A training-based action rehabilitation method

Abstract

The present invention discloses a training-based movement rehabilitation method, which relates to the field of medical rehabilitation. The main solutions are as follows: formulating joint training plans, strength training plans, and balance training plans according to the patient's physical state data and sensor data, and selecting the difficulty level of the virtual scenario according to the joint training plan, strength training plan, and balance training plan, solving the problem of the lack of an objective evaluation standard; adjusting the difficulty level of the virtual scenario according to the action accuracy Q n , the qualified threshold QA, and the good threshold QB, solving the problem of unreasonable training intensity and progress; evaluating the patient's rehabilitation degree according to the patient's rehabilitation index μ n and the set of rehabilitation index thresholds, being able to clarify the rehabilitation stage where the patient is located, being beneficial for the patient and the rehabilitation therapist to understand the rehabilitation effect, and solving the problem of being difficult to accurately quantify the patient's rehabilitation progress.

Description

Technical Field

[0001] The present invention relates to the technical field of medical rehabilitation, and in particular to a movement rehabilitation method based on training. Background Art

[0002] With the increasing popularity of transportation and changes in people's lifestyles, the frequency of traffic accidents and sports injuries is also increasing. People injured in traffic accidents and athletes injured due to improper exercise are in urgent need of effective rehabilitation methods to restore body functions.

[0003] Traditional rehabilitation methods usually rely on doctors' subjective observations and simple scale assessments, and use a unified training model for rehabilitation treatment.

[0004] However, traditional rehabilitation methods lack objective evaluation indicators, resulting in unreasonable training intensity and progress, making it difficult to accurately quantify the patient's rehabilitation progress and unable to meet the personalized needs of different patients, which in turn affects the rehabilitation effect. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the shortcomings of the prior art, the present invention provides a training-based movement rehabilitation method, which solves the problems of lack of objective evaluation indicators and inability to meet the personalized needs of different patients by formulating a rehabilitation training plan and selecting the difficulty level of a virtual training scene according to the adjusted joint training intensity DA, the adjusted strength training intensity GA and the adjusted balance training difficulty HA; solves the problems of unreasonable training intensity and progress by timely adjusting the difficulty level of the virtual scene; and solves the problem of difficulty in accurately quantifying the patient's rehabilitation progress by evaluating the patient's rehabilitation degree.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a training-based action rehabilitation method, characterized in that it includes the following steps:

[0009] Collect patients’ personal information data, physical condition data, sensor data, and psychological state data;

[0010] Calculate joint training intensity D, strength training intensity G and balance training difficulty H based on the patient's personal information data and physical condition data;

[0011] The physiological index coefficient K and the adjustment coefficient θ are calculated according to the patient's physical state data and sensor data, and the adjusted joint training intensity DA, the adjusted strength training intensity GA and the adjusted balance training difficulty HA are calculated in combination with the joint training intensity D, the strength training intensity G and the balance training difficulty H;

[0012] Preset a joint training threshold set; formulate a joint training plan based on the comparison result between the adjusted joint training intensity DA and the joint training threshold set;

[0013] Preset a strength training threshold set; formulate a strength training plan based on the comparison result between the adjusted strength training intensity GA and the strength training threshold set;

[0014] Preset a balance training threshold set; formulate a balance training plan according to the comparison result between the adjusted balance training difficulty HA and the balance training threshold set;

[0015] Select the difficulty level of the virtual scenario according to the joint training plan, strength training plan and balance training plan;

[0016] Calculate the movement accuracy Q based on the patient's sensor data n ; Preset qualified threshold QA and good threshold QB, according to the action accuracy Q n , qualified threshold QA and good threshold QB to adjust the difficulty level of the virtual scene;

[0017] Based on the action accuracy Q n Calculate the action improvement X n ; Calculate the psychological state improvement degree Z based on the psychological state data n , and improve the degree X through action n and improvement in mental status Z n Further calculate the patient recovery index μ n ; Preset the rehabilitation index threshold set, according to the patient's rehabilitation index μ n And the recovery index threshold set is used to evaluate the patient's recovery degree.

[0018] Preferably, in the above-mentioned training-based action rehabilitation method: the method for calculating the joint training intensity D is:

[0019] Personal information data at least includes the patient's actual age 1 ;

[0020] According to the actual age of the patient 1 The age coefficient A is calculated based on the following formula:

[0021]

[0022] Among them, A 2 is the base age; A 3 is the age adjustment range; α 1 is the age influence factor, with a value of 0.01 to 0.1;

[0023] The body status data at least includes the patient's joint activity angle value B a ;

[0024]

[0025] Among them, B a is the active angle value of the a-th joint, a represents the serial number of different joints, and the value is a positive integer; B (a,max) is the maximum normal motion angle value of the ath joint; is the adjustment coefficient of the activity angle ratio of the ath joint, ranging from 0 to 1;

[0026] The formula for calculating joint training intensity D is based on the age coefficient A and the comprehensive index B of limb range of motion:

[0027] D=A×β c ×B×C

[0028] Among them, C is the basic joint training intensity; β c is the gender influencing factor, c is the gender number, and its values ​​are 1 and 2. When c is 1, the corresponding patient gender is female, and when c is 2, the corresponding patient gender is male.

[0029] Preferably, in the above-mentioned training-based action rehabilitation method: the method for calculating the strength training intensity G is:

[0030] The physical condition data also includes the measured strength value E of each muscle group i ;

[0031] According to the measured strength value E of each muscle group i The formula for calculating the comprehensive muscle strength index F is:

[0032]

[0033] Among them, E i is the measured strength value of the i-th muscle group, i represents the sequence number of different muscle groups, and its value is a positive integer; E (i,normal) is the normal strength value of the i-th muscle group; τ i is the strength ratio adjustment coefficient of the i-th muscle group, ranging from 0.15 to 0.35;

[0034] According to the age coefficient A, gender influence factor β c The strength training intensity G is calculated based on the comprehensive muscle strength index F, and the formula is:

[0035] G=A×β c ×F×G 0

[0036] Among them, G 0 Basic strength training intensity.

