Cerebral palsy child lower limb spasm training method and system based on treatment data

Abstract

The invention discloses a cerebral palsy child lower limb spasm training method and system based on treatment data, relates to the field of rehabilitation medicine, and solves the problem that a cerebral palsy child lower limb spasm training method is poor in pertinence. Comprising the following steps: S1, respectively acquiring historical training sample data, a target child patient spasm attack intensity stability coefficient and a target child patient period spasm frequency stability coefficient to obtain historical spasm attack data; s2, performing training interval division on the target spasm child patient according to the historical spasm attack data to obtain training interval division data of the target child patient; s3, performing lower limb spasm training on the target child patient in the first spasm training classification interval according to the target child patient training interval division data; and S4, performing lower limb spasm training on the target child patient in the second spasm training classification interval according to the training interval division data of the target child patient. According to the method, the pertinence and the safety of the lower limb spasm training method for the child patient with cerebral palsy can be improved.

Description

Technical Field

[0001] The present invention belongs to the field of rehabilitation medicine and relates to data analysis technology, and specifically to a lower limb muscle spasm training method and system for children with cerebral palsy based on treatment data. Background Art

[0002] The existing training methods for lower limb muscle spasticity in children with cerebral palsy have the following defects:

[0003] 1. The current training methods often adopt a set of standardized processes, and it is impossible to determine the training duration based on the patient's historical training records, resulting in a lack of targeted training and difficulty in ensuring the treatment effect;

[0004] 2. The current training method cannot use medical indicators to determine whether the training is completed in real time during the training process, which may easily lead to excessive training intensity and cause unnecessary harm to the children.

[0005] To this end, we propose a lower limb muscle spasticity training method and system for children with cerebral palsy based on treatment data. Summary of the invention

[0006] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a lower limb muscle spasm training method and system for children with cerebral palsy based on treatment data. The present invention is based on obtaining historical spasm incidence data, dividing the target spastic children into training intervals according to the historical spastic incidence data, obtaining the target children's training interval division data, performing lower limb spasticity training on the target children in the first spasticity training classification interval according to the target children's training interval division data, and performing lower limb spasticity training on the target children in the second spasticity training classification interval according to the target children's training interval division data.

[0007] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: the lower limb muscle spasticity training method for children with cerebral palsy based on treatment data comprises the following specific steps:

[0008] Step S1: respectively obtaining the first sample spasm recovery coefficient to the jth sample spasm recovery coefficient corresponding to the target child, the average spasm training time of the target child, and the spasm training time stability coefficient of the target child, obtaining historical training sample data, obtaining the spasm onset intensity stability coefficient of the target child and the target child's periodic spasm number stability coefficient, and defining the historical training sample data, the target child's spasm onset intensity stability coefficient, and the target child's periodic spasm number stability coefficient as historical spasm onset data;

[0009] Step S2: Calculate the common reference coefficient for spasticity treatment based on the historical spasticity incidence data, obtain the common reference coefficient threshold for spasticity treatment, perform threshold judgment on the common reference coefficient for spasticity treatment, divide the target child into the first spasticity training classification interval or the second spasticity training classification interval, and obtain the training interval division data for the target child;

[0010] Step S3: obtaining the average spasm training time of the target child according to the historical spasm incidence data, obtaining the average electromyographic signal strength, the average EMG signal frequency and the average potential difference, and calculating the training time weight, calculating the product of the average spasm training time of the target child and the training time weight, and obtaining the training time of the target child at that time, and performing lower limb spasticity training on the target child in the first spasm training classification interval according to the training time of the target child at that time;

[0011] Step S4: Analyze the historical spasm incidence data to obtain a spasm recovery coefficient benchmark interval, obtain the real-time EMG signal frequency, the first real-time muscle tension score, and the second real-time muscle tension score of the target child in the first spasm training classification interval, calculate the real-time spasm recovery coefficient, and perform lower limb spasticity training on the target child in the second spasm training classification interval based on the real-time spasm recovery coefficient and the spasm recovery coefficient benchmark interval.

[0012] Furthermore, the step S1 further includes the following specific steps:

[0013] Step S1: acquiring and analyzing the historical data of spasticity training of the target cerebral palsy child to obtain historical training sample data;

[0014] Step S12: Analyze the historical spasm intensity of the target child to obtain the spasm intensity stability coefficient of the target child;

[0015] Step S13: obtaining the stability coefficient of the number of periodic spasms of the target child;

[0016] Step S14: defining the target patient's spasm intensity stability coefficient, the target patient's periodic spasm frequency stability coefficient, and historical training sample data as historical spasm incidence data;

[0017] The step S13 further includes the following specific steps:

[0018] Step S131: Acquire historical treatment data of the target child, divide the treatment time period corresponding to the historical treatment data into k treatment monitoring cycles, and name them as the first treatment monitoring cycle to the kth treatment monitoring cycle respectively;

[0019] Step S132: obtaining the number of spasms corresponding to the target child in the first treatment monitoring cycle to the kth treatment monitoring cycle according to the historical treatment data, and obtaining the number of spasms in the first monitoring cycle to the kth monitoring cycle;

[0020] Step S133: Calculate the average of the number of spasms from the first monitoring cycle to the kth monitoring cycle to obtain the average number of spasms in the target child;

[0021] Step S134: calculating the spasm number from the first monitoring cycle to the kth monitoring cycle and the average spasm number of the target patient's cycle to obtain a stability coefficient of the spasm number of the target patient's cycle;

[0022] The stability coefficient of the number of periodic spasms in the target child is calculated, and the specific formula is configured as follows:

[0023]

[0024] Among them, Zcx is the stability coefficient of the number of spasms in the target child cycle, Zq1 to Zqk are the number of spasms in the first monitoring cycle to the kth monitoring cycle, respectively, Zqp is the average number of spasms in the target child cycle, and k is the quantity value corresponding to the treatment monitoring cycle.

[0025] Furthermore, the step S11 further includes the following specific steps:

[0026] Step S111: acquiring the historical data of spasticity training of the target cerebral palsy child, selecting j spasticity trainings from the historical data of spasticity training as sample spasticity trainings, and naming them as the first sample spasticity training to the jth sample spasticity training respectively;

[0027] Step S112: acquiring the single spasm training durations corresponding to the first sample spasm training to the j-th sample spasm training according to the spasm training history data, and obtaining the first single spasm training duration to the j-th single spasm training duration;

[0028] Step S113: Calculate the average of the first single spasm training duration to the jth single spasm training duration to obtain the average spasm training duration of the target child;

[0029] Step S114: calculating the stability coefficient of the spasm training duration of the target child by combining the first single spasm training duration to the jth single spasm training duration and the average spasm training duration of the target child;

[0030] The stability coefficient of the spasticity training duration of the target children was calculated, and the specific formula configuration is as follows:

[0031]

[0032] Among them, Xlw is the stability coefficient of the spasm training duration of the target children, Xyp is the average spasm training duration of the target children, Xy1 to Xyj are the duration of the first single spasm training to the jth single spasm training, and j is the number value corresponding to the selected sample spasm training;

[0033] Step S115: acquiring the limb recovery coefficient corresponding to the first sample spasticity training to obtain the first sample spasticity recovery coefficient;

[0034] Step S116: respectively acquiring the limb recovery coefficients corresponding to the second sample spasticity training to the j-th sample spasticity training to obtain the second sample spasticity recovery coefficient to the j-th sample spasticity recovery coefficient;

[0035] Step S117: defining the first sample spasm recovery coefficient to the jth sample spasm recovery coefficient, the average spasm training duration of the target patient, and the spasm training duration stability coefficient of the target patient as historical training sample data;

[0036] The step S115 further includes the following specific steps:

[0037] Step S1151: After the target child patient completes the first sample spasticity training, the EMG signal frequency corresponding to the training part of the child at the end of the training is obtained through the electromyography monitoring device to obtain the first EMG signal frequency;

[0038] Step S1152: Obtain the Ashworth score of muscle tension corresponding to the lower limb spasticity training site of the target child before the first sample spasticity training is performed, to obtain a first muscle tension score;

[0039] Step S1153: Obtain the Ashworth score of muscle tension corresponding to the lower limb spasticity training site of the target child after completing the first sample spasticity training, and obtain a second muscle tension score;

[0040] Step S1154: obtaining a first sample spasm recovery coefficient by calculating the first EMG signal frequency, the first muscle tension score, and the second muscle tension score;

[0041] The spasm recovery coefficient of the first sample is calculated as follows:

[0042] Hf1=(Zp1-Zp2)+Egp*c1;

[0043] Wherein, Hf1 is the first sample spasm recovery coefficient, Zp1 is the first muscle tension score, Zp2 is the second muscle tension score, Egp is the first EMG signal frequency, c1 is the set proportional coefficient and c1 is greater than 0.

