Method for early warning of exercise rehabilitation risk of chronic disease patients based on data analysis

Abstract

The present invention relates to the technical field of sports rehabilitation, and specifically, to a method for early warning of sports rehabilitation risks for chronic disease patients based on data analysis. It includes the following steps: S1. Preset the amount of exercise required for the patient to perform rehabilitation exercises in the smart wearable device; S2. Collect the exercise required for daily life generated by the patient before performing rehabilitation exercises; S3. Calculate the correlation between the exercise required for daily life and the rehabilitation exercise, and calculate the influence state of the exercise required for daily life on the rehabilitation exercise according to the correlation. In this method for early warning of sports rehabilitation risks for chronic disease patients based on data analysis, a smart wearable device is used to obtain the patient's living habits before the rehabilitation exercise, analyze the exercise of the patient other than the rehabilitation exercise from the living habits, and then use this exercise as a part of the rehabilitation exercise to achieve dynamic adjustment of the rehabilitation exercise.

Description

Technical Field

[0001] The present invention relates to the technical field of sports rehabilitation, and more specifically, to a method for early warning of sports rehabilitation risks for chronic disease patients based on data analysis. Background Art

[0002] The importance of sports rehabilitation has been increasingly emphasized by medical practitioners in China. Currently, home-based sports rehabilitation has gradually become a new trend in sports rehabilitation. Compared with traditional center-based rehabilitation, home-based rehabilitation offers more flexible time, less space requirement, and lower overall medical costs.

[0003] During the home-based sports rehabilitation process, since patients cannot communicate face-to-face with doctors, they usually carry out sports rehabilitation training according to the exercise indicators formulated in advance by doctors. The amount of exercise in these exercise indicators increases gradually according to the stages of sports rehabilitation treatment. During this process, to avoid unexpected risks, smart wearable devices are equipped for patients to monitor their vital sign data during exercise. For example, the Chinese patent with the publication number CN119673440A discloses the use of smart wearable devices to analyze data such as patients' heart rate, respiratory rate, and body temperature. When these data are abnormal, early warning reminders are sent to patients in a timely manner to prevent patients from continuing to exercise. Although this method reduces the occurrence of unexpected risks, there are still the following deficiencies when facing home-based sports rehabilitation:

[0004] During the home period, in addition to sports rehabilitation, patients are also prone to having other exercise needs in daily life, such as going up and down stairs, cooking, cleaning, etc. Some of the exercise amounts in these exercise needs are likely to be superimposed on the exercise amount of sports rehabilitation, thus easily causing the phenomenon of over-exercise for patients.

[0005] Therefore, how to dynamically adjust the exercise indicators formulated in advance by doctors according to the exercise needs of patients during the home period is an important problem faced by current home-based sports rehabilitation. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for early warning of sports rehabilitation risks for chronic disease patients based on data analysis, which analyzes the living habits of patients and gives timely warnings to patients according to the living habits, so as to solve the problem raised in the above background art, that is, some of the exercise amounts are likely to be superimposed on the exercise amount of sports rehabilitation, thus easily causing the phenomenon of over-exercise for patients,

[0007] To achieve the above purpose, the method for early warning of sports rehabilitation risks for chronic disease patients based on data analysis includes the following steps:

[0008] S1. Preset the amount of exercise required for the patient to perform rehabilitation exercise in the smart wearable device ;

[0009] S2. Collect the home-based movements required by the patient before performing rehabilitation exercises.

[0010] S3. Calculate the correlation between the home-based movements required and the rehabilitation exercises, and calculate the impact status of the home-based movements required on the rehabilitation exercises based on the correlation.

[0011] S4. When the rehabilitation exercises are affected by the home-based movements required, generate a warning progress based on the proportion relationship between the home-based movements required and the rehabilitation exercises.

[0012] S5. When the patient reaches the warning progress during the rehabilitation exercises, give a warning reminder to the patient.

[0013] In the above technical solution, when it is predicted that the patient will generate home-based movements required, by combining the home-based movements required to reduce the duration of the rehabilitation exercises, a timely reminder is given to the patient.

[0014] On this basis, the steps of presetting the exercise amount in S1 are as follows: are as follows:

[0015] S1.1. Collect the disease information of the patient and obtain the type of rehabilitation exercise corresponding to the disease information. ;

[0016] S1.2. Analyze the rehabilitation exercise stage of the patient and establish an exercise amount for the patient according to the rehabilitation exercise stage. ;

[0017] S1.3. Store the exercise amount in the smart wearable device.