[0037] Preferably, in the above-mentioned training-based movement rehabilitation method: the method for calculating the balance training difficulty H is:

[0038] The patient's physical condition data also includes fracture recovery progress I 1 and expected recovery progress I 2 ;

[0039] According to the fracture recovery progress I 1 Expected recovery progress 2 The calculation of fracture recovery degree I is based on the formula:

[0040]

[0041] Among them, γ p is the importance coefficient of the pth fracture site, ranging from 0.1 to 1;

[0042] The patient's physical condition data also includes the current neurological function assessment value M 1 and pre-injury neurological function assessment value M 2 ;

[0043] According to the current neurological function assessment value M 1 and pre-injury neurological function assessment value M 2 The formula for calculating the neurological function recovery coefficient M is as follows:

[0044]

[0045] Among them, M q is the importance coefficient of the qth nerve injury site, ranging from 0.2 to 1;

[0046] According to the age coefficient A, gender influence factor β c , fracture recovery degree I and nerve function recovery coefficient M to calculate the balance training difficulty H, based on the formula:

[0047] H=A×β c ×I×M×H 0

[0048] Among them, H 0 Balance the difficulty of training.

[0049] Preferably, in the above-mentioned training-based action rehabilitation method: the method for calculating the physiological index coefficient K is:

[0050] The sensor data includes at least the actual heart rate value a 1 , actual blood pressure value b 1 and actual respiratory rate c 1 ;

[0051] According to the actual heart rate value a 1 , actual blood pressure value b 1 and actual respiratory rate c 1 The formula for calculating the physiological index coefficient K is:

[0052]

[0053] Among them, a 2 is the normal heart rate value; v 2 is the normal blood pressure value; c 2 is the normal respiratory rate; 1 is the heart rate risk coefficient, ranging from 0.3 to 0.5; ε 2 is the blood pressure risk coefficient, ranging from 0.2 to 0.5; ε 3 is the respiratory rate risk coefficient, ranging from 0.2 to 0.5; and ε 1 +ε 2 +ε 3 =1;

[0054] The method for calculating the adjustment coefficient θ is:

[0055] The sensor data also includes pressure sensor data L 1 and electromyography sensor data L 2 ;

[0056] According to the pressure sensor data L 1 and electromyography sensor data L 2 The adjustment factor θ is calculated based on the formula:

[0057] θ=L 1 ×θ 1 +L 2 ×θ 2

[0058] Among them, θ 1 is the pressure sensor data L 1 The weight coefficient is 0.3~0.6; θ 2 is the electromyography sensor data L 2 The weight coefficient is 0.4 to 0.7; and θ 1 +θ 2 =1.

[0059] Preferably, in the above-mentioned training-based action rehabilitation method: the adjusted joint training intensity DA is calculated according to the physiological index coefficient K and the adjustment coefficient θ, and the formula based on it is as follows:

[0060] DA=K×θ×D

[0061] The adjusted strength training intensity GA is calculated based on the physiological index coefficient K and the adjustment coefficient θ, and the formula is as follows:

[0062] GA=K×θ×G

[0063] The adjusted balance training difficulty HA is calculated based on the physiological index coefficient K and the adjustment coefficient θ, and the formula is as follows:

[0064] HA = K × θ × H.

[0065] Preferably, in the above-mentioned training-based action rehabilitation method, the step of selecting the difficulty level of the virtual scene according to the joint training plan, the strength training plan and the balance training plan is:

[0066] Develop a joint training plan, the specific methods are:

[0067] The joint training threshold set includes the medium joint training threshold D 1 and advanced joint training threshold D 2 ;

[0068] Compare the adjusted joint training intensity DA with the joint training threshold set and select the joint training plan. The specific criteria are:

[0069]

[0070] Develop a strength training plan by:

[0071] Strength training threshold set includes medium strength training threshold G 1 and high strength training threshold G 2 ;

[0072] The adjusted strength training intensity GA is compared with the strength training threshold set to select a strength training program. The specific criteria are:

[0073]

[0074] Develop a balance training plan by:

[0075] The balance training threshold set includes the medium balance training threshold H 1 and advanced balance training threshold H 2 ;

[0076] Compare the adjusted balance training difficulty HA with the balance training threshold set and select a balance training plan. The specific criteria are:

[0077]

[0078] The standard for selecting the difficulty level of virtual scenes according to the joint training plan, strength training plan and balance training plan is: when the joint training plan, strength training plan and balance training plan all reach the high-difficulty training plan, select the high-difficulty virtual training scene; when at least two of the joint training plan, strength training plan and balance training plan reach the medium-difficulty training plan, select the medium-difficulty virtual training scene; in other cases, select the low-difficulty virtual training scene.

[0079] Preferably, in the above-mentioned training-based action rehabilitation method: according to the action accuracy Q n , qualified threshold QA and good threshold QB to adjust the difficulty level of the virtual scene:

[0080] The sensor data also includes the patient's actual movement angle N (n，m) and the actual motion trajectory P (n，m) ;

[0081] According to the patient's actual movement angle N (n，m) Calculate the angle error rate NC n , the formula based on is:

[0082]

[0083] Among them, NC n is the angle error rate of the patient's nth training cycle, where n represents the sequence number of different training cycles and is a positive integer; N (n，m) is the actual angle of the mth movement during the nth training cycle of the patient; m represents the sequence number of different movement actions, which is a positive integer; NE (n，m) is the standard angle of the mth movement in the nth training cycle;

[0084] According to the patient's actual movement trajectory P (n，m) Calculate the trajectory error rate PC n , the formula based on is:

[0085]

[0086] Among them, PC n is the trajectory error rate of the patient in the nth training cycle; P (n，m) is the actual trajectory of the mth movement of the patient during the nth training cycle; PE (n， m ) is the standard trajectory of the mth movement of the patient during the nth training cycle;

[0087] According to the angle error rate NC n and trajectory error rate PC n Calculate the action accuracy Q n , the formula based on is:

[0088] Q n =1-(NC n ×∈ 1 +PC n ×∈ 2 )