[0044] Furthermore, the step S12 further includes the following specific steps:

[0045] Step S121: acquiring historical treatment data of the target child, acquiring the body parts where the lower limbs of the target child have cramps according to the historical treatment data, and marking the body parts where the cramps occur as the first cramp part to the mth cramp part respectively;

[0046] Step S122: acquiring the number of spasm occurrences corresponding to the first spasm site to the mth spasm site respectively according to the historical treatment data, and obtaining the number of spasm occurrences from the first site to the mth site;

[0047] Step S123: obtaining the average single spasm duration of the first spasm site, and obtaining the average spasm duration of the first site;

[0048] The details are as follows:

[0049] Step S1231: selecting n spasms at the first spasm site as sample monitoring spasms, and marking them as the first sample monitoring spasm to the nth sample monitoring spasm;

[0050] Step S1232: Obtain the single duration corresponding to the first sample monitoring spasm to the nth sample monitoring spasm, and obtain the duration of the first spasm to the nth spasm;

[0051] Step S1233: Calculate the average of the duration of the first spasm to the duration of the nth spasm to obtain the average single spasm duration corresponding to the first spasm site, and name it the average spasm duration of the first site;

[0052] Step S124: respectively obtaining the single average spasm durations corresponding to the second spasm site to the mth spasm site, and obtaining the average spasm duration of the second site to the average spasm duration of the mth site;

[0053] Step S125: Calculate the product of the number of spasms at the first part and the average spasm duration of the first part to obtain a spasm intensity coefficient corresponding to the first spasm part, and name it the first spasm intensity coefficient;

[0054] Step S126: respectively acquiring the spasm intensity coefficients corresponding to the second spasm site to the mth spasm site, to obtain the spasm intensity coefficients of the second site to the mth site;

[0055] Step S127: Calculate the average of the spasm intensity coefficients of the first part to the mth part to obtain the average spasm intensity coefficient of the parts;

[0056] Step S128: calculating the spasm intensity coefficient of the first part to the mth part and the average of the spasm intensity coefficients of the parts to obtain the spasm intensity stability coefficient of the target child;

[0057] The stability coefficient of the spasm intensity of the target child is calculated, and the specific formula is configured as follows:

[0058]

[0059] Among them, Jqx is the stability coefficient of the spasm intensity of the target child, Jqs1 to Jqsm are the spasm intensity coefficients of the first to mth parts respectively, Jlq is the average value of the spasm intensity coefficients of the parts, and m is the number of parts where spasms occur in the lower limbs of the target child.

[0060] Furthermore, the step S2 further includes the following specific steps:

[0061] Step S21: Acquire historical spasm incidence data, and acquire the spasm incidence intensity stability coefficient of the target patient, the target patient periodic spasm frequency stability coefficient, and historical training sample data according to the historical spasm incidence data;

[0062] Step S22: obtaining the stability coefficient of the spasticity training duration of the target child according to the historical training sample data;

[0063] Step S23: calculating the stability coefficient of the target child's spasm intensity, the stability coefficient of the target child's periodic spasm number, and the stability coefficient of the target child's spasm training duration to obtain a common reference coefficient for spasm treatment;

[0064] The common reference coefficient for spasticity treatment is calculated, and the specific formula is configured as follows:

[0065] Bqw=Zcx+Jqx+Xlw;

[0066] Among them, Bqw is the common reference coefficient for spasticity treatment, Zcx is the stability coefficient for the number of periodic spasticity in the target child, Jqx is the stability coefficient for the intensity of spasticity in the target child, and Xlw is the stability coefficient for the duration of spasticity training in the target child;

[0067] Step S24: Obtain the common reference coefficient threshold for spasticity treatment, and perform threshold judgment on the common reference coefficient threshold for spasticity treatment according to the common reference coefficient for spasticity treatment, divide the target child into the first spasticity training classification interval or the second spasticity training classification interval, and obtain the training interval division data for the target child.

[0068] Furthermore, the step S24 further includes the following specific steps:

[0069] Step S241: respectively obtaining a target child's spasm intensity stability coefficient threshold, a target child's periodic spasm number stability coefficient threshold, and a target child's spasm training duration stability coefficient threshold;

[0070] Step S242: calculating the target child's spasm intensity stability coefficient threshold, the target child's periodic spasm number stability coefficient threshold, and the target child's spasm training duration stability coefficient threshold to obtain a spasm treatment common reference coefficient threshold;

[0071] Step S243: when the spasticity treatment commonality reference coefficient is less than or equal to the spasticity treatment commonality reference coefficient threshold, it is determined that the target child is in the first spasticity training classification interval;

[0072] Step S244: When the common reference coefficient for spasticity treatment is greater than the common reference coefficient threshold for spasticity treatment, it is determined that the target child is in the second spasticity training classification interval.

[0073] Furthermore, the step S3 further includes the following specific steps:

[0074] Step S31: Acquire historical spasm incidence data, and acquire historical training sample data according to the historical spasm incidence data;

[0075] Step S32: obtaining the average spasticity training duration of the target children according to the historical training sample data;

[0076] Step S33: Before training the target child, obtain the training time weight by performing electromyography monitoring on the target child;

[0077] Step S34: Calculate the product of the average spasticity training duration of the target patient and the training duration weight to obtain the training duration of the target patient;

[0078] Step S35: Perform lower limb spasticity training on the target child in the first spasticity training classification interval. When the training duration reaches the current training duration of the target child, the training is completed.

[0079] Furthermore, the step S33 further includes the following specific steps:

[0080] Step S331: performing electromyographic monitoring on the lower limbs of the target child, marking the time range for the target to receive electromyographic monitoring as the electromyographic monitoring time range, and acquiring the electromyographic monitoring results corresponding to the electromyographic monitoring time range to obtain electromyographic monitoring data of the target child;

[0081] Step S332: obtaining the average myoelectric signal strength of the target child within the myoelectric monitoring time range according to the myoelectric monitoring data of the target child;

[0082] Step S333: obtaining the average EMG signal frequency of the target child within the EMG monitoring time range according to the EMG monitoring data of the target child;

[0083] Step S334: obtaining the average potential difference of the target child within the electromyography monitoring time range according to the electromyography monitoring data of the target child;

[0084] Step S335: Calculate the average electromyographic signal strength, the average EMG signal frequency, and the average potential difference to obtain the training duration weight;

[0085] The training duration weight is calculated, and the specific formula configuration is as follows:

[0086] Wx=(Jdp+Emg×b1+Dwc)×b2;

[0087] Among them, Wx is the training duration weight, Jdp is the average electromyographic signal strength, Emg is the average EMG signal frequency, Dwc is the average potential difference, b1 and b2 are the set proportional coefficients and both b1 and b2 are greater than 0.

