Ankle pump motion monitoring data processing method and system

Abstract

The invention relates to the technical field of ankle pump motion monitoring, and discloses an ankle pump motion monitoring data processing method and system, and the system comprises a patient data collection module, an ankle pump motion data detection module, a physiological signal collection module, an ankle pump motion analysis module, and a motion data evaluation module. The patient data acquisition module acquires age and physiological data actively uploaded by a patient, and constructs a physiological database. A motion database is constructed by obtaining motion data and interference data of a patient in the ankle pump motion process, meanwhile, physiological signals of the patient in the ankle pump motion process are obtained, and a physiological signal database is constructed; the exercise integrity coefficient and the exercise interference coefficient are combined with the physiological data weight and the physiological signal weight to comprehensively calculate the ankle pump exercise coefficient, and the exercise intensity adjustment suggestion of the patient is given based on the ankle pump exercise coefficient, so that the intensity of the patient during the next training can be effectively determined according to the omnibearing information of the patient in the training process; and a progressive growth type training process is completed.

Description

Technical Field

[0001] The present invention relates to the technical field of ankle pump motion monitoring, and in particular to a method and system for processing ankle pump motion monitoring data. Background Art

[0002] Ankle pump exercise is an effective method of lower limb functional training. It mainly drives the ankle joint to move through the rhythmic contraction of the lower limb muscles, thus playing a pump-like role and promoting blood and lymphatic circulation in the lower limbs. It plays an important role in rehabilitation after orthopedic surgery and prevention of deep vein thrombosis in the lower limbs. Specifically, ankle pump exercise includes flexion and extension and circumduction of the ankle joint. These movements can effectively promote blood circulation in the lower limbs, prevent lower limb edema, varicose veins and deep vein thrombosis, and also help prevent muscle atrophy and ankle stiffness. Existing ankle pump exercise monitoring methods mainly rely on manual observation or simple equipment recording, which has the problems of insufficient accuracy, inability to take into account the patient's own factors in the comprehensive evaluation ability, and inaccurate evaluation results. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention provides a method and system for processing ankle pump exercise monitoring data, which has the advantages of accurately evaluating ankle pump exercise results and solves the above-mentioned technical problems.

[0004] To achieve the above-mentioned object, the present invention provides the following technical solutions: a processing system for ankle pump motion monitoring data, comprising a patient data acquisition module, an ankle pump motion data detection module, a physiological signal acquisition module, an ankle pump motion analysis module and a motion data evaluation module; The patient data collection module acquires the age and physiological data actively uploaded by the patient and constructs a physiological database; The ankle pump motion data detection module obtains the motion data and interference data of the patient during the ankle pump motion process and constructs a motion database; The physiological signal acquisition module acquires the patient's physiological signals during ankle pump exercise and constructs a physiological signal database; The ankle pump motion analysis module includes a physiological data analysis unit, a physiological signal analysis unit and a comprehensive analysis unit. The physiological data analysis unit obtains the physiological data weight based on the physiological database analysis and calculation. The physiological signal analysis unit obtains the physiological signal weight based on the constructed physiological signal database analysis and calculation. The comprehensive analysis unit obtains the motion integrity coefficient and the motion interference coefficient based on the motion database extraction, and obtains the ankle pump motion coefficient by comprehensive calculation based on the physiological data weight and the physiological signal weight. The motion data evaluation module evaluates the patient's current motion state based on the ankle pump motion coefficient, and gives the patient exercise intensity adjustment suggestions based on the ankle pump motion coefficient.

[0005] As a preferred technical solution of the present invention, the specific steps of the physiological data analysis unit to obtain the physiological data weight based on the physiological database analysis and calculation are as follows: Step A1: Screening data from the patient database based on the patient's age to obtain a set of all patients of the same age; Step A2: Obtain the current patient's The BMI index detected in each time period is expressed as follows: ; in, They represent the BMI value of the patient at the first measurement time, ..., the The BMI value at the time of the first measurement, ..., The BMI value at the time of measurement, represents the BMI dataset, , and calculate the average BMI value of patients based on the BMI data set ; Step A3: Obtain all diseases of the patient's ankle, foot, and calf from the physiological database and calculate the disease impact coefficient , the specific expression is as follows: ; in, Indicates the number of all diseases of the ankle, foot, and calf that the patient has experienced. represents a natural constant; Step A4: Based on the average BMI value of the patients calculated in step A2 , the disease impact coefficient calculated in step A3 The physiological data weight is calculated comprehensively with the patient set in step A1.