[0089] Among them, Q n is the patient's movement accuracy during the nth training cycle; ∈ 1 NC for angle accuracy n The weight coefficient is 0.2~0.7;∈ 2 PC for trajectory accuracy n The weight coefficient is 0.3~0.8; and ∈ 1 +∈ 2 =1;

[0090] According to the action accuracy Q n , qualified threshold QA and good threshold QB to adjust the difficulty level of the virtual scene:

[0091]

[0092] Preferably, in the above-mentioned training-based action rehabilitation method: calculating the action improvement degree X n and improvement in mental status Z n And get the patient recovery index μ n The method is:

[0093] According to the action accuracy Q n Calculate the action improvement X n , the formula based on is:

[0094]

[0095] Among them, X n is the patient's movement accuracy in the nth training cycle; Q 1 is the patient's movement accuracy during the first training cycle; QR is the target movement accuracy;

[0096] The psychological state data includes the user's initial psychological state score Y 0 and the actual mental state score Y n ;

[0097] According to the user's initial psychological state score Y 0 and the current mental state score Y n Calculate the improvement of mental state Z n , the formula based on is:

[0098]

[0099] Among them, Z n Y is the improvement of the patient's mental state during the nth training cycle; n Y is the psychological state score at the end of the nth training cycle; 3 Score the target's mental state;

[0100] According to the action improvement X n and improvement in mental status Z n Calculate the patient recovery index μ n The method is:

[0101] μ n =X n ×μ 1 +Z n ×μ 2

[0102] Among them, μ n is the patient's rehabilitation index at the end of the nth training cycle; μ 1 Action improvement X n The weight coefficient is 0.3~0.6; μ 2 The improvement of mental state is Z n The weight coefficient is 0.4 to 0.7; and μ 1 +μ 2 =1.

[0103] Preferably, in the above-mentioned training-based movement rehabilitation method: the method for evaluating the patient's recovery degree is:

[0104] The rehabilitation index threshold set includes the moderate threshold T 1 and height threshold T 2 ;

[0105] The criteria for evaluating the degree of patient recovery based on the patient recovery index and the recovery index threshold set are:

[0106]

[0107] (III) Beneficial effects

[0108] The present invention provides a training-based action rehabilitation method, which has the following beneficial effects:

[0109] (1) By collecting the patient's personal information data, physical condition data, sensor data, and psychological state data, it can provide a basis for formulating rehabilitation plans and selecting virtual training scenarios, which is conducive to solving the problem of not being able to meet the personalized needs of different patients;

[0110] (2) According to the physiological index coefficient K and the adjustment coefficient θ, the adjusted joint training intensity DA, the adjusted strength training intensity GA and the adjusted balance training difficulty HA are calculated; according to the comparison results of the adjusted joint training intensity DA and the joint training threshold set, a joint training plan is formulated; according to the comparison results of the adjusted strength training intensity GA and the strength training threshold set, a strength training plan is formulated; according to the comparison results of the adjusted balance training difficulty HA and the balance training threshold set, a balance training plan is formulated, which can provide patients with a reasonable training plan and solve the problems of lack of objective evaluation indicators and inability to meet the personalized needs of different patients;

[0111] (3) According to the action accuracy Q n , qualified threshold QA and good threshold QB to adjust the difficulty level of virtual scenes, which can provide clear standards for adjusting the difficulty level of virtual scenes and solve the problems of unreasonable training intensity and progress;

[0112] (4) According to the patient's recovery index μ n The use of a set of rehabilitation indicator thresholds to assess a patient's degree of rehabilitation can clarify the patient's stage of rehabilitation, help patients and rehabilitation therapists understand the rehabilitation effect, and solve the problem of difficulty in accurately quantifying a patient's rehabilitation progress. BRIEF DESCRIPTION OF THE DRAWINGS

[0113] Figure 1 The figure is a schematic diagram of the working steps of a training-based movement rehabilitation method of the present invention. DETAILED DESCRIPTION

[0114] 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.

[0115] See also Figure 1 The present invention provides a training-based action rehabilitation method, comprising the following steps:

[0116] Step 1: Collect the patient's personal information data, physical condition data, sensor data and psychological state data;

[0117] Combined with the content of step 1:

[0118] By collecting the patient's personal information data, physical condition data, sensor data, and psychological state data, it can provide a basis for formulating rehabilitation plans and selecting virtual training scenarios, which is conducive to solving the problem of not being able to meet the personalized needs of different patients;

[0119] Step 2: Calculate the joint training intensity D, strength training intensity G and balance training difficulty H based on the patient's personal information data and physical condition data;

[0120] The physiological index coefficient K and the adjustment coefficient θ are calculated according to the patient's physical state data and sensor data, and the adjusted joint training intensity DA, the adjusted strength training intensity GA and the adjusted balance training difficulty HA are calculated in combination with the joint training intensity D, the strength training intensity G and the balance training difficulty H;

[0121] Preset a joint training threshold set; formulate a joint training plan based on the comparison result between the adjusted joint training intensity DA and the joint training threshold set;

[0122] Preset a strength training threshold set; formulate a strength training plan based on the comparison result between the adjusted strength training intensity GA and the strength training threshold set;

[0123] Preset a balance training threshold set; formulate a balance training plan according to the comparison result between the adjusted balance training difficulty HA and the balance training threshold set;

[0124] Select the difficulty level of the virtual scenario according to the joint training plan, strength training plan and balance training plan;

[0125] Step 2 includes the following steps:

[0126] Step 201: The method for calculating the joint training intensity D is:

[0127] Personal information data at least includes the patient's actual age 1 ;

[0128] According to the actual age of the patient 1 The age coefficient A is calculated based on the following formula:

[0129]

[0130] Among them, A 2 is the benchmark age, which is determined based on clinical data and rehabilitation experience and is between 30 and 40 years old; A 3 is the age adjustment range, which is used to measure the impact of the degree of deviation from the benchmark age on the age coefficient A. It is set according to different rehabilitation needs and population characteristics, and the value ranges from 20 to 80; α 1 Age influence factor, reflecting the influence of age on the body's recovery ability. The larger the value, the greater the influence of age on the body's recovery ability. It is determined based on clinical experience and is between 0.01 and 0.1.