[0088] Furthermore, the step S4 further includes the following specific steps:

[0089] Step S41: Acquire historical spasm incidence data, and acquire historical training sample data according to the historical spasm incidence data;

[0090] Step S42: acquiring the first sample spasm recovery coefficient to the jth sample spasm recovery coefficient according to the historical training sample data;

[0091] Step S43: sorting the first sample spasm recovery coefficient to the jth sample spasm recovery coefficient according to the numerical values, marking the first sample spasm recovery coefficient as the first spasm recovery coefficient threshold, marking the last sample spasm recovery coefficient as the second spasm recovery coefficient threshold, and marking the numerical range between the first spasm recovery coefficient threshold and the second spasm recovery coefficient threshold as the spasm recovery coefficient reference interval;

[0092] Step S44: in the process of performing spasticity training on the target child in the second spasticity training classification interval, obtaining the real-time spasticity recovery coefficient corresponding to the target child in real time;

[0093] The step S44 further includes the following specific steps:

[0094] Step S441: obtaining the EMG signal frequency corresponding to the training part of the child in real time during the training process through the electromyography monitoring device to obtain the real-time EMG signal frequency;

[0095] Step S442: Obtain the Ashworth score of muscle tension corresponding to the lower limb spasticity training site of the target child before the spasticity training, and obtain a first real-time muscle tension score;

[0096] Step S443: while the target child is undergoing spasticity training, the muscle tension Ashworth score corresponding to the lower limb spasticity training site at the current moment is obtained to obtain a second real-time muscle tension score;

[0097] Step S444: obtaining a real-time spasm recovery coefficient by calculating the real-time EMG signal frequency, the first real-time muscle tension score, and the second real-time muscle tension score;

[0098] The real-time spasm recovery coefficient is calculated, and the specific formula is configured as follows:

[0099] Hfs=(Zps1-Zps2)+Egps*c1;

[0100] Wherein, Hfs is the real-time spasm recovery coefficient, Zps1 is the first real-time muscle tension score, Zps2 is the second real-time muscle tension score, Egps is the real-time EMG signal frequency, c1 is the set proportional coefficient and c1 is greater than 0;

[0101] Step S45: When the real-time spasm recovery coefficient corresponding to the target child is within the spasm recovery coefficient reference interval, the spasm training is completed;

[0102] Step S46: When the real-time spasm recovery coefficient corresponding to the target child is not in the spasm recovery coefficient reference interval, continue to perform spasm training on the target child until the real-time spasm recovery coefficient is in the spasm recovery coefficient reference interval.

[0103] The lower limb muscle spasticity training system for children with cerebral palsy based on treatment data includes:

[0104] Data acquisition module: used to obtain historical spasm incidence data;

[0105] Data analysis module: used to divide the target spastic children into training intervals according to the historical spasticity incidence data, and obtain the training interval division data of the target children;

[0106] The first training module is used to perform lower limb spasticity training on target children in the first spasticity training classification interval according to the training interval division data of the target children;

[0107] The second training module is used to perform lower limb spasticity training on target children in the second spasticity training classification interval according to the training interval division data of the target children.

[0108] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0109] 1. The present invention obtains historical spasm incidence data to perform lower limb spasticity training on target children in the first spasticity training classification interval and the second spasticity training classification interval, thereby effectively ensuring the pertinence of the spasticity training duration and the training effect;

[0110] 2. The present invention obtains the real-time spasm recovery coefficient during the spasm training of the target child to control the duration of the lower limb spasticity training, thereby ensuring that the training duration is within the patient's physical tolerance range, and providing safety protection for the spasticity training of the target child. BRIEF DESCRIPTION OF THE DRAWINGS

[0111] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0112] Figure 1 It is a diagram of the implementation steps of the present invention;

[0113] Figure 2 is the overall system block diagram of the present invention;

[0114] Figure 3 Schematic diagram of electromyography monitoring in the present invention. DETAILED DESCRIPTION

[0115] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. 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.

[0116] Embodiment 1

[0117] See also Figure 1 The present invention provides a technical solution: a method for training lower limb muscle spasm in children with cerebral palsy based on treatment data, comprising the following specific steps:

[0118] Step S1: Obtain historical spasm incidence data;

[0119] Step S1: Acquire and analyze the historical data of spasticity training of the target child to obtain historical training sample data;

[0120] The step S11 further includes the following specific steps:

[0121] Step S111: acquiring the target child's spasticity training history data, selecting j spasticity trainings from the spasticity training history data as sample spasticity trainings, and naming them as the first sample spasticity training to the jth sample spasticity training, respectively;

[0122] Step S112: acquiring the single spasm training durations corresponding to the first sample spasm training to the j-th sample spasm training according to the spasm training history data, and obtaining the first single spasm training duration to the j-th single spasm training duration;

[0123] Step S113: Calculate the average of the first single spasm training duration to the jth single spasm training duration to obtain the average spasm training duration of the target child;

[0124] Step S114: calculating the stability coefficient of the spasm training duration of the target child by combining the first single spasm training duration to the jth single spasm training duration and the average spasm training duration of the target child;

[0125] The stability coefficient of the spasticity training duration of the target children was calculated, and the specific formula configuration is as follows:

[0126]

[0127] Among them, Xlw is the stability coefficient of the spasm training duration of the target children, Xyp is the average spasm training duration of the target children, Xy1 to Xyj are the duration of the first single spasm training to the jth single spasm training, and j is the number value corresponding to the selected sample spasm training;

[0128] Step S115: acquiring the limb recovery coefficient corresponding to the first sample spasticity training to obtain the first sample spasticity recovery coefficient;

[0129] The step S115 further includes the following specific steps:

[0130] Step S1151: After the target child patient completes the first sample spasticity training, the EMG signal frequency corresponding to the training part of the child at the end of the training is obtained through the electromyography monitoring device to obtain the first EMG signal frequency;

[0131] Step S1152: Obtain the Ashworth score of muscle tension corresponding to the lower limb spasticity training site of the target child before the first sample spasticity training is performed, to obtain a first muscle tension score;

[0132] Step S1153: Obtain the Ashworth score of muscle tension corresponding to the lower limb spasticity training site of the target child after completing the first sample spasticity training, and obtain a second muscle tension score;

[0133] Step S1154: obtaining a first sample spasm recovery coefficient by calculating the first EMG signal frequency, the first muscle tension score, and the second muscle tension score;

[0134] The spasm recovery coefficient of the first sample is calculated as follows:

[0135] Hf1=(Zp1-Zp2)+Egp*c1;

[0136] Wherein, Hf1 is the first sample spasm recovery coefficient, Zp1 is the first muscle tension score, Zp2 is the second muscle tension score, Egp is the first EMG signal frequency, c1 is the set proportional coefficient and c1 is greater than 0;

[0137] Step S116: respectively acquiring the limb recovery coefficients corresponding to the second sample spasticity training to the j-th sample spasticity training to obtain the second sample spasticity recovery coefficient to the j-th sample spasticity recovery coefficient;

[0138] Step S117: defining the first sample spasm recovery coefficient to the jth sample spasm recovery coefficient, the average spasm training duration of the target patient, and the spasm training duration stability coefficient of the target patient as historical training sample data;

[0139] Step S12: Analyze the historical spasm intensity of the target child to obtain the spasm intensity stability coefficient of the target child;

[0140] The step S12 further includes the following specific steps:

[0141] Step S121: acquiring historical treatment data of the target child, acquiring the body parts where the lower limbs of the target child have cramps according to the historical treatment data, and marking the body parts where the cramps occur as the first cramp part to the mth cramp part respectively;

[0142] Step S122: acquiring the number of spasm occurrences corresponding to the first spasm site to the mth spasm site respectively according to the historical treatment data, and obtaining the number of spasm occurrences from the first site to the mth site;

[0143] Step S123: obtaining the average single spasm duration of the first spasm site, and obtaining the average spasm duration of the first site;

[0144] The details are as follows:

[0145] Step S1231: selecting n spasms at the first spasm site as sample monitoring spasms, and marking them as the first sample monitoring spasm to the nth sample monitoring spasm;

[0146] Step S1232: Obtain the single duration corresponding to the first sample monitoring spasm to the nth sample monitoring spasm, and obtain the duration of the first spasm to the nth spasm;

[0147] Step S1233: Calculate the average of the duration of the first spasm to the duration of the nth spasm to obtain the average single spasm duration corresponding to the first spasm site, and name it the average spasm duration of the first site;

[0148] Step S124: respectively obtaining the single average spasm durations corresponding to the second spasm site to the mth spasm site, and obtaining the average spasm duration of the second site to the average spasm duration of the mth site;

[0149] Step S125: Calculate the product of the number of spasms at the first part and the average spasm duration of the first part to obtain a spasm intensity coefficient corresponding to the first spasm part, and name it the first spasm intensity coefficient;

[0150] Step S126: respectively acquiring the spasm intensity coefficients corresponding to the second spasm site to the mth spasm site, to obtain the spasm intensity coefficients of the second site to the mth site;