[0018] On this basis, the steps of collecting the home-based movements required in S2 are as follows:

[0019] S2.1. Set a home observation period.

[0020] S2.2. Collect the behavior information of the patient within the observation period through the smart wearable device, and mark the behavior information that generates movement as the home-based movements required.

[0021] On this basis, the steps of calculating the correlation in S3 are as follows:

[0022] S3.1. Obtain the home movement type of the home-based movements required. ;

[0023] S3.2. Set a correlation threshold , when the similarity between the home movement type and the rehabilitation exercise type is ≥ the correlation threshold When there is an association between the required home exercise and the rehabilitation exercise; when the similarity between the home exercise type and the rehabilitation exercise type is less than the correlation threshold it indicates that there is no association between the required home exercise and the rehabilitation exercise.

[0024] On this basis, the steps for calculating the impact status in S3 are as follows:

[0025] S3.3. Obtain the start time , end time and exercise duration of the required home exercise;

[0026] S3.4. Preset an impact time before the start time and after the end time to obtain an impact time period ;

[0027] S3.5. Obtain the exercise amount of the patient's rehabilitation exercise . When the exercise amount coincides or partially coincides with the impact time period , mark the patient's current rehabilitation exercise as affected; when the exercise amount does not coincide with the impact time period , mark the patient's current rehabilitation exercise as unaffected.

[0028] On this basis, the formula for generating the warning progress in S4 is as follows:

[0029] ;

[0030] In the formula, is the warning progress; is the total progress of the rehabilitation exercise; is the exercise duration of the required home exercise; is the exercise amount of the rehabilitation exercise.

[0031] In another technical solution, the following steps are further included:

[0032] S6. Obtain the subsequent status of the patient after receiving the warning reminder, and the subsequent status includes stopping exercise and continuing exercise;

[0033] S7. Set a judgment threshold. When the number of times of stopping exercise ≥ the judgment threshold and the patient chooses to continue exercise subsequently, change the rehabilitation exercise to the next stage; when the number of times of stopping exercise < the judgment threshold, do nothing.

[0034] In this technical solution, since the sports required for daily life are superimposed on the rehabilitation exercises, by obtaining the patient's status after receiving the warning reminder, on the one hand, it is determined whether the patient is exercising according to the warning reminder; on the other hand, the number of executions by the patient can be used as a basis to identify the patient's physical recovery, so as to dynamically adjust the rehabilitation exercise stage.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0036] 1. In the method for warning of the risk of exercise rehabilitation of chronic disease patients based on data analysis, an intelligent wearable device is used to obtain the patient's living habits before the rehabilitation exercise, analyze the exercise of the patient other than the rehabilitation exercise from the living habits, and then use this exercise as a part of the rehabilitation exercise to achieve dynamic adjustment of the rehabilitation exercise, reducing the phenomenon of excessive exercise of the patient during home stay.

[0037] 2. In the method for warning of the risk of exercise rehabilitation of chronic disease patients based on data analysis, obtaining the sports required for the patient's home can not only avoid the phenomenon of excessive exercise of the patient, but also explore the patient's physical recovery status during the rehabilitation exercise, so as to achieve dynamic adjustment of the rehabilitation exercise stage. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic diagram of the overall method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0040] In view of the phenomenon that home patients are prone to excessive exercise, the present invention provides a method for warning of the risk of exercise rehabilitation of chronic disease patients based on data analysis. Please refer to Figure 1 As shown, the warning method includes the following steps:

[0041] S1. Preset the amount of exercise required for the patient to perform rehabilitation exercises in an intelligent wearable device (such as a smart watch that can monitor data such as the patient's heart rate, respiratory rate, and body temperature) ;

[0042] S2. Collect the sports required for daily life generated by the patient before performing the rehabilitation exercise;

[0043] S3. Calculate the correlation between the sports required for daily life and the rehabilitation exercise, and calculate the influence state of the sports required for daily life on the rehabilitation exercise based on the correlation;

[0044] S4. When the rehabilitation exercise is affected by the exercise required at home, generate a warning progress according to the proportion relationship between the exercise required at home and the rehabilitation exercise;

[0045] S5. When the patient reaches the warning progress during the rehabilitation exercise, give a warning reminder to the patient.