[0131] The body status data at least includes the patient's joint activity angle value B a ;

[0132] According to the joint activity angle value B a The formula for calculating the comprehensive index B of limb range of motion is:

[0133]

[0134] It should be noted that this formula calculates the activity angle value B of each joint a The maximum normal motion angle value B of the joint (a,max) The ratio of the angle of each joint and the adjustment coefficient of the ratio of the angle of each joint The sum of the products of is used to obtain the comprehensive index B of the range of limb motion; the larger the comprehensive index B of the range of limb motion is, the better the range of limb motion of the patient is; the smaller the comprehensive index B of the range of limb motion is, the worse the range of limb motion of the patient is;

[0135] Among them, B a is the active angle value of the a-th joint, a represents the serial number of different joints, and the value is a positive integer; B (a,max) is the maximum normal range of motion of the ath joint, determined according to rehabilitation medicine practice and medical professional standards; is the adjustment coefficient of the activity angle ratio of the ath joint, which is determined according to the importance and frequency of use of different joints in daily life, and has a value of 0.1 to 1. For example, the adjustment coefficient of the activity angle ratio of the shoulder joint is 0.7; the adjustment coefficient of the activity angle ratio of the interphalangeal joint is 0.2;

[0136] The formula for calculating joint training intensity D is based on the age coefficient A and the comprehensive index B of limb range of motion:

[0137] D=A×β c ×B×C

[0138] It should be noted that this formula uses the age coefficient A and the gender influence factor β c and the comprehensive index B of limb range of motion to adjust the basic joint training intensity C to obtain the joint training intensity D;

[0139] Among them, C is the basic joint training intensity, which is determined according to the patient's physical foundation and clinical guidelines; β c is the gender influencing factor, which is determined based on clinical experience and rehabilitation research data, and its value is 0.85-1.25; c is the gender code, and its value is 1 or 2. When c is 1, the corresponding patient is female, and β 1 The value range of β is 0.85~1.05, which is determined according to the physical foundation of female patients. When c is 2, the corresponding patient is male, 2 The value range is 0.95~1.25, which is determined according to the physical foundation of male patients.

[0140] Step 202: The method for calculating the strength training intensity G is:

[0141] The physical condition data also includes the measured strength value E of each muscle group i ;

[0142] According to the measured strength value E of each muscle group i The formula for calculating the comprehensive muscle strength index F is:

[0143]

[0144] It should be noted that this formula calculates the actual strength value E of each muscle group. i The normal strength value E of this muscle group (i,normal) The ratio of the muscle group to the strength ratio adjustment coefficient τ i The sum of the products of is the comprehensive muscle strength index F. The larger the comprehensive muscle strength index F is, the higher the overall muscle strength of the patient is; the smaller the comprehensive muscle strength index F is, the lower the overall muscle strength of the patient is.

[0145] Among them, E i is the measured strength value of the i-th muscle group, i represents the sequence number of different muscle groups, and its value is a positive integer; E (i,normal) is the normal strength value of the ith muscle group, determined based on medical research and population statistics; τ i is the strength ratio adjustment coefficient of the ith muscle group, which is determined according to the role and importance of different muscle groups in body movement, and has a value of 0.15 to 0.35. For example, the strength ratio adjustment coefficient of the thigh muscle group is 0.32; the strength ratio adjustment coefficient of the shoulder muscle group is 0.27;

[0146] According to the age coefficient A, gender influence factor β c The strength training intensity G is calculated based on the comprehensive muscle strength index F, and the formula is:

[0147] G=A×β c ×F×G 0

[0148] It should be noted that this formula uses the age coefficient A and the gender influence factor β c And muscle strength comprehensive index F to adjust the basic strength training intensity G 0 , get the strength training intensity G;

[0149] Among them, G 0 The intensity of basic strength training is determined according to the patient's body fat and muscle content and clinical guideline standards.

[0150] Step 203: The method for calculating the balance training difficulty H is:

[0151] The patient's physical condition data also includes fracture recovery progress I 1 and expected recovery progress I 2 ;

[0152] According to the fracture recovery progress I 1 Expected recovery progress 2 The calculation of fracture recovery degree I is based on the formula:

[0153]

[0154] It should be noted that this formula calculates the fracture recovery progress I 1 Expected recovery progress 2 The ratio of is used to obtain the fracture recovery degree I;

[0155] Among them, γ p is the importance coefficient of the pth fracture site, which is determined according to the importance and frequency of use of the fracture site in daily life, and has a value of 0.1 to 1. For example, when the fracture site is the hip joint, the value is 0.8; when the fracture site is the finger, the value is 0.1;

[0156] The patient's physical condition data also includes the current neurological function assessment value M 1 and pre-injury neurological function assessment value M 2 ;

[0157] According to the current neurological function assessment value M 1 and pre-injury neurological function assessment value M 2 The formula for calculating the neurological function recovery coefficient M is as follows:

[0158]

[0159] It should be noted that this formula calculates the current neurological function assessment value M 1 and pre-injury neurological function assessment value M 2 The ratio of the nerve function recovery coefficient M is obtained; the larger the nerve function recovery coefficient M is, the higher the degree of nerve function recovery is; the smaller the nerve function recovery coefficient M is, the lower the degree of nerve function recovery is;

[0160] Among them, M q is the importance coefficient of the qth nerve injury site, which is determined according to the degree of influence of the nerve injury site on body function, and the value is 0.2 to 1. For example, when the nerve injury site is the hand nerve, the value is 0.8; when the nerve injury site is the peripheral skin nerve, the value is 0.3;

[0161] According to the age coefficient A, gender influence factor β c, fracture recovery degree I and nerve function recovery coefficient M to calculate the balance training difficulty H, based on the formula:

[0162] H=A×β c ×I×M×H 0

[0163] It should be noted that this formula uses the age coefficient A and the gender influence factor β c , fracture recovery degree I and nerve function recovery coefficient M to adjust the difficulty H of basic balance training 0 , get the balance training difficulty H;

[0164] Among them, H 0 The difficulty of basic balance training is determined according to the patient's injury site and clinical guideline standards.