[0151] Step S127: Calculate the average of the spasm intensity coefficients of the first part to the mth part to obtain the average spasm intensity coefficient of the parts;

[0152] Step S128: calculating the spasm intensity coefficient of the first part to the mth part and the average of the spasm intensity coefficients of the parts to obtain the spasm intensity stability coefficient of the target child;

[0153] The stability coefficient of the spasm intensity of the target child is calculated, and the specific formula is configured as follows:

[0154]

[0155] Among them, Jqx is the stability coefficient of the spasm intensity of the target child, Jqs1 to Jqsm are the spasm intensity coefficients of the first to the mth part, Jlq is the average value of the spasm intensity coefficients of the parts, and m is the number of parts of the target child's lower limbs where spasms occur;

[0156] Step S13: obtaining the stability coefficient of the number of periodic spasms of the target child;

[0157] The step S13 further includes the following specific steps:

[0158] Step S131: Acquire historical treatment data of the target child, divide the treatment time period corresponding to the historical treatment data into k treatment monitoring cycles, and name them as the first treatment monitoring cycle to the kth treatment monitoring cycle respectively;

[0159] Step S132: obtaining the number of spasms corresponding to the target child in the first treatment monitoring cycle to the kth treatment monitoring cycle according to the historical treatment data, and obtaining the number of spasms in the first monitoring cycle to the kth monitoring cycle;

[0160] Step S133: Calculate the average of the number of spasms from the first monitoring cycle to the kth monitoring cycle to obtain the average number of spasms in the target child;

[0161] Step S134: calculating the spasm number from the first monitoring cycle to the kth monitoring cycle and the average spasm number of the target patient's cycle to obtain a stability coefficient of the spasm number of the target patient's cycle;

[0162] The stability coefficient of the number of periodic spasms in the target child is calculated, and the specific formula is configured as follows:

[0163]

[0164] Among them, Zcx is the stability coefficient of the number of spasms in the target child cycle, Zq1 to Zqk are the number of spasms in the first monitoring cycle to the number of spasms in the kth monitoring cycle, Zqp is the average number of spasms in the target child cycle, and k is the number value corresponding to the treatment monitoring cycle;

[0165] Step S14: defining the target patient's spasm intensity stability coefficient, the target patient's periodic spasm frequency stability coefficient, and historical training sample data as historical spasm incidence data;

[0166] Step S2: dividing the target spastic child into training intervals according to the historical spasticity incidence data, and obtaining the target spastic child training interval division data;

[0167] Step S21: Acquire historical spasm incidence data, and acquire the spasm incidence intensity stability coefficient of the target patient, the target patient periodic spasm frequency stability coefficient, and historical training sample data according to the historical spasm incidence data;

[0168] Step S22: obtaining the stability coefficient of the spasticity training duration of the target child according to the historical training sample data;

[0169] Step S23: calculating the stability coefficient of the target child's spasm intensity, the stability coefficient of the target child's periodic spasm number, and the stability coefficient of the target child's spasm training duration to obtain a common reference coefficient for spasm treatment;

[0170] The common reference coefficient for spasticity treatment is calculated, and the specific formula is configured as follows:

[0171] Bqw=Zcx+Jqx+Xlw;

[0172] Among them, Bqw is the common reference coefficient for spasticity treatment, Zcx is the stability coefficient for the number of periodic spasticity in the target child, Jqx is the stability coefficient for the intensity of spasticity in the target child, and Xlw is the stability coefficient for the duration of spasticity training in the target child;

[0173] Step S24: obtaining a common reference coefficient threshold value for spasticity treatment, and performing a threshold judgment on the common reference coefficient threshold value for spasticity treatment according to the common reference coefficient for spasticity treatment, dividing the target child into a first spasticity training classification interval or a second spasticity training classification interval, and obtaining training interval division data for the target child;

[0174] The step S24 further includes the following specific steps:

[0175] Step S241: respectively obtaining a target child's spasm intensity stability coefficient threshold, a target child's periodic spasm number stability coefficient threshold, and a target child's spasm training duration stability coefficient threshold;

[0176] Step S242: calculating the target child's spasm intensity stability coefficient threshold, the target child's periodic spasm number stability coefficient threshold, and the target child's spasm training duration stability coefficient threshold to obtain a spasm treatment common reference coefficient threshold;

[0177] Step S243: when the spasticity treatment commonality reference coefficient is less than or equal to the spasticity treatment commonality reference coefficient threshold, it is determined that the target child is in the first spasticity training classification interval;

[0178] Step S244: when the spasticity treatment commonality reference coefficient is greater than the spasticity treatment commonality reference coefficient threshold, it is determined that the target child is in the second spasticity training classification interval;

[0179] Step S3: performing lower limb spasticity training on the target child in the first spasticity training classification interval according to the target child training interval division data;

[0180] Step S31: Acquire historical spasm incidence data, and acquire historical training sample data according to the historical spasm incidence data;

[0181] Step S32: obtaining the average spasticity training duration of the target children according to the historical training sample data;

[0182] Step S33: Before training the target child, obtain the training time weight by performing electromyography monitoring on the target child;

[0183] The step S33 further includes the following specific steps:

[0184] Step S331: performing electromyographic monitoring on the lower limbs of the target child, marking the time range for the target to receive electromyographic monitoring as the electromyographic monitoring time range, and acquiring the electromyographic monitoring results corresponding to the electromyographic monitoring time range to obtain electromyographic monitoring data of the target child;

[0185] Step S332: obtaining the average myoelectric signal strength of the target child within the myoelectric monitoring time range according to the myoelectric monitoring data of the target child;

[0186] Step S333: obtaining the average EMG signal frequency of the target child within the EMG monitoring time range according to the EMG monitoring data of the target child;

[0187] Step S334: obtaining the average potential difference of the target child within the electromyography monitoring time range according to the electromyography monitoring data of the target child;

[0188] Step S335: Calculate the average electromyographic signal strength, the average EMG signal frequency, and the average potential difference to obtain the training duration weight;

[0189] The training duration weight is calculated, and the specific formula configuration is as follows:

[0190] Wx=(Jdp+Emg×b1+Dwc)×b2;

[0191] Wherein, Wx is the training duration weight, Jdp is the average electromyographic signal strength, Emg is the average EMG signal frequency, Dwc is the average potential difference, b1 and b2 are the set proportional coefficients and both b1 and b2 are greater than 0;

[0192] Step S34: Calculate the product of the average spasticity training duration of the target patient and the training duration weight to obtain the training duration of the target patient;

[0193] Step S35: performing lower limb spasticity training on the target child in the first spasticity training classification interval, and when the training duration reaches the training duration of the target child, the training is completed;

[0194] Step S4: performing lower limb spasticity training on the target child in the second spasticity training classification interval according to the target child training interval division data;

[0195] Step S41: Acquire historical spasm incidence data, and acquire historical training sample data according to the historical spasm incidence data;

[0196] Step S42: acquiring the first sample spasm recovery coefficient to the jth sample spasm recovery coefficient according to the historical training sample data;

[0197] Step S43: sorting the first sample spasm recovery coefficient to the jth sample spasm recovery coefficient according to the numerical values, marking the first sample spasm recovery coefficient as the first spasm recovery coefficient threshold, marking the last sample spasm recovery coefficient as the second spasm recovery coefficient threshold, and marking the numerical range between the first spasm recovery coefficient threshold and the second spasm recovery coefficient threshold as the spasm recovery coefficient reference interval;

[0198] Step S44: in the process of performing spasticity training on the target child in the second spasticity training classification interval, obtaining the real-time spasticity recovery coefficient corresponding to the target child in real time;

[0199] The step S44 further includes the following specific steps:

[0200] Step S441: Please refer to Figure 3 , obtain the EMG signal frequency corresponding to the training part of the child in real time during the training process through the electromyography monitoring device, and obtain the real-time EMG signal frequency;

[0201] Step S442: Obtain the Ashworth score of muscle tension corresponding to the lower limb spasticity training site of the target child before the spasticity training, and obtain a first real-time muscle tension score;

[0202] Step S443: while the target child is undergoing spasticity training, the muscle tension Ashworth score corresponding to the lower limb spasticity training site at the current moment is obtained to obtain a second real-time muscle tension score;

[0203] Step S444: obtaining a real-time spasm recovery coefficient by calculating the real-time EMG signal frequency, the first real-time muscle tension score, and the second real-time muscle tension score;

[0204] The real-time spasm recovery coefficient is calculated, and the specific formula is configured as follows:

[0205] Hfs=(Zps1-Zps2)+Egps*c1;

[0206] Wherein, Hfs is the real-time spasm recovery coefficient, Zps1 is the first real-time muscle tension score, Zps2 is the second real-time muscle tension score, Egps is the real-time EMG signal frequency, c1 is the set proportional coefficient and c1 is greater than 0;

[0207] Step S45: When the real-time spasm recovery coefficient corresponding to the target child is within the spasm recovery coefficient reference interval, the spasm training is completed;

[0208] Step S46: When the real-time spasm recovery coefficient corresponding to the target child is not in the spasm recovery coefficient reference interval, continue to perform spasm training on the target child until the real-time spasm recovery coefficient is in the spasm recovery coefficient reference interval.