[0046] Specifically, the preset exercise volume in S1 is as follows:

[0047] S1.1. Collect the disease information of the patient and obtain the type of rehabilitation exercise corresponding to the disease information ;

[0048] S1.2. Analyze the rehabilitation exercise stage that the patient is in, and establish an exercise volume for the patient according to the rehabilitation exercise stage ;

[0049] S1.3. Store the exercise volume in the smart wearable device.

[0050] Among them, the exercise volume in S1.2 is calculated in units of time. For example, when the patient's chronic disease is in the leg or hand, the type of rehabilitation exercise established by the doctor at this time is leg movement or hand movement. Then analyze the rehabilitation exercise stage required by the patient. Suppose it takes three stages to complete the treatment, and the training time for each stage is 15 days. The exercise volume in this process increases gradually according to the stage order. For example, the exercise volume [[ID= of the patient in the first stage is 20 minutes, the exercise volume of the second stage is 30 minutes, and the exercise volume of the third stage is 60 minutes. Then store the exercise volumes of these three stages in the smart wearable device.

[0051] Currently, the conventional warning method is basically to give a warning reminder to the patient after the patient reaches the corresponding exercise volume . For example, the warning time for the patient corresponding to the first stage is 20 minutes after the rehabilitation exercise. This can avoid the phenomenon of the patient overexercising. However, due to the particularity of staying at home, some patients are also prone to generate other exercises besides the rehabilitation exercise, such as going up and down stairs or cooking, etc., and it is difficult for doctors to know the exercises required by these patients at home. Therefore, suppose the patient has the behavior of going up and down stairs in a short time after finishing the rehabilitation exercise of the leg. At this time, the patient's leg is prone to the phenomenon of overexercising, thus affecting the rehabilitation progress.

[0052] To this end, during the process of the patient staying at home, the present invention analyzes the patient's living habits and gives timely warnings to the patient according to the living habits, so as to reduce the phenomenon of excessive exercise in the above situation. Specifically, it is carried out through the following steps in S2:

[0053] The steps of collecting the exercise required for staying at home in S2 are as follows:

[0054] S2.1. Set the home observation period. The observation period is the period from when the patient stays at home to when they start rehabilitation exercise. Therefore, during the observation period, the patient does not perform rehabilitation exercise to collect the patient's living habits. For example, the observation period is 3 days. At this time, after the patient returns home from the hospital, they can wait for 3 days to start rehabilitation exercise. During these 3 days, the patient's living habits are collected;

[0055] S2.2. Collect the patient's behavior information during the observation period through a smart wearable device, and mark the behavior information that generates exercise as the exercise required for staying at home. For example, during the 3-day observation period, the patient performs behaviors such as going up and down stairs (by stairs), taking a walk, and cooking. Since going up and down stairs, taking a walk, and cooking all generate exercise, going up and down stairs, taking a walk, and cooking are marked as the exercise required for staying at home.

[0056] On this basis, the steps of calculating the relevance and influence status in S3 are as follows:

[0057] S3.1. Obtain the home exercise type of the exercise required for staying at home , the home exercise type is mainly identified through a smart wearable device. For example, the GPS data, Beidou data, etc. in the smart wearable device are used to obtain the patient's moving speed, and then the patient's heart rate is measured. When the moving speed and heart rate exceed the threshold, it is marked as running (belonging to leg exercise). Another example is that when the patient is moving, if the altitude of the patient's location shows a regular increase, it is marked as climbing stairs (belonging to leg exercise). Another example is that it can also be assisted by calling the microphone of the smart wearable device. When the patient's hand generates movement, at this time, the microphone is used to monitor the surrounding environment sound. When the sound of running water or the sound of a range hood is captured, it is marked as cooking (belonging to hand exercise).

[0058] S3.2. Set the correlation threshold , when the similarity between the home exercise type and the rehabilitation exercise type ≥ the correlation threshold , it indicates that there is an association between the exercise required for staying at home and the rehabilitation exercise; when the similarity between the home exercise type and the rehabilitation exercise type < the correlation threshold When it indicates that there is no association between the home-based required exercise and the rehabilitation exercise.