[0165] Step 204: The method for calculating the physiological index coefficient K is:

[0166] The sensor data includes at least the actual heart rate value a 1 , actual blood pressure value b 1 and actual respiratory rate c 1 ;

[0167] It should be noted that during the training process, the patient obtains the actual heart rate value a by wearing a heart rate belt, pressure sensor and smart chest belt 1 , actual blood pressure value b 1 and actual respiratory rate c 1 The heart rate belt detects the heart's electrical signals to get the actual heart rate value a 1 The pressure sensor detects the pressure change in the cuff to obtain the actual blood pressure value b 1 The smart chest belt detects the expansion and contraction of the chest or abdomen during breathing to obtain the actual breathing rate c 1 ;

[0168] According to the actual heart rate value a 1 , actual blood pressure value b 1 and actual respiratory rate c 1 The formula for calculating the physiological index coefficient K is:

[0169]

[0170] It should be noted that the formula considers the degree of deviation between the actual values ​​of the three physiological indicators of heart rate, blood pressure and respiratory rate and the normal values, and multiplies them by the corresponding risk coefficient to obtain the physiological indicator coefficient K; the larger the physiological indicator coefficient K, the better the patient's physiological state; the smaller the physiological indicator coefficient K, the worse the patient's physiological state;

[0171] Among them, a 2 is the normal heart rate value; b2 is the normal blood pressure value; c 2 is the normal breathing rate; the normal heart rate value is a 2 Normal blood pressure value b 2 and normal respiratory rate c 2 Determined based on the patient’s age, sex, and physical condition;

[0172] ε 1 is the heart rate risk coefficient, which is determined according to the influence of heart rate on the physiological index coefficient K and has a value of 0.3 to 0.5; ε 2 is the blood pressure risk coefficient, which is determined according to the influence of blood pressure on the physiological index coefficient K and has a value of 0.2 to 0.5; ε 3 is the respiratory rate risk coefficient, which is determined according to the influence of respiratory rate on the physiological index coefficient K and has a value of 0.2 to 0.5; and ε 1 +ε 2 +ε 3 =1;

[0173] The method for calculating the adjustment coefficient θ is:

[0174] The sensor data also includes pressure sensor data L 1 and electromyography sensor data L 2 ;

[0175] It should be noted that during the training process, the patient wears a pressure sensor and an electromyography sensor to obtain pressure sensor data L 1 and electromyography sensor data L 2 , pressure sensor data L 1 It can reflect the pressure distribution of the patient on the ground or training equipment during training; the electromyographic sensor data L 2 It can reflect the patient's muscle activity;

[0176] According to the pressure sensor data L 1 and electromyography sensor data L 2 The adjustment factor θ is calculated based on the formula:

[0177] θ=L 1 ×θ 1 +L 2 ×θ 2

[0178] Among them, θ 1 is the pressure sensor data L 1 The weight coefficient is determined according to the influence of the pressure distribution on the adjustment coefficient θ during the patient's training process, and the value is 0.3 to 0.6; θ 2 is the electromyography sensor data L 2The weight coefficient is determined according to the influence of the patient's muscle activity during training on the adjustment coefficient θ, and the value is 0.4 to 0.7; and θ 1 +θ 2 =1.

[0179] Step 205: Calculate the adjusted joint training intensity DA according to the physiological index coefficient K and the adjustment coefficient θ, based on the following formula:

[0180] DA=K×θ×D

[0181] It should be noted that the formula adjusts the joint training intensity D through the physiological index coefficient K and the adjustment coefficient θ to obtain the adjusted joint training intensity DA;

[0182] The adjusted strength training intensity GA is calculated based on the physiological index coefficient K and the adjustment coefficient θ, and the formula is as follows:

[0183] GA=K×θ×G

[0184] It should be noted that the formula adjusts the strength training intensity G through the physiological index coefficient K and the adjustment coefficient θ to obtain the adjusted strength training intensity GA;

[0185] The adjusted balance training difficulty HA is calculated based on the physiological index coefficient K and the adjustment coefficient θ, and the formula is as follows:

[0186] HA=K×θ×H

[0187] It should be noted that the formula adjusts the balance training difficulty H through the physiological index coefficient K and the adjustment coefficient θ to obtain the adjusted balance training difficulty HA.

[0188] Step 206: The steps of selecting the difficulty level of the virtual scene according to the joint training plan, the strength training plan and the balance training plan are as follows:

[0189] Develop a joint training plan, the specific methods are:

[0190] The joint training threshold set includes the medium joint training threshold D 1 and advanced joint training threshold D 2 ;

[0191] Compare the adjusted joint training intensity DA with the joint training threshold set and select the joint training plan. The specific criteria are:

[0192]

[0193] It should be noted that the low-difficulty joint training plan mainly includes: performing simple joint flexion and extension, such as bending and stretching the arms, lifting and lowering the legs, and gradually increasing the range and frequency of motion; the medium-difficulty joint training plan mainly includes: on the basis of the low-difficulty joint training plan, further increasing the range of joint motion and performing multi-angle joint activities, such as lifting legs and rotating movements at different angles; the high-difficulty joint training plan mainly includes: performing multi-joint combination exercises, such as rotating the wrist while drawing circles with the arms.

