset number threshold, then at the current time, the target to be reminded is reminded to switch from the target lying position to the non-target lying position; wherein the second preset number threshold is less than the first preset number threshold.

3. The lying position change reminder method according to claim 1, characterized in that: After step S300, the method further includes: S500: If the target to be reminded has not changed its supine position, obtain a key vital sign data list set G = (G1, G2, ..., G i ,...,G n ); where G i is a list of vital sign data of the target to be reminded collected at the i-th collection time point within the key time window; G i =(G i，1 , G i，2 ,...,G i，j ,...,G i，m );G i，j is the jth individual feature data of the target to be reminded collected at the i-th collection time point within the key time window; the end time of the key time window is the time when the target to be reminded is detected to start executing the target task; S600: Obtain a first predicted lying position transition time range based on G and the first classification model; the first predicted lying position transition time range is one of a plurality of preset lying position transition time ranges; each preset lying position transition time range has a corresponding weight; S700: Obtain a second predicted lying position transition time range based on the ZS and the second classification model; wherein a maximum time length of the second predicted lying position transition time range is less than a shortest time length of the first predicted lying position transition time range; S800: Obtain a target predicted lying position transition time range H based on the weight corresponding to the first predicted lying position transition time range and the second predicted lying position transition time range, where H meets the following conditions: H=[h min ,h max ]; Among them, h min is the shortest time length corresponding to H; h max is the maximum time length corresponding to H; h min =e min ×α;h max =e max ×α;e min is the shortest time length corresponding to the second predicted lying position conversion time range E; max is the maximum time length corresponding to the second predicted lying position conversion time range E; S900, based on H, issues a lying position change reminder.

4. The lying position change reminder method according to claim 3, characterized in that: After step S500, the method further includes: S900, obtain key target data list M=(M1, M2, ..., M g ,...,M h ); g = 1, 2, ..., h; where h is the number of key target data; M g The g-th target data corresponding to the target to be reminded; the target data is used to describe the basic physiological information of the target to be reminded; S1000, obtaining a first predicted lying position conversion time range based on G, M and the third classification model; and jumping to step S800.

5. The lying position change reminder method according to claim 1, characterized in that: After step S300, the method further includes: S1100: If the number of predicted body state types included in Y that are target hidden state types is less than a preset number threshold, jump to "obtaining a vital sign data list set ZS and a body state type list ZT once every preset time length." 6. The lying position change reminder method according to claim 3, characterized in that: The time length of the critical time window is equal to the preset time length.

7. A lying position change reminder device, characterized in that: The device comprises: The list acquisition unit is configured to acquire a physical sign data list set ZS and a physical condition type list ZT once every preset time interval in response to detecting that the target to be reminded starts to execute the target item; wherein ZS=(ZS1, ZS2, ..., ZS i ,...,ZS n ); i = 1, 2, ..., n; n is the number of acquisition time points from the preset time length to the current time; the time interval between any two adjacent acquisition time points is the same; ZS i is a list of vital sign data of the target to be reminded collected from the preset time length to the i-th collection time point in the current time; ZS i =(ZS i，1 , ZS i，2 ,...,ZS i，j ,...,ZS i，m ); j = 1, 2, ..., m; m is the number of vital sign data corresponding to the target to be reminded; ZS i，j is the jth physical feature data of the target to be reminded collected at the i-th collection time point before the preset time length to the current time; ZT=(ZT1, ZT2, ..., ZT i ,...,ZT n );ZT i The physical state type corresponding to the i-th collection time point of the target to be reminded from the preset time length to the current time; the lying position corresponding to the target to be reminded when executing the target item is the target lying position; The prediction unit is used to obtain the predicted body state type sequence Y=(Y1, Y2, ..., Y x ,...,Y y ); x = 1, 2, ..., y; where y is the number of predicted body state types obtained within the target time window; Y x is the predicted physical state type of the target to be reminded corresponding to the xth target time point in the target time window; the time interval between any two adjacent target time points is the same; the start time of the target time window is the current time; where ZS is the observation sequence of the HMM model; ZT is the hidden sequence of the HMM model; The conversion unit is configured to issue a lying position conversion reminder at the current time if the number of predicted body state types included in Y that are target hidden state types is equal to or greater than a first preset number threshold.

8. A non-transitory computer-readable storage medium, characterized in that The storage medium stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the method according to any one of claims 1 to 6.

9. An electronic device, characterized in that: The device comprises a processor and the non-transitory computer-readable storage medium as claimed in claim 8.

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