[0059] For example, assume that the patient goes out for a walk every day and needs to go up and down stairs during the process of going out. At this time, the type of home-based exercise of the patient is climbing stairs, and the corresponding body part for the exercise is the legs. At this time, if the type of rehabilitation exercise is squatting and standing up or jogging, then the corresponding body part for the exercise is also the legs. At this time, it can be found that the type of home-based exercise and the type of rehabilitation exercise are related, and both belong to leg exercises. At the same time, since the two cannot be completely similar, therefore, by setting a threshold to make a judgment. Assume that the threshold is 70%. At this time, as long as the similarity between the type of home-based exercise and the type of rehabilitation exercise is greater than or equal to 70%, it can be determined that there is an association between the two.

[0060] S3.3. Obtain the start time of the home-based required exercise , the end time and the exercise duration ;

[0061] S3.4. Preset an influence time before the start time and after the end time to obtain an influence time period ;

[0062] S3.5. Obtain the exercise amount of the patient's rehabilitation exercise . When the exercise amount coincides or partially coincides with the influence time period , mark the patient's current rehabilitation exercise as affected; when the exercise amount does not coincide with the influence time period , mark the patient's current rehabilitation exercise as not affected.

[0063] For example, the patient goes out every day and needs to go up and down stairs during the process of going out. Therefore, going up and down stairs is the home-based required exercise of the patient. Assume that the patient goes out at 6 pm (i.e., the start time ), and returns home at 7 pm (i.e., the end time ). The time for going up and down stairs during this process is 5 minutes (i.e., the exercise duration ). Then, preset an influence time. Assume that the influence time is 20 minutes. At this time, it can also indicate that the influence time period of the home-based required exercise is between 5:40 pm and 7:20 pm. Then, assume the exercise amount of the patient's rehabilitation exercise is 20 minutes. Then, obtain the time when the patient clicks to start the rehabilitation exercise, which is assumed to be 5:25. It can be calculated that the rehabilitation exercise should end at 5:45. Therefore, the amount of exercise will overlap with the impact time period for 5 minutes at this time, and the rehabilitation exercise can be marked as affected.

[0064] In the above example, going out and coming home are both identified through the positioning system of the smart wearable device.

[0065] Moreover, since the time when the patient goes out every day cannot be exactly the same, a time period can also be predicted based on the time when the patient goes out within the observation period. For example, the patient goes out at 6 pm and comes home at 7 pm on the first day; goes out at 6:05 pm and comes home at 7 pm on the second day; goes out at 6:10 pm and comes home at 6:50 pm on the third day. At this time, based on the earliest going out time and the latest coming home time, that is, the start time is 6 pm, and the end time is 7 pm.

[0066] Specifically, the formula for generating the warning progress in S4 is as follows:

[0067] ;

[0068] In the formula, is the warning progress; is the total progress of the rehabilitation exercise; is the exercise duration required for home exercise; is the amount of exercise of the rehabilitation exercise. The total progress of the rehabilitation exercise is 100%. Assuming the exercise duration is 5 minutes and the amount of exercise is 20 minutes, it can be calculated that the warning progress is 75%. In this way, when the patient clicks to start the rehabilitation exercise on the smart wearable device, the progress of the rehabilitation exercise starts to be calculated from 1%. When the progress reaches 75%, the smart wearable device issues a warning reminder to alert the patient. The remaining 25% of the progress is compensated by the exercise required for home exercise. That is, after the patient goes out at 6 pm, the remaining 25% of the progress is compensated by the leg exercise when going up and down the stairs.

[0069] That is to say, use the smart wearable device to obtain the patient's living habits before the rehabilitation exercise, analyze the exercise of the patient other than the rehabilitation exercise from the living habits, and then use this exercise as part of the rehabilitation exercise to achieve dynamic adjustment of the rehabilitation exercise and reduce the phenomenon of excessive exercise caused by too much exercise during the patient's home stay.

[0070] It should be understood that any aspects of the present invention involving privacy will be informed to the user, and relevant data will only be collected after the user agrees to authorize the privacy.

[0071] Moreover, in some embodiments, the warning method further includes the following steps:

[0072] S6. Obtain the subsequent status of the patient after receiving the warning reminder, where the subsequent status includes stopping exercise and continuing exercise;

[0073] S7. Set a determination threshold. When the number of times of stopping exercise ≥ the determination threshold and the patient chooses to continue exercise subsequently, change the rehabilitation exercise of the patient to the next stage; when the number of times of stopping exercise < the determination threshold, do nothing.