[0194] Among them, the medium joint training threshold D 1 and advanced joint training threshold D 2 Determined based on clinical experience and rehabilitation standards;

[0195] Develop a strength training plan. The specific method is as follows: The strength training threshold set includes the medium strength training threshold G 1 and high strength training threshold G 2 ;

[0196] The adjusted strength training intensity GA is compared with the strength training threshold set to select a strength training program. The specific criteria are:

[0197]

[0198] It should be noted that the low-difficulty strength training program is mainly for isometric contraction training, for example, the patient sits on a chair and squeezes the thigh muscles downward to feel the contraction of the thigh muscles; it also includes auxiliary strength training, for example, using elastic bands to perform leg flexion and extension training; the medium-difficulty strength training program is mainly for progressive resistance training, for example, when performing arm weighted curls, the weight gradually increases from 1 kg to 4 kg; it also includes functional strength training, for example, simulating the movements of going up and down stairs; the high-difficulty strength training program is mainly for high-intensity resistance training, such as using a load of more than 10 kg for strength training, while increasing the number of action sets and repetitions;

[0199] Among them, the moderate strength training threshold G 1 and high strength training threshold G 2 Determined based on clinical experience and rehabilitation standards;

[0200] Develop a balance training plan by:

[0201] The balance training threshold set includes the medium balance training threshold H 1 and advanced balance training threshold H 2 ;

[0202] Compare the adjusted balance training difficulty HA with the balance training threshold set and select a balance training plan. The specific criteria are:

[0203]

[0204] It should be noted that the low-difficulty balance training program is mainly static balance training, such as patients standing on both feet or one foot without external force; the medium-difficulty balance training program is mainly dynamic balance training, such as walking on a balance board; the high-difficulty balance training program is mainly agility training, such as setting up obstacles, and patients complete running, jumping and turning without touching the obstacles;

[0205] The standard for selecting the difficulty level of virtual scenes according to the joint training plan, strength training plan and balance training plan is: when the joint training plan, strength training plan and balance training plan all reach the high-difficulty training plan, the high-difficulty virtual training scene is selected; the high-difficulty virtual training scene mainly includes urban traffic simulation scenes, such as road intersections with complex traffic conditions and streets with heavy traffic;

[0206] When at least two of the joint training plan, strength training plan, and balance training plan reach the medium difficulty training plan, a medium difficulty virtual training scene is selected; the medium difficulty virtual training scene mainly includes community environment scenes, such as shopping malls, supermarkets, and gyms;

[0207] In other cases, low-difficulty virtual training scenes are selected. Low-difficulty virtual training scenes are mainly daily life scenes, such as living rooms and parks with relatively flat roads;

[0208] It should be noted that the virtual training scene is a training scene that simulates a real or imaginary environment and is constructed using computing technology, virtual reality, augmented reality and other means. During the training process, the patient wears a virtual reality helmet, motion capture equipment, tactile feedback device and controller. The virtual reality helmet provides an immersive visual experience, the motion capture equipment tracks the user's body movements, the tactile feedback device enhances the sense of reality, and the controller is used for interaction. By setting up a virtual training scene, the fun and participation of the training can be enhanced, and the patient's enthusiasm for training can be improved.

[0209] Combined with the contents of step 201 to step 206:

[0210] The adjusted joint training intensity DA, the adjusted strength training intensity GA and the adjusted balance training difficulty HA are calculated according to the physiological index coefficient K and the adjustment coefficient θ; the problem of lack of objective evaluation indicators is solved; a joint training plan is formulated according to the comparison results of the adjusted joint training intensity DA and the joint training threshold set; a strength training plan is formulated according to the comparison results of the adjusted strength training intensity GA and the strength training threshold set; a balance training plan is formulated according to the comparison results of the adjusted balance training difficulty HA and the balance training threshold set, which can ensure the safety of patients during training and solve the problem of unreasonable training plans; virtual scenes of different difficulty levels are selected according to the joint training plan, strength training plan and balance training plan, which solves the problem of not being able to meet the personalized needs of different patients;

[0211] Step 3: Calculate the motion accuracy Q based on the patient’s sensor data n ; Preset qualified threshold QA and good threshold QB, according to the action accuracy Q n , qualified threshold AQ and good threshold QB to adjust the difficulty level of the virtual scene;

[0212] Step 3 includes the following steps:

[0213] Step 301: According to the action accuracy Q n , qualified threshold QA and good threshold QB to adjust the difficulty level of the virtual scene:

[0214] The sensor data also includes the patient's actual movement angle N (n，m) and the actual motion trajectory P (n，m) ;

[0215] It should be noted that the patient needs to wear a motion capture electromagnetic sensor during the training process. The electromagnetic sensor determines the position and posture of the person by receiving the signal from the magnetic field generator, and obtains the patient's actual movement angle N (n，m) and the actual motion trajectory P (n，m) ;

[0216] According to the patient's actual movement angle N (n，m) Calculate the angle error rate NC n , the formula based on is:

[0217]

[0218] It should be noted that this formula calculates the patient's actual movement angle N (n，m) Standard angle NE with sports action (n，m) The degree of deviation is obtained, and the angle error rate NC is obtained. n ; The greater the deviation, the greater the angle error rate NC n The higher the deviation, the smaller the angle error rate NCn The lower;

[0219] NC n is the angle error rate of the patient's nth training cycle, where n represents the sequence number of different training cycles and is a positive integer; N (n，m) is the actual angle of the mth movement of the patient during the nth training cycle; m represents the sequence number of different movement actions, which is a positive integer; NE (n，m) The standard angle of the mth movement in the nth training cycle is determined according to the patient's physical condition, disease type and medical research data; the training cycle is determined according to the patient's training situation and the rehabilitation therapist's advice;

[0220] According to the patient's actual movement trajectory P (n，m) Calculate the trajectory error rate PC n , the formula based on is:

[0221]

[0222] It should be noted that this formula calculates the patient's actual motion trajectory P (n，m) PE with standard trajectory of sports action (n，m) The degree of deviation is obtained by obtaining the trajectory error rate PC n ; The greater the deviation, the higher the trajectory error rate PC n The higher the deviation, the smaller the trajectory error rate PC n The lower;

[0223] Among them, PC n is the trajectory error rate of the patient in the nth training cycle; P (n，m) is the actual trajectory of the mth movement of the patient during the nth training cycle; PE (n，m) The standard trajectory of the mth movement of the patient during the nth training cycle is determined according to the patient's physical condition, disease type and medical research data;

[0224] According to the angle error rate NC n and trajectory error rate PC n Calculate the action accuracy Q n , the formula based on is:

[0225] Q n =1-(NC n ×∈ 1 +PC n ×∈ 2 )

[0226] It should be noted that the formula obtains the action accuracy Q through the weighted formula n ; Angle error rate NC n The higher the action accuracy Q nThe lower the angle error rate NC n The lower the action accuracy Q n The higher the trajectory error rate PC n The higher the action accuracy Q n The lower the trajectory error rate PC n The lower the action accuracy Q n The higher;

[0227] Among them, Q n is the patient's movement accuracy during the nth training cycle; ∈ 1 NC for angle accuracy n The weighting factor is based on the angle accuracy NC n Q for action accuracy n The importance of is determined, and the value is 0.2~0.7;∈ 2 PC for trajectory accuracy n The weight coefficient is based on the trajectory error rate PC n Q for action accuracy n The importance of is determined, and the value is 0.3~0.8; and ∈ 1 +∈ 2 =1;

[0228] According to the action accuracy Q n , qualified threshold QA and good threshold QB to adjust the difficulty level of the virtual scene:

[0229]

[0230] Among them, the qualified threshold QA and the good threshold QB are determined according to the patient's physical condition and rehabilitation standard guidelines.