[0209] In this application, if corresponding calculation formulas appear, the above calculation formulas are all dimensionless and take their numerical calculations. The weight coefficients, proportional coefficients and other coefficients in the formulas are set to a result value obtained by quantifying each parameter. The size of the weight coefficient and the proportional coefficient can be determined as long as it does not affect the proportional relationship between the parameter and the result value.

[0210] Embodiment 2

[0211] See also Figure 2, based on another concept of the same invention, a lower limb muscle spasticity training system for children with cerebral palsy based on treatment data is proposed, comprising a data acquisition module, a data analysis module, a first training module, a second training module and a server, wherein the data acquisition module, the data analysis module, the first training module and the second training module are respectively connected to the server, and the server controls the data acquisition module, the data analysis module, the first training module and the second training module respectively;

[0212] The data acquisition module acquires historical spasm incidence data;

[0213] The data acquisition module includes a spasm training unit, a spasm intensity unit, and a spasm frequency unit;

[0214] The spasticity training unit acquires and analyzes the historical data of spasticity training of the target child to obtain historical training sample data;

[0215] Obtain the target child's spasticity training history data, select j spasticity trainings from the spasticity training history data as sample spasticity trainings, and name them as the first sample spasticity training to the jth sample spasticity training, respectively;

[0216] It should be noted here that:

[0217] In the present application, j referred to herein is the quantity value corresponding to the selected sample spasticity training, and j is an integer greater than 0;

[0218] According to the spasm training history data, the single spasm training durations corresponding to the first sample spasm training to the j-th sample spasm training are obtained, and the duration of the first single spasm training to the j-th single spasm training is obtained;

[0219] The average of the first single spasm training duration to the jth single spasm training duration was calculated to obtain the average spasm training duration of the target children;

[0220] The stability coefficient of the spasm training duration of the target children is obtained by calculating the duration of the first single spasm training to the jth single spasm training duration and the average spasm training duration of the target children;

[0221] The stability coefficient of the spasticity training duration of the target children was calculated, and the specific formula configuration is as follows:

[0222]

[0223] Among them, Xlw is the stability coefficient of the spasm training duration of the target children, Xyp is the average spasm training duration of the target children, Xy1 to Xyj are the duration of the first single spasm training to the jth single spasm training, and j is the number value corresponding to the selected sample spasm training;

[0224] Acquiring the limb recovery coefficient corresponding to the first sample spasticity training to obtain the first sample spasticity recovery coefficient;

[0225] The details are as follows:

[0226] After the target child patient completes the first sample spasticity training, the EMG signal frequency corresponding to the training part of the child at the end of the training is obtained through the electromyography monitoring device to obtain the first EMG signal frequency;

[0227] Obtain the Ashworth score of muscle tension corresponding to the lower limb spasticity training site of the target child before the first sample spasticity training, and obtain the first muscle tension score;

[0228] Obtain the Ashworth score of muscle tension corresponding to the lower limb spasticity training site of the target child after completing the first sample spasticity training, and obtain the second muscle tension score;

[0229] It should be noted here that:

[0230] The Ashworth score involved here is a subjective scale for assessing muscle tone, which is widely used in neurology and rehabilitation;

[0231] Among them, the Ashworth score comparison is as follows:

[0232] 0 points: No increased muscle tone (normal).

[0233] 1 point: Mild increase in muscle tone with slight resistance to movement that is easily overcome during voluntary movement.

[0234] 2 points: Moderate increase in muscle tone with marked resistance to movement, but still able to move during voluntary movements.

[0235] 3 points: Significant increase in tension, greater resistance to movement during activity, and even a noticeable sense of resistance during passive movement.

[0236] 4 points: Extreme tension, the muscles are in a constant state of spasm, and passive movement is almost impossible.

[0237] The first sample spasm recovery coefficient is obtained by calculating the first EMG signal frequency, the first muscle tension score and the second muscle tension score;

[0238] The spasm recovery coefficient of the first sample is calculated as follows:

[0239] Hf1=(Zp1-Zp2)+Egp*c1;

[0240] Wherein, Hf1 is the first sample spasm recovery coefficient, Zp1 is the first muscle tension score, Zp2 is the second muscle tension score, Egp is the first EMG signal frequency, c1 is the set proportional coefficient and c1 is greater than 0;

[0241] The limb recovery coefficients corresponding to the second sample spasticity training to the j-th sample spasticity training are respectively obtained to obtain the second sample spasticity recovery coefficient to the j-th sample spasticity recovery coefficient;

[0242] The spasm recovery coefficients of the first sample to the jth sample, the average spasm training duration of the target children, and the spasm training duration stability coefficient of the target children are defined as historical training sample data;

[0243] The spasm intensity unit analyzes the spasm intensity of the target child at the historical spasm site and obtains the spasm intensity stability coefficient of the target child;

[0244] The details are as follows:

[0245] Obtain historical treatment data of the target child, obtain the body parts of the target child where the lower limbs have spasms according to the historical treatment data, and mark the body parts where the spasms occur as the first spasm part to the mth spasm part respectively;

[0246] It should be noted here that:

[0247] In the present application, m specifically refers to the number of locations where spasticity occurs in the lower limbs of the target child, and m is an integer greater than 0;

[0248] For example, if the target child has cramps in the lower limbs including the knee, calf, thigh and ankle, the first cramp site here can be the knee, the second cramp site can be the calf, the third cramp site can be the thigh, and the fourth cramp site can be the thigh;

[0249] According to the historical treatment data, the number of spasms corresponding to the first spasm site to the mth spasm site is obtained, and the number of spasms from the first site to the mth site is obtained;

[0250] The average single spasm duration of the first spasm site is obtained as follows:

[0251] For the first spasm site, n spasms are selected as sample monitoring spasms, and they are marked as the first sample monitoring spasm to the nth sample monitoring spasm respectively;

[0252] Obtain the single duration corresponding to the spasm monitored from the first sample to the spasm monitored from the nth sample, and obtain the duration of the first spasm to the duration of the nth spasm;

[0253] The average duration of the first spasm to the nth spasm is calculated to obtain the average duration of a single spasm corresponding to the first spasm site, and is named the average duration of the first spasm site;

[0254] The single average spasm durations corresponding to the second spasm site to the mth spasm site are obtained respectively, and the average spasm duration of the second site to the average spasm duration of the mth site are obtained;

[0255] The product of the number of spasms at the first part and the average spasm duration at the first part is calculated to obtain the spasm intensity coefficient corresponding to the first spasm part, and the coefficient is named the spasm intensity coefficient at the first part;

[0256] The spasm intensity coefficients corresponding to the second spasm site to the mth spasm site are respectively obtained to obtain the spasm intensity coefficients of the second site to the mth site;

[0257] Calculate the average of the spasm intensity coefficients of the first part to the mth part to obtain the average spasm intensity coefficient of each part;

[0258] The spasm intensity stability coefficient of the target child is obtained by calculating the spasm intensity coefficient of the first part to the mth part and the average of the spasm intensity coefficients of the parts;

[0259] The stability coefficient of the spasm intensity of the target child is calculated, and the specific formula is configured as follows:

[0260]