[0074] For example, during the 7-day rehabilitation exercise of a patient, the patient received 5 warning reminders and stopped exercising after receiving these 5 warning reminders. Assuming the judgment threshold is 3 times, this indicates that the patient's body is difficult to bear both the rehabilitation exercise and the exercise required for daily life at the same time. However, in the subsequent process, when the patient receives a warning reminder again but chooses to continue exercising, this indicates that the patient can bear both the rehabilitation exercise and the exercise required for daily life at the same time. That is to say, the patient's physical condition has recovered well, and at this time, the amount of exercise can be increased to the next stage, realizing the dynamic adjustment of the amount of exercise according to the patient's own status. .

[0075] In summary, obtaining the exercise required for the patient's daily life can not only prevent the patient from overexercising, but also explore the patient's physical recovery status during the rehabilitation exercise, thereby realizing the dynamic adjustment of the rehabilitation exercise stage.

[0076] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification are only preferred examples of the present invention and do not limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A data analysis-based early warning method for exercise rehabilitation risks in patients with chronic diseases, characterized by: The steps include: S1. Preset the amount of exercise required for the patient's rehabilitation exercise in the smart wearable device ; S2. Collect the patient's home exercise requirements before rehabilitation exercises; S3. Calculate the correlation between the required home exercise and the rehabilitation exercise, and calculate the impact of the required home exercise on the rehabilitation exercise based on the correlation; S4. When rehabilitation exercise is affected by required home exercise, an early warning progress is generated based on the ratio between required home exercise and rehabilitation exercise; S5. When the patient reaches the warning progress during the rehabilitation exercise process, the patient will be given a warning reminder; Wherein, the S3 includes the following steps: S3.

1. Obtain the types of home exercise required at home ; S3.

2. Setting the correlation threshold , when home exercise type and rehabilitation exercise types Similarity between ≥ correlation threshold When the type of home exercise is and rehabilitation exercise types Similarity between them < correlation threshold When , it shows that there is no association between the required exercise at home and rehabilitation exercise; S3.

3. Obtain the start time of the required exercise at home , end time and exercise duration ; S3.

4. At the start time Before and end time Then preset the impact time and get the impact time period ; S3.

5. Obtaining the amount of exercise the patient undergoes during rehabilitation , when the amount of exercise and impact time period When overlap or partial overlap occurs, the patient's rehabilitation exercise is marked as affected; when the amount of exercise Not affected by the time period When overlap occurs, the patient's rehabilitation exercise is marked as unaffected; The formula for generating the early warning progress in S4 is as follows: ； Where, To provide early warning progress; The overall progress of rehabilitation exercises; The duration of exercise required for home use; The amount of exercise for rehabilitation.

2. The method for early warning of exercise rehabilitation risks for patients with chronic diseases based on data analysis according to claim 1, characterized in that: The preset amount of exercise in S1 The steps are as follows: S1.

1. Collect the patient's disease information and obtain the rehabilitation exercise type corresponding to the disease information ; S1.

2. Analyze the patient's rehabilitation exercise stage and establish an exercise volume for the patient based on the rehabilitation exercise stage ; S1.3, the amount of exercise Stored in smart wearable devices.

3. The method for early warning of exercise rehabilitation risks for patients with chronic diseases based on data analysis according to claim 2, characterized in that: The amount of exercise Calculations are performed using time units.

4. The method for early warning of exercise rehabilitation risks for patients with chronic diseases based on data analysis according to claim 1, characterized in that: The steps of collecting the required home exercise in S2 are as follows: S2.

1. Set up a home observation period; S2.

2. Collect the patient's behavioral information during the observation period through smart wearable devices, and mark the behavioral information that generates movement as required exercise at home.

5. The method for early warning of exercise rehabilitation risks for patients with chronic diseases based on data analysis according to claim 4, characterized in that: During the observation period, the patient was not performing any rehabilitation exercise.

6. The method for early warning of exercise rehabilitation risks for patients with chronic diseases based on data analysis according to claim 1, characterized in that: The following steps are also included: S6. Obtaining the patient's subsequent status after receiving the warning reminder, the subsequent status including stopping exercise and continuing exercise; S7. Set a judgment threshold. When the number of exercise stops is greater than or equal to the judgment threshold, and the patient chooses to continue exercising later, the rehabilitation exercise is changed to the next stage. When the number of exercise stops is less than the judgment threshold, no action is taken.

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