[0006] As a preferred technical solution of the present invention, the specific expression of the physiological data weight calculated in step A4 is as follows: ; in, represents the standard BMI value of the same age as the patient obtained based on the patient set, represents the average BMI value of patients, Indicates the number of all diseases of the ankle, foot, and calf that the patient has experienced. Represents the weight of physiological data.

[0007] As a preferred technical solution of the present invention, the specific steps of the physiological signal analysis unit analyzing and calculating the physiological signal weight based on the constructed physiological signal database are as follows: Step B1: Obtain the total number of The specific expression of the myoelectric signal frequency of the sampling point is as follows: ; in, represents the EMG signal frequency dataset, They represent the first, ..., and , ..., The frequency of the electromyographic signal collected at the sampling points is , the EMG signal frequency is obtained through an electrode patch at the gastrocnemius muscle of the calf; Step B2: Calculate the patient's fatigue level. The specific expression is as follows: ; in, Indicates the patient's fatigue level. Indicates that during the training process, the first, ..., , ..., All the EMG signal frequencies collected at the sampling points that are lower than the fatigue frequency The total number of Step B3: Collect the breathing change rate of the patient during the current training. The specific expression is as follows: ; in, Indicates the breathing change rate of the patient during current training. Indicates the number of breaths during the patient’s current training. Indicates the number of breaths the patient took during the last training session; Step B4: Patient fatigue level calculated based on step B2 The physiological signal weight is obtained by comprehensively calculating the respiratory change rate of the patient during current training.

[0008] As a preferred technical solution of the present invention, the patient's fatigue level calculated in step B4 is The specific expression of the physiological signal weight obtained by comprehensively calculating the breathing change rate of the patient during current training is as follows: ; in, represents the physiological signal weight, Indicates the breathing change rate of the patient during current training. Indicates the patient's fatigue level.

[0009] As a preferred technical solution of the present invention, the specific expression of the motion integrity coefficient extracted by the comprehensive analysis unit based on the motion database is as follows: ; in, Indicates the number of completions. Indicates the standard number of times, Indicates the number of times completed by the left ankle. Indicates the number of times the right ankle is completed. represents the mean value of the completed angle, Indicates the standard finishing angle, Represents the motion integrity coefficient.

[0010] As a preferred technical solution of the present invention, the specific expression of the motion interference coefficient extracted by the comprehensive analysis unit based on the motion database is as follows: ; in, represents the motion interference coefficient, Indicates the number of completions. Indicates the number of times the knee rocks during the ankle pump exercise.

[0011] As a preferred technical solution of the present invention, the comprehensive analysis unit extracts the motion integrity coefficient and the motion interference coefficient based on the motion database, and combines the physiological data weight and the physiological signal weight to comprehensively calculate the specific expression of the ankle pump motion coefficient as follows: ; in, represents the ankle pump motion coefficient, represents the motion interference coefficient, represents the physiological signal weight, represents the weight of physiological data, Represents the motion integrity coefficient.

[0012] As a preferred technical solution of the present invention, the specific expression of the exercise data evaluation module for giving the patient exercise intensity adjustment suggestion based on the ankle pump exercise coefficient is as follows: ; in, Indicates the patient's exercise intensity. Indicates the patient's historical exercise intensity, represents the historical ankle pump motion coefficient, represents the ankle pump motion coefficient.

[0013] The present invention also provides a method for processing ankle pump motion monitoring data, based on the above-mentioned ankle pump motion monitoring data processing system, comprising the following steps: Step 1: Collect the age and physiological data actively uploaded by the current patient and build a physiological database; Step 2: Analyze and calculate the physiological data weight based on the physiological database; Step 3: Acquire the patient's motion data and interference data during the ankle pump exercise to build a motion database, and simultaneously acquire the patient's physiological signals during the ankle pump exercise and build a physiological signal database; Step 4: Analyze and calculate the physiological signal weight based on the constructed physiological signal database, and extract the motion integrity coefficient and motion interference coefficient based on the motion database; Step 5: The motion integrity coefficient and the motion interference coefficient are combined with the physiological data weight and the physiological signal weight to obtain the ankle pump motion coefficient; Step 6: Evaluate the patient's current exercise status based on the ankle pump motion coefficient, and give suggestions on adjusting the patient's exercise intensity based on the ankle pump motion coefficient.

[0014] Compared with the prior art, the present invention provides a method and system for processing ankle pump motion monitoring data, which has the following beneficial effects: The present invention constructs a motion database by acquiring the motion data and interference data of the patient during the ankle pump exercise process, and simultaneously acquires the physiological signals of the patient during the ankle pump exercise process and constructs a physiological signal database, and comprehensively calculates the ankle pump motion coefficient by combining the motion integrity coefficient and the motion interference coefficient with the physiological data weight and the physiological signal weight, and gives the patient's exercise intensity adjustment suggestion based on the ankle pump motion coefficient, so as to effectively determine the intensity of the patient's next training according to the patient's comprehensive information during the training process, thereby completing the progressive growth training process. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the system framework of the present invention; Figure 2 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0016] 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.