[0231] Combined with the content of step 301:

[0232] According to the action accuracy Q n , qualified threshold QA and good threshold QB to adjust the difficulty level of virtual scenes, which can provide clear standards for adjusting the difficulty level of virtual scenes and solve the problems of unreasonable training intensity and progress;

[0233] Step 4: Calculate the action improvement X n and improvement in mental status Z n And get the patient recovery index μ n ; Preset moderate threshold T 1 and height threshold T 2 , according to the patient's recovery index μ n , moderate threshold T 1 and height threshold Y 2 Assess the patient's recovery;

[0234] Step 4 includes the following steps:

[0235] Step 401: Calculate the action improvement X n and improvement in mental status Z n And get the patient recovery index μ n The method is:

[0236] According to the action accuracy Q n Calculate the action improvement X n , the formula based on is:

[0237]

[0238] It should be noted that this formula compares the patient's movement accuracy Q in different training cycles. n , get the action improvement X n ;

[0239] Among them, X n is the patient's movement accuracy in the nth training cycle; Q 1 It is the movement accuracy of the patient in the first training cycle; QR is the target movement accuracy, which is determined according to medical standards and rehabilitation goals;

[0240] The psychological state data includes the user's initial psychological state score Y 0 and the actual mental state score Y n ;

[0241] According to the user's initial psychological state score Y 0 and the current mental state score Y n Calculate the improvement of mental state Z n , the formula based on is:

[0242]

[0243] It should be noted that this formula compares the psychological state scores Y of patients in different training cycles. n , and get the improvement of mental state Z n ;

[0244] Among them, Z n Y is the improvement of the patient's mental state during the nth training cycle; n Y is the psychological state score at the end of the nth training cycle; 3 A target mental state score is created, determined based on medical criteria and rehabilitation goals;

[0245] According to the action improvement X n and improvement in mental status Z n Calculate the patient recovery index μ n The method is:

[0246] μ n =X n ×μ 1 +Z n ×μ 2

[0247] It should be noted that this formula obtains the patient recovery index μ through the weighted formula n ; Action improvement X n The higher the value, the better the rehabilitation effect. The patient's rehabilitation index μ n The higher the action improvement, the better n The lower the value, the worse the rehabilitation effect. n The lower; the improvement of mental state Z n The higher it is, the better the patient's mental state is, and the patient's recovery index μ n The higher the improvement of mental state Z n The lower the value, the worse the patient's mental state is. n The lower;

[0248] Among them, μ n is the patient's rehabilitation index at the end of the nth training cycle; μ 1 Action improvement X n The weight coefficient is based on the action improvement X n Improvement in mental status Z n The importance of μ is determined by the value of 0.3~0.6; 2 The improvement of mental state is Z n The weight coefficient is based on the improvement of the psychological state Z n Improvement in mental status Z n The importance of is determined, and the value is 0.4~0.7; and μ 1 +μ 2 =1.

[0249] Step 402: The method for evaluating the patient's recovery degree is:

[0250] The rehabilitation index threshold set includes the moderate threshold T 1 and height threshold T 2 ;

[0251] The criteria for evaluating the degree of patient recovery based on the patient recovery index and the recovery index threshold set are:

[0252]

[0253] Among them, the moderate threshold T 1 and height threshold T 2 Determined based on the patient's injury type, physical condition, rehabilitation goals and rehabilitation guideline standards.

[0254] Combined with the contents of step 401 to step 402:

[0255] According to the patient's recovery index μ n , moderate threshold T 1 and height threshold T 2 Assessing the patient's degree of recovery can clarify the patient's stage of rehabilitation, which helps the patient and the rehabilitation therapist understand the rehabilitation effect and solves the problem of difficulty in accurately quantifying the patient's rehabilitation progress.

[0256] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware or in combination with computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0257] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0258] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A training-based movement rehabilitation method, characterized in that: The following steps are involved: Collect patients’ personal information data, physical condition data, sensor data, and psychological state data; Calculate joint training intensity D, strength training intensity G and balance training difficulty H based on the patient's personal information data and physical condition data; The physiological index coefficient K and the adjustment coefficient θ are calculated according to the patient's physical state data and sensor data, and the adjusted joint training intensity DA, the adjusted strength training intensity GA and the adjusted balance training difficulty HA are calculated in combination with the joint training intensity D, the strength training intensity G and the balance training difficulty H; Preset joint training threshold sets; According to the comparison results of the adjusted joint training intensity DA and the joint training threshold set, a joint training plan is formulated; A set of preset strength training thresholds; According to the comparison results of the adjusted strength training intensity GA and the strength training threshold set, a strength training plan is formulated; Preset balance training threshold set; Formulate a balance training plan according to the comparison result of the adjusted balance training difficulty HA and the balance training threshold set; Select the difficulty level of the virtual scenario according to the joint training plan, strength training plan and balance training plan; Calculate the movement accuracy Q based on the patient's sensor data n ; Preset qualified threshold QA and good threshold QB, according to the action accuracy Q n , qualified threshold QA and good threshold QB to adjust the difficulty level of the virtual scene; Based on the action accuracy Q n Calculate the action improvement X n ; Calculate the psychological state improvement degree Z based on the psychological state data n , and improve the degree X through action n and improvement in mental status Z n Further calculate the patient recovery index μ n ; Preset the rehabilitation index threshold set, according to the patient's rehabilitation index μ n and rehabilitation index threshold set to evaluate the patient's rehabilitation degree; where: the psychological state data includes the user's initial psychological state score Y0 and the actual psychological state score Y n ; According to the user's initial psychological state score Y0 and current psychological state score Y n Calculate the improvement of mental state Z n , the formula based on is: Among them, Z n Y is the improvement of the patient's mental state during the nth training cycle; n is the psychological state score at the end of the nth training cycle; Y3 is the target psychological state score; According to the action improvement X n and improvement in mental status Z n Calculate the patient recovery index μ n The method is: μ n =X n ×μ1+Z n ×μ2 Among them, μ n is the rehabilitation index of the patient at the end of the nth training cycle; μ1 is the degree of movement improvement X n The weight coefficient is 0.3-0.6; μ2 is the improvement degree of psychological state Z n The weight coefficient is between 0.4 and 0.7; and μ1+μ2=1.