[0261] Among them, Jqx is the stability coefficient of the spasm intensity of the target child, Jqs1 to Jqsm are the spasm intensity coefficients of the first to the mth part, Jlq is the average value of the spasm intensity coefficients of the parts, and m is the number of parts of the target child's lower limbs where spasms occur;

[0262] The spasm frequency unit obtains the stability coefficient of the number of periodic spasms in the target child;

[0263] The details are as follows:

[0264] Obtain historical treatment data of the target child, divide the treatment time period corresponding to the historical treatment data into k treatment monitoring cycles, and name them as the first treatment monitoring cycle to the kth treatment monitoring cycle respectively;

[0265] According to the historical treatment data, the number of spasms of the target child corresponding to the first treatment monitoring period to the kth treatment monitoring period is obtained, and the number of spasms from the first monitoring period to the kth monitoring period is obtained;

[0266] The average number of spasms from the first monitoring cycle to the kth monitoring cycle is calculated to obtain the average number of spasms in the target child;

[0267] The spasm number from the first monitoring cycle to the kth monitoring cycle and the average spasm number of the target patient in the cycle are calculated to obtain the spasm number stability coefficient of the target patient in the cycle;

[0268] The stability coefficient of the number of periodic spasms in the target child is calculated, and the specific formula is configured as follows:

[0269]

[0270] Among them, Zcx is the stability coefficient of the number of spasms in the target child cycle, Zq1 to Zqk are the number of spasms in the first monitoring cycle to the number of spasms in the kth monitoring cycle, Zqp is the average number of spasms in the target child cycle, and k is the number value corresponding to the treatment monitoring cycle;

[0271] The stability coefficient of spasm intensity of target patients, the stability coefficient of periodic spasm number of target patients and historical training sample data are defined as historical spasm incidence data;

[0272] The data acquisition module acquires historical spasm incidence data and transmits it to the data analysis module, the first training module and the second training module;

[0273] The data analysis module divides the target spastic children into training intervals according to the historical spasticity incidence data, and obtains the training interval division data of the target children;

[0274] Obtain historical spasm incidence data, and obtain the spasm incidence intensity stability coefficient of the target patient, the target patient periodic spasm number stability coefficient, and historical training sample data according to the historical spasm incidence data;

[0275] Obtain the stability coefficient of the duration of spasticity training for target children based on historical training sample data;

[0276] The common reference coefficient of spasticity treatment is obtained by calculating the stability coefficient of the target child's spasticity intensity, the stability coefficient of the target child's periodic spasticity number, and the stability coefficient of the target child's spasticity training duration;

[0277] The common reference coefficient for spasticity treatment is calculated, and the specific formula is configured as follows:

[0278] Bqw=Zcx+Jqx+Xlw;

[0279] Among them, Bqw is the common reference coefficient for spasticity treatment, Zcx is the stability coefficient for the number of periodic spasticity in the target child, Jqx is the stability coefficient for the intensity of spasticity in the target child, and Xlw is the stability coefficient for the duration of spasticity training in the target child;

[0280] Obtaining a common reference coefficient threshold for spasticity treatment, and performing threshold judgment on the common reference coefficient threshold for spasticity treatment according to the common reference coefficient for spasticity treatment, dividing the target child into the first spasticity training classification interval or the second spasticity training classification interval, and obtaining training interval division data for the target child;

[0281] The common reference coefficient thresholds for spasticity treatment are obtained as follows:

[0282] The target children's spasm intensity stability coefficient threshold, the target children's periodic spasm number stability coefficient threshold and the target children's spasm training duration stability coefficient threshold are obtained respectively;

[0283] The common reference coefficient threshold for spasm treatment is obtained by calculating the stability coefficient threshold of spasm intensity, the stability coefficient threshold of periodic spasm number and the stability coefficient threshold of spasm training duration of the target child;

[0284] The common reference coefficient threshold for spasticity treatment is calculated, and the specific formula configuration is as follows:

[0285] Bqwy=Zcxy+Jqxy+Xlwy;

[0286] Among them, Bqwy is the common reference coefficient threshold for spasm treatment, Zcxy is the stability coefficient threshold for the number of periodic spasms in the target child, Jqxy is the stability coefficient threshold for the intensity of spasm in the target child, and Xlwy is the stability coefficient threshold for the duration of spasm training in the target child;

[0287] The threshold value of the common reference coefficient for spasticity treatment is determined according to the common reference coefficient for spasticity treatment, as follows:

[0288] When the commonality reference coefficient of spasticity treatment is less than or equal to the commonality reference coefficient threshold of spasticity treatment, the target child is judged to be in the first spasticity training classification interval;

[0289] When the commonality reference coefficient of spasticity treatment is greater than the commonality reference coefficient threshold of spasticity treatment, the target child is judged to be in the second spasticity training classification interval;

[0290] It should be noted here that:

[0291] In the present application, the condition of children in the first spasticity training classification interval is more stable than that of children in the second spasticity training classification interval, so the lower limb spasticity training methods carried out on them are more common;

[0292] The data analysis module acquires the training interval division data of the target child and transmits it to the first training module and the second training module;

[0293] The first training module performs lower limb spasticity training on the target child in the first spasticity training classification interval according to the target child's training interval division data;

[0294] Acquire historical spasm incidence data, and acquire historical training sample data based on the historical spasm incidence data;

[0295] The average spasticity training duration of the target children was obtained based on historical training sample data;

[0296] Before training the target children, the training duration weight was obtained through electromyography monitoring of the target children;

[0297] The details are as follows:

[0298] Performing electromyographic monitoring on the lower limbs of the target child, marking the time range in which the target receives electromyographic monitoring as the electromyographic monitoring time range, and acquiring the electromyographic monitoring results corresponding to the electromyographic monitoring time range to obtain the electromyographic monitoring data of the target child;

[0299] According to the electromyographic monitoring data of the target child, the average electromyographic signal strength of the target child within the electromyographic monitoring time range is obtained;

[0300] According to the electromyography monitoring data of the target child, the average EMG signal frequency of the target child within the electromyography monitoring time range is obtained;

[0301] According to the electromyography monitoring data of the target child, the average potential difference of the target child within the electromyography monitoring time range is obtained;

[0302] The average electromyographic signal intensity, average EMG signal frequency and average potential difference are calculated to obtain the training duration weight;

[0303] The training duration weight is calculated, and the specific formula configuration is as follows:

[0304] Wx=(Jdp+Emg×b1+Dwc)×b2;

[0305] Wherein, Wx is the training duration weight, Jdp is the average electromyographic signal strength, Emg is the average EMG signal frequency, Dwc is the average potential difference, b1 and b2 are the set proportional coefficients and both b1 and b2 are greater than 0;

[0306] Calculate the product of the average spasticity training time of the target children and the training time weight to obtain the training time of the target children;

[0307] The target children in the first spasticity training classification interval were given lower limb spasticity training. When the training duration reached the target children's training duration, the training was completed.

[0308] The second training module performs lower limb spasticity training on the target children in the second spasticity training classification interval according to the training interval division data of the target children;

[0309] Acquire historical spasm incidence data, and acquire historical training sample data based on the historical spasm incidence data;

[0310] Obtaining the first sample spasm recovery coefficient to the jth sample spasm recovery coefficient according to the historical training sample data;

[0311] Sort the spasm recovery coefficients of the first sample to the jth sample according to their numerical values, mark the spasm recovery coefficient of the first sample as the first spasm recovery coefficient threshold, mark the spasm recovery coefficient of the last sample as the second spasm recovery coefficient threshold, and mark the numerical range between the first spasm recovery coefficient threshold and the second spasm recovery coefficient threshold as the spasm recovery coefficient reference interval;

[0312] During the process of performing spasticity training on the target child in the second spasticity training classification interval, a real-time spasticity recovery coefficient corresponding to the target child is obtained in real time;

[0313] The details are as follows:

[0314] The EMG signal frequency corresponding to the training part of the child in real time during the training process is obtained by using the electromyography monitoring device to obtain the real-time EMG signal frequency;

[0315] Obtain the Ashworth score of muscle tension corresponding to the lower limb spasticity training site of the target child before the spasticity training, and obtain the first real-time muscle tension score;