[0017] See also Figure 1 - Figure 2 , a processing system for ankle pump motion monitoring data, comprising a patient data acquisition module, an ankle pump motion data detection module, a physiological signal acquisition module, an ankle pump motion analysis module and a motion data evaluation module; The patient data collection module obtains the age and physiological data actively uploaded by the patient and builds a physiological database; The ankle pump motion data detection module obtains the motion data and interference data of the patient during the ankle pump motion process and constructs a motion database; The physiological signal acquisition module obtains the patient's physiological signals during ankle pump exercise and builds a physiological signal database; The ankle pump motion analysis module includes a physiological data analysis unit, a physiological signal analysis unit and a comprehensive analysis unit. The physiological data analysis unit calculates and obtains the physiological data weight based on the physiological database. The physiological signal analysis unit calculates and obtains the physiological signal weight based on the constructed physiological signal database. The comprehensive analysis unit extracts the motion integrity coefficient and the motion interference coefficient based on the motion database, and calculates and obtains the ankle pump motion coefficient by combining the physiological data weight and the physiological signal weight. The specific steps of the physiological data analysis unit to obtain the physiological data weight based on the physiological database analysis are as follows: Step A1: Screening data from the patient database based on the patient's age to obtain a set of all patients of the same age; Step A2: Obtain the current patient's The BMI index detected in each time period is expressed as follows: ; in, They represent the BMI value of the patient at the first measurement time, ..., the The BMI value at the time of the first measurement, ..., The BMI value at the time of measurement, Represents the BMI data set, and calculates the average BMI value of the patient based on the BMI data set , BMI is calculated as weight / height 2 , ; Step A3: Obtain all diseases of the patient's ankle, foot, and calf from the physiological database and calculate the disease impact coefficient , the specific expression is as follows: ; in, Indicates the number of all diseases of the ankle, foot, and calf that the patient has experienced. represents a natural constant; Step A4: Based on the average BMI value of the patients calculated in step A2 , the disease impact coefficient calculated in step A3 The specific expression of physiological data weight obtained by comprehensive calculation with the patient set in step A1 is as follows: ; in, Indicates the standard BMI value of the patient of the same age obtained based on the patient set. The standard BMI value here is the average value of the patient of the same age in the patient set. represents the average BMI value of patients, Indicates the number of all diseases of the ankle, foot, and calf that the patient has experienced. Represents the weight of physiological data.

[0018] The specific steps of the physiological signal analysis unit analyzing and calculating the physiological signal weight based on the constructed physiological signal database are as follows: Step B1: Obtain the total number of The specific expression of the myoelectric signal frequency of the sampling point is as follows: ; in, represents the EMG signal frequency dataset, They represent the first, ..., and , ..., The frequency of the electromyographic signal collected at the sampling points is ,The ,electromyographic signal frequency is obtained through the electrode patch at the calf gastrocnemius; Step B2: Calculate the patient's fatigue level. The specific expression is as follows: ; in, Indicates the patient's fatigue level. Indicates that during the training process, the first, ..., , ..., All the EMG signal frequencies collected at the sampling points that are lower than the fatigue frequency The total number of Step B3: Collect the breathing change rate of the patient during the current training. The specific expression is as follows: ; in, Indicates the breathing change rate of the patient during current training. Indicates the number of breaths during the patient’s current training. Indicates the number of breaths the patient took during the last training session; Step B4: Patient fatigue level calculated based on step B2 The physiological signal weight is calculated by combining the patient's breathing change rate during current training. The specific expression is as follows: ; in, represents the physiological signal weight, Indicates the breathing change rate of the patient during current training. Indicates the patient's fatigue level.

[0019] The specific expression of the motion integrity coefficient extracted by the comprehensive analysis unit based on the motion database is as follows: ; in, Indicates the number of completions. Indicates the standard number of times, Indicates the number of times completed by the left ankle. Indicates the number of times the right ankle is completed. represents the mean value of the completed angle, Indicates the standard finishing angle, It represents the movement completeness coefficient, and the completion angle specifically refers to the dorsiflexion angle of the foot.

[0020] The specific expression of the motion interference coefficient extracted by the comprehensive analysis unit based on the motion database is as follows: ; in, represents the motion interference coefficient, Indicates the number of completions. Indicates the number of times the knee rocks during the ankle pump exercise.