[0316] When the target child is undergoing spasticity training, the muscle tension Ashworth score corresponding to the lower limb spasticity training site at the current moment is obtained to obtain a second real-time muscle tension score;

[0317] The real-time EMG signal frequency, the first real-time muscle tension score and the second real-time muscle tension score are calculated to obtain a real-time spasm recovery coefficient;

[0318] The real-time spasm recovery coefficient is calculated, and the specific formula is configured as follows:

[0319] Hfs=(Zps1-Zps2)+Egps*c1;

[0320] Wherein, Hfs is the real-time spasm recovery coefficient, Zps1 is the first real-time muscle tension score, Zps2 is the second real-time muscle tension score, Egps is the real-time EMG signal frequency, c1 is the set proportional coefficient and c1 is greater than 0;

[0321] When the real-time spasm recovery coefficient corresponding to the target child is within the spasm recovery coefficient baseline range, the spasm training is completed;

[0322] When the real-time spasm recovery coefficient corresponding to the target child is not within the spasm recovery coefficient reference interval, the target child continues to be subjected to spasm training until the real-time spasm recovery coefficient is within the spasm recovery coefficient reference interval;

[0323] It should be noted here that:

[0324] In the present application, when determining that the real-time spasm recovery coefficient corresponding to the target child is within the spasm recovery coefficient reference interval, the interval boundary is included.

[0325] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for training lower limb muscle spasticity in children with cerebral palsy based on treatment data, characterized in that: include: Step S1: respectively obtaining the first sample spasm recovery coefficient to the jth sample spasm recovery coefficient corresponding to the target child, the average spasm training time of the target child, and the spasm training time stability coefficient of the target child, obtaining historical training sample data, obtaining the spasm onset intensity stability coefficient of the target child and the target child's periodic spasm number stability coefficient, and defining the historical training sample data, the target child's spasm onset intensity stability coefficient, and the target child's periodic spasm number stability coefficient as historical spasm onset data; Step S2: Calculate the common reference coefficient for spasticity treatment based on the historical spasticity incidence data, obtain the common reference coefficient threshold for spasticity treatment, perform threshold judgment on the common reference coefficient for spasticity treatment, divide the target child into the first spasticity training classification interval or the second spasticity training classification interval, and obtain the training interval division data for the target child; Step S3: obtaining the average spasm training time of the target children according to the historical spasm incidence data, obtaining the average electromyographic signal strength, the average EMG signal frequency and the average potential difference, and calculating the training time weight, calculating the average spasm training time and the training time weight of the target children, and obtaining the training time of the target children at that time, and performing lower limb spasticity training on the target children in the first spasm training classification interval according to the training time of the target children at that time; Step S4: Analyze the historical spasm incidence data to obtain a spasm recovery coefficient benchmark interval, obtain the real-time EMG signal frequency, the first real-time muscle tension score, and the second real-time muscle tension score of the target child in the first spasm training classification interval, calculate the real-time spasm recovery coefficient, and perform lower limb spasticity training on the target child in the second spasm training classification interval based on the real-time spasm recovery coefficient and the spasm recovery coefficient benchmark interval.

2. The method for training lower limb muscle spasm in children with cerebral palsy based on treatment data according to claim 1, characterized in that: The step S1 further includes the following specific steps: Step S1: acquiring and analyzing the historical data of spasticity training of the target cerebral palsy child to obtain historical training sample data; Step S12: Analyze the historical spasm intensity of the target child to obtain the spasm intensity stability coefficient of the target child; Step S13: obtaining the stability coefficient of the number of periodic spasms of the target child; Step S14: defining the target patient's spasm intensity stability coefficient, the target patient's periodic spasm frequency stability coefficient, and historical training sample data as historical spasm incidence data; The step S13 further includes the following specific steps: Obtain historical treatment data of the target child, divide the treatment time period corresponding to the historical treatment data into k treatment monitoring cycles, and name them as the first treatment monitoring cycle to the kth treatment monitoring cycle respectively; According to the historical treatment data, the number of spasms of the target child corresponding to the first treatment monitoring period to the kth treatment monitoring period is obtained, and the number of spasms from the first monitoring period to the kth monitoring period is obtained; The average number of spasms from the first monitoring cycle to the kth monitoring cycle is calculated to obtain the average number of spasms in the target child; The stability coefficient of the spasm number in the target child's cycle is obtained by calculating the spasm number from the first monitoring cycle to the kth monitoring cycle and the average spasm number in the target child's cycle.

3. The method for training lower limb muscle spasm in children with cerebral palsy based on treatment data according to claim 2, characterized in that: The step S11 further includes the following specific steps: Step S111: acquiring the historical data of spasticity training of the target cerebral palsy child, selecting j spasticity trainings from the historical data of spasticity training as sample spasticity trainings, and naming them as the first sample spasticity training to the jth sample spasticity training respectively; Step S112: acquiring the single spasm training durations corresponding to the first sample spasm training to the j-th sample spasm training according to the spasm training history data, and obtaining the first single spasm training duration to the j-th single spasm training duration; Step S113: Calculate the average of the first single spasm training duration to the jth single spasm training duration to obtain the average spasm training duration of the target child; Step S114: calculating the stability coefficient of the spasm training duration of the target child by combining the first single spasm training duration to the jth single spasm training duration and the average spasm training duration of the target child; The stability coefficient of the spasticity training duration of the target children was calculated, and the specific formula configuration is as follows: Among them, Xlw is the stability coefficient of the spasm training duration of the target children, Xyp is the average spasm training duration of the target children, Xy1 to Xyj are the duration of the first single spasm training to the jth single spasm training, and j is the number value corresponding to the sample spasm training; Step S115: acquiring the limb recovery coefficient corresponding to the first sample spasticity training to obtain the first sample spasticity recovery coefficient; Step S116: respectively acquiring the limb recovery coefficients corresponding to the second sample spasticity training to the j-th sample spasticity training to obtain the second sample spasticity recovery coefficient to the j-th sample spasticity recovery coefficient; Step S117: defining the first sample spasm recovery coefficient to the jth sample spasm recovery coefficient, the average spasm training duration of the target patient, and the spasm training duration stability coefficient of the target patient as historical training sample data; The step S115 further includes the following specific steps: After the target child patient completes the first sample spasticity training, the EMG signal frequency corresponding to the training part of the child at the end of the training is obtained through the electromyography monitoring device to obtain the first EMG signal frequency; Obtain the Ashworth score of muscle tension corresponding to the lower limb spasticity training site of the target child before the first sample spasticity training, and obtain the first muscle tension score; Obtain the Ashworth score of muscle tension corresponding to the lower limb spasticity training site of the target child after completing the first sample spasticity training, and obtain the second muscle tension score; The first sample spasm recovery coefficient is obtained by calculating the first EMG signal frequency, the first muscle tension score and the second muscle tension score; The spasm recovery coefficient of the first sample is calculated as follows: Hf1=(Zp1-Zp2)+Egp*c1; Wherein, Hf1 is the first sample spasm recovery coefficient, Zp1 is the first muscle tension score, Zp2 is the second muscle tension score, Egp is the first EMG signal frequency, c1 is the set proportional coefficient and c1 is greater than 0.

4. The method for training lower limb muscle spasm in children with cerebral palsy based on treatment data according to claim 2, characterized in that: The step S12 further includes the following specific steps: Step S121: acquiring historical treatment data of the target child, acquiring the body parts where the lower limbs of the target child have cramps according to the historical treatment data, and marking the body parts where the cramps occur as the first cramp part to the mth cramp part respectively; Step S122: acquiring the number of spasm occurrences corresponding to the first spasm site to the mth spasm site respectively according to the historical treatment data, and obtaining the number of spasm occurrences from the first site to the mth site; Step S123: obtaining the average single spasm duration of the first spasm site, and obtaining the average spasm duration of the first site; The details are as follows: For the first spasm site, n spasms are selected as sample monitoring spasms, and they are marked as the first sample monitoring spasm to the nth sample monitoring spasm respectively; Obtain the single duration corresponding to the spasm monitored from the first sample to the spasm monitored from the nth sample, and obtain the duration of the first spasm to the duration of the nth spasm; The average duration of the first spasm to the nth spasm is calculated to obtain the average duration of a single spasm corresponding to the first spasm site, and is named the average duration of the first spasm site; Step S124: respectively obtaining the single average spasm durations corresponding to the second spasm site to the mth spasm site, and obtaining the average spasm duration of the second site to the average spasm duration of the mth site; Step S125: Calculate the product of the number of spasms at the first part and the average spasm duration of the first part to obtain a spasm intensity coefficient corresponding to the first spasm part, and name it the first spasm intensity coefficient; Step S126: respectively acquiring the spasm intensity coefficients corresponding to the second spasm site to the mth spasm site, to obtain the spasm intensity coefficients of the second site to the mth site; Step S127: Calculate the average of the spasm intensity coefficients of the first part to the mth part to obtain the average spasm intensity coefficient of the parts; Step S128: The spasm intensity stability coefficient of the target child is obtained by calculating the spasm intensity coefficients of the first part to the mth part and the average of the spasm intensity coefficients of the parts.