[0021] The comprehensive analysis unit extracts the motion integrity coefficient and motion interference coefficient based on the motion database, and combines the physiological data weight and the physiological signal weight to comprehensively calculate the specific expression of the ankle pump motion coefficient as follows: ; in, represents the ankle pump motion coefficient, represents the motion interference coefficient, represents the physiological signal weight, represents the weight of physiological data, Represents the motion integrity coefficient.

[0022] The motion data evaluation module evaluates the patient's current motion state based on the ankle pump motion coefficient, and gives the patient exercise intensity adjustment suggestions based on the ankle pump motion coefficient. The specific expression of the motion data evaluation module giving the patient exercise intensity adjustment suggestions based on the ankle pump motion coefficient is as follows: ; in, Indicates the patient's exercise intensity. Indicates the patient's historical exercise intensity, represents the historical ankle pump motion coefficient, represents the ankle pump motion coefficient.

[0023] Embodiment: The specific data recorded in this embodiment are as follows Table 1: Table 1

[0024] At this time, the calculation ; ; ; ; ; ; At this time, the intensity is adjusted upward to 3.306, so that the intensity of the patient's next training can be effectively determined based on the patient's comprehensive information during the training process, thereby completing the progressive growth training process; A method for processing ankle pump motion monitoring data, based on the above-mentioned ankle pump motion monitoring data processing system, comprises the following steps: Step 1: Collect the age and physiological data actively uploaded by the current patient and build a physiological database; Step 2: Analyze and calculate the physiological data weight based on the physiological database; Step 3: Acquire the patient's motion data and interference data during the ankle pump exercise to build a motion database, and simultaneously acquire the patient's physiological signals during the ankle pump exercise and build a physiological signal database; Step 4: Analyze and calculate the physiological signal weight based on the constructed physiological signal database, and extract the motion integrity coefficient and motion interference coefficient based on the motion database; Step 5: The motion integrity coefficient and the motion interference coefficient are combined with the physiological data weight and the physiological signal weight to obtain the ankle pump motion coefficient; Step 6: Evaluate the patient's current exercise status based on the ankle pump motion coefficient, and give suggestions on adjusting the patient's exercise intensity based on the ankle pump motion coefficient.

[0025] The present invention can also be used for an ankle pump motion monitoring device, and directly connected with the ankle pump motion monitoring device, and the user's motion data can be recorded in real time in the form of a mobile phone APP, and data transmission can be realized through the internal communication of the AHRS; Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A processing system for ankle pump motion monitoring data, characterized in that: It includes a patient data acquisition module, an ankle pump motion data detection module, a physiological signal acquisition module, an ankle pump motion analysis module and a motion data evaluation module; The patient data collection module acquires the age and physiological data actively uploaded by the patient and constructs a physiological database; The ankle pump motion data detection module obtains the motion data and interference data of the patient during the ankle pump motion process and constructs a motion database; The physiological signal acquisition module acquires the patient's physiological signals during ankle pump exercise and constructs a physiological signal database; The ankle pump motion analysis module includes a physiological data analysis unit, a physiological signal analysis unit and a comprehensive analysis unit. The physiological data analysis unit obtains the physiological data weight based on the physiological database analysis and calculation. The physiological signal analysis unit obtains the physiological signal weight based on the constructed physiological signal database analysis and calculation. The comprehensive analysis unit obtains the motion integrity coefficient and the motion interference coefficient based on the motion database extraction, and obtains the ankle pump motion coefficient by comprehensive calculation based on the physiological data weight and the physiological signal weight. The motion data evaluation module evaluates the patient's current motion state based on the ankle pump motion coefficient, and gives the patient exercise intensity adjustment suggestions based on the ankle pump motion coefficient.

2. A processing system for ankle pump motion monitoring data according to claim 1, characterized in that: The specific steps of the physiological data analysis unit to obtain the physiological data weight based on the physiological database analysis are as follows: Step A1: Screening data from the patient database based on the patient's age to obtain a set of all patients of the same age; Step A2: Obtain the current patient's The BMI index detected in each time period is expressed as follows: ； in, They represent the BMI value of the patient at the first measurement time, ..., The BMI value at the time of the first measurement, ..., The BMI value at the time of measurement, represents the BMI dataset, , and calculate the average BMI value of patients based on the BMI data set ; Step A3: Obtain all diseases of the patient's ankle, foot, and calf from the physiological database and calculate the disease impact coefficient , the specific expression is as follows: ； in, Indicates the number of all diseases of the ankle, foot, and calf that the patient has experienced. represents a natural constant; Step A4: Based on the average BMI value of the patients calculated in step A2 , the disease impact coefficient calculated in step A3 The physiological data weight is calculated comprehensively with the patient set in step A1.

3. A processing system for ankle pump motion monitoring data according to claim 2, characterized in that: The specific expression of the physiological data weight calculated in step A4 is as follows: ； in, represents the standard BMI value of the same age as the patient obtained based on the patient set, represents the average BMI value of patients, Indicates the number of all diseases of the ankle, foot, and calf that the patient has experienced. Represents the weight of physiological data.

4. The ankle pump motion monitoring data processing system according to claim 1, characterized in that: The specific steps of the physiological signal analysis unit analyzing and calculating the physiological signal weight based on the constructed physiological signal database are as follows: Step B1: Obtain the total number of The specific expression of the myoelectric signal frequency of the sampling point is as follows: ； in, represents the EMG signal frequency dataset, They represent the first, ..., and , ..., The frequency of the electromyographic signal collected at the sampling points is , the EMG signal frequency is obtained through an electrode patch at the gastrocnemius muscle of the calf; Step B2: Calculate the patient's fatigue level. The specific expression is as follows: ； in, Indicates the patient's fatigue level. Indicates that during the training process, the first, ..., , ..., All the EMG signal frequencies collected at the sampling points that are lower than the fatigue frequency The total number of Step B3: Collect the breathing change rate of the patient during the current training. The specific expression is as follows: ； in, Indicates the breathing change rate of the patient during current training. Indicates the number of breaths during the patient’s current training. Indicates the number of breaths the patient took during the last training session; Step B4: Patient fatigue level calculated based on step B2 The physiological signal weight is obtained by comprehensively calculating the respiratory change rate of the patient during current training.

5. The ankle pump motion monitoring data processing system according to claim 4, characterized in that: The patient's fatigue level calculated in step B2 is used in step B4. The specific expression of the physiological signal weight obtained by comprehensively calculating the breathing change rate of the patient during current training is as follows: ； in, represents the physiological signal weight, Indicates the breathing change rate of the patient during current training. Indicates the patient's fatigue level.

6. The ankle pump motion monitoring data processing system according to claim 1, characterized in that: The specific expression of the motion integrity coefficient extracted by the comprehensive analysis unit based on the motion database is as follows: ； in, Indicates the number of completions. Indicates the standard number of times, Indicates the number of times completed by the left ankle. Indicates the number of times the right ankle is completed. represents the mean value of the completed angle, Indicates the standard finishing angle, Represents the motion integrity coefficient.

7. The ankle pump motion monitoring data processing system according to claim 6, characterized in that: The specific expression of the motion interference coefficient extracted by the comprehensive analysis unit based on the motion database is as follows: ； in, represents the motion interference coefficient, Indicates the number of completions. Indicates the number of times the knee rocks during the ankle pump exercise.

8. The ankle pump motion monitoring data processing system according to claim 7, characterized in that: The comprehensive analysis unit extracts the motion integrity coefficient and the motion interference coefficient based on the motion database, and combines the physiological data weight and the physiological signal weight to comprehensively calculate the specific expression of the ankle pump motion coefficient as follows: ； in, represents the ankle pump motion coefficient, represents the motion interference coefficient, represents the physiological signal weight, represents the weight of physiological data, Represents the motion integrity coefficient.

9. The ankle pump motion monitoring data processing system according to claim 8, characterized in that: The specific expression of the exercise data evaluation module for providing the patient's exercise intensity adjustment suggestion based on the ankle pump exercise coefficient is as follows: ； in, Indicates the patient's exercise intensity. Indicates the patient's historical exercise intensity, represents the historical ankle pump motion coefficient, represents the ankle pump motion coefficient.

10. A method for processing ankle pump motion monitoring data, based on a system for processing ankle pump motion monitoring data according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Collect the age and physiological data actively uploaded by the current patient and build a physiological database; Step 2: Analyze and calculate the physiological data weight based on the physiological database; Step 3: Acquire the patient's motion data and interference data during the ankle pump exercise to build a motion database, and simultaneously acquire the patient's physiological signals during the ankle pump exercise and build a physiological signal database; Step 4: Analyze and calculate the physiological signal weight based on the constructed physiological signal database, and extract the motion integrity coefficient and motion interference coefficient based on the motion database; Step 5: The motion integrity coefficient and the motion interference coefficient are combined with the physiological data weight and the physiological signal weight to obtain the ankle pump motion coefficient; Step 6: Evaluate the patient's current exercise status based on the ankle pump motion coefficient, and give suggestions on adjusting the patient's exercise intensity based on the ankle pump motion coefficient.

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