5. The method for training lower limb muscle spasm in children with cerebral palsy based on treatment data according to claim 1, characterized in that: The step S2 further includes the following specific steps: Step S21: Acquire historical spasm incidence data, and acquire the spasm incidence intensity stability coefficient of the target patient, the target patient periodic spasm frequency stability coefficient, and historical training sample data according to the historical spasm incidence data; Step S22: obtaining the stability coefficient of the spasticity training duration of the target child according to the historical training sample data; Step S23: calculating the stability coefficient of the target child's spasm intensity, the stability coefficient of the target child's periodic spasm number, and the stability coefficient of the target child's spasm training duration to obtain a common reference coefficient for spasm treatment; The common reference coefficient for spasticity treatment is calculated, and the specific formula is configured as follows: Bqw=Zcx+Jqx+Xlw; Among them, Bqw is the common reference coefficient for spasticity treatment, Zcx is the stability coefficient for the number of periodic spasticity in the target child, Jqx is the stability coefficient for the intensity of spasticity in the target child, and Xlw is the stability coefficient for the duration of spasticity training in the target child; Step S24: Obtain the common reference coefficient threshold for spasticity treatment, and perform threshold judgment on the common reference coefficient threshold for spasticity treatment according to the common reference coefficient for spasticity treatment, divide the target child into the first spasticity training classification interval or the second spasticity training classification interval, and obtain the training interval division data for the target child.

6. The method for training lower limb muscle spasm in children with cerebral palsy based on treatment data according to claim 5, characterized in that: The step S24 further includes the following specific steps: Step S241: respectively obtaining a target child's spasm intensity stability coefficient threshold, a target child's periodic spasm number stability coefficient threshold, and a target child's spasm training duration stability coefficient threshold; Step S242: calculating the target child's spasm intensity stability coefficient threshold, the target child's periodic spasm number stability coefficient threshold, and the target child's spasm training duration stability coefficient threshold to obtain a spasm treatment common reference coefficient threshold; Step S243: when the spasticity treatment commonality reference coefficient is less than or equal to the spasticity treatment commonality reference coefficient threshold, it is determined that the target child is in the first spasticity training classification interval; Step S244: When the common reference coefficient for spasticity treatment is greater than the common reference coefficient threshold for spasticity treatment, it is determined that the target child is in the second spasticity training classification interval.

7. The method for training lower limb muscle spasm in children with cerebral palsy based on treatment data according to claim 1, characterized in that: The step S3 further includes the following specific steps: Step S31: Acquire historical spasm incidence data, and acquire historical training sample data according to the historical spasm incidence data; Step S32: obtaining the average spasticity training duration of the target children according to the historical training sample data; Step S33: Before training the target child, obtain the training time weight by performing electromyography monitoring on the target child; Step S34: Calculate the product of the average spasticity training duration of the target patient and the training duration weight to obtain the training duration of the target patient; Step S35: Perform lower limb spasticity training on the target child in the first spasticity training classification interval. When the training duration reaches the current training duration of the target child, the training is completed.

8. The method for training lower limb muscle spasm in children with cerebral palsy based on treatment data according to claim 7, characterized in that: The step S33 further includes the following specific steps: Step S331: performing electromyographic monitoring on the lower limbs of the target child, marking the time range in which the target receives electromyographic monitoring as the electromyographic monitoring time range, and acquiring the electromyographic monitoring result corresponding to the electromyographic monitoring time range to obtain the electromyographic monitoring data of the target child; Step S332: obtaining the average myoelectric signal strength of the target child within the myoelectric monitoring time range according to the myoelectric monitoring data of the target child; Step S333: obtaining the average EMG signal frequency of the target child within the EMG monitoring time range according to the EMG monitoring data of the target child; Step S334: obtaining the average potential difference of the target child within the electromyography monitoring time range according to the electromyography monitoring data of the target child; Step S335: Calculate the average electromyographic signal strength, the average EMG signal frequency, and the average potential difference to obtain the training duration weight; The training duration weight is calculated, and the specific formula configuration is as follows: Wx=(Jdp+Emg×b1+Dwc)×b2; Among them, Wx is the training duration weight, Jdp is the average electromyographic signal strength, Emg is the average EMG signal frequency, Dwc is the average potential difference, b1 and b2 are the set proportional coefficients and both b1 and b2 are greater than 0.

9. The method for training lower limb muscle spasm in children with cerebral palsy based on treatment data according to claim 1, characterized in that: The step S4 further includes the following specific steps: Step S41: Acquire historical spasm incidence data, and acquire historical training sample data according to the historical spasm incidence data; Step S42: acquiring the first sample spasm recovery coefficient to the jth sample spasm recovery coefficient according to the historical training sample data; Step S43: sorting the first sample spasm recovery coefficient to the jth sample spasm recovery coefficient according to the numerical values, marking the first sample spasm recovery coefficient as the first spasm recovery coefficient threshold, marking the last sample spasm recovery coefficient as the second spasm recovery coefficient threshold, and marking the numerical range between the first spasm recovery coefficient threshold and the second spasm recovery coefficient threshold as the spasm recovery coefficient reference interval; Step S44: in the process of performing spasticity training on the target child in the second spasticity training classification interval, obtaining the real-time spasticity recovery coefficient corresponding to the target child in real time; The step S44 further includes the following specific steps: The EMG signal frequency corresponding to the training part of the child in real time during the training process is obtained by using the electromyography monitoring device to obtain the real-time EMG signal frequency; Obtain the Ashworth score of muscle tension corresponding to the lower limb spasticity training site of the target child before the spasticity training, and obtain the first real-time muscle tension score; When the target child is undergoing spasticity training, the muscle tension Ashworth score corresponding to the lower limb spasticity training site at the current moment is obtained to obtain a second real-time muscle tension score; The real-time EMG signal frequency, the first real-time muscle tension score and the second real-time muscle tension score are calculated to obtain a real-time spasm recovery coefficient; The real-time spasm recovery coefficient is calculated, and the specific formula is configured as follows: Hfs=(Zps1-Zps2)+Egps*c1; Wherein, Hfs is the real-time spasm recovery coefficient, Zps1 is the first real-time muscle tension score, Zps2 is the second real-time muscle tension score, Egps is the real-time EMG signal frequency, c1 is the set proportional coefficient and c1 is greater than 0; Step S45: When the real-time spasm recovery coefficient corresponding to the target child is within the spasm recovery coefficient reference interval, the spasm training is completed; Step S46: When the real-time spasm recovery coefficient corresponding to the target child is not in the spasm recovery coefficient reference interval, continue to perform spasm training on the target child until the real-time spasm recovery coefficient is in the spasm recovery coefficient reference interval.

10. A lower limb muscle spasm training system for children with cerebral palsy based on treatment data, applicable to the lower limb muscle spasm training method for children with cerebral palsy based on treatment data as claimed in any one of claims 1 to 9, characterized in that: The training system comprises: Data acquisition module: used to obtain historical spasm incidence data; Data analysis module: used to divide the target spastic children into training intervals according to the historical spasticity incidence data, and obtain the training interval division data of the target children; The first training module is used to perform lower limb spasticity training on target children in the first spasticity training classification interval according to the training interval division data of the target children; The second training module is used to perform lower limb spasticity training on target children in the second spasticity training classification interval according to the training interval division data of the target children.

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