2. A method of movement rehabilitation based on training according to claim 1, characterized in that: The method for calculating joint training intensity D is: Personal information data shall at least include the patient’s actual age A1; The age coefficient A is calculated based on the patient's actual age A1, according to the following formula: Among them, A2 is the base age; A3 is the age adjustment range; α1 is the age impact factor, with a value of 0.01 to 0.1; The body status data at least includes the patient's joint activity angle value B a ; According to the joint activity angle value B a The formula for calculating the comprehensive index B of limb range of motion is: Among them, B a is the active angle value of the a-th joint, a represents the serial number of different joints, and the value is a positive integer; B (a,max) is the maximum normal motion angle value of the ath joint; is the adjustment coefficient of the activity angle ratio of the ath joint, ranging from 0 to 1; The formula for calculating joint training intensity D is based on the age coefficient A and the comprehensive index B of limb range of motion: D=A×β c ×B×C Among them, C is the basic joint training intensity; β c is the gender influencing factor, c is the gender number, and its values ​​are 1 and 2. When c is 1, the corresponding patient gender is female, and when c is 2, the corresponding patient gender is male.

3. A method of movement rehabilitation based on training according to claim 2, characterized in that: The method for calculating strength training intensity G is: The physical condition data also includes the measured strength value E of each muscle group i ; According to the measured strength value E of each muscle group i The formula for calculating the comprehensive muscle strength index F is: Among them, E i is the measured strength value of the i-th muscle group, i represents the sequence number of different muscle groups, and its value is a positive integer; E (i,normal) is the normal strength value of the i-th muscle group; τ i is the strength ratio adjustment coefficient of the i-th muscle group, ranging from 0.15 to 0.35; According to the age coefficient A, gender influence factor β c The strength training intensity G is calculated based on the comprehensive muscle strength index F, and the formula is: G=A×β c ×F×G0 Among them, G0 is the basic strength training intensity.

4. The method of movement rehabilitation based on training according to claim 3, characterized in that: The method for calculating the balance training difficulty H is: The patient's physical status data also includes fracture recovery progress I1 and expected recovery progress I2; The fracture recovery degree I is calculated based on the fracture recovery progress I1 and the expected recovery progress I2, and the formula is: Among them, γ p is the importance coefficient of the pth fracture site, ranging from 0.1 to 1; The patient's physical condition data also includes a current neurological function assessment value M1 and a neurological function assessment value before injury M2; The neurological function recovery coefficient M is calculated based on the current neurological function assessment value M1 and the neurological function assessment value before injury M2, and the formula is as follows: Among them, M q is the importance coefficient of the qth nerve injury site, ranging from 0.2 to 1; According to the age coefficient A, gender influence factor β c , fracture recovery degree I and nerve function recovery coefficient M to calculate the balance training difficulty H, based on the formula: H=A×β c ×I×M×H0 Among them, H0 is the basic balance training difficulty.

5. The method of movement rehabilitation based on training according to claim 4, characterized in that: The method for calculating the physiological index coefficient K is: The sensor data includes at least an actual heart rate value a1, an actual blood pressure value b1, and an actual respiratory rate c1; The physiological index coefficient K is calculated based on the actual heart rate value a1, the actual blood pressure value b1 and the actual respiratory rate c1, and the formula is as follows: Among them, a2 is the normal heart rate value; b2 is the normal blood pressure value; c2 is the normal respiratory rate; ε1 is the heart rate risk coefficient, which is 0.3-0.5; ε2 is the blood pressure risk coefficient, which is 0.2-0.5; ε3 is the respiratory rate risk coefficient, which is 0.2-0.5; and ε1+ε2+ε3=1; The method for calculating the adjustment coefficient θ is: The sensor data also includes pressure sensor data L1 and electromyographic sensor data L2; The adjustment coefficient θ is calculated based on the pressure sensor data L1 and the electromyography sensor data L2, and the formula is: θ=L1×θ1+L2×θ2 Among them, θ1 is the weight coefficient of the pressure sensor data L1, and its value is 0.3-0.6; θ2 is the weight coefficient of the electromyography sensor data L2, and its value is 0.4-0.7; and θ1+θ2=1.

6. A method of movement rehabilitation based on training according to claim 5, characterized in that: The adjusted joint training intensity DA is calculated based on the physiological index coefficient K and the adjustment coefficient θ, and the formula is as follows: DA=K×θ×D The adjusted strength training intensity GA is calculated based on the physiological index coefficient K and the adjustment coefficient θ, and the formula is as follows: GA=K×θ×G The adjusted balance training difficulty HA is calculated based on the physiological index coefficient K and the adjustment coefficient θ, and the formula is as follows: HA = K × θ × H.

7. The method of movement rehabilitation based on training according to claim 6, characterized in that: The steps for selecting the difficulty level of the virtual scenario according to the joint training plan, strength training plan and balance training plan are as follows: Develop a joint training plan, the specific methods are: The joint training threshold set includes a medium joint training threshold D1 and a high joint training threshold D2; Compare the adjusted joint training intensity DA with the joint training threshold set and select the joint training plan. The specific criteria are: Develop a strength training plan by: The strength training threshold set includes a moderate strength training threshold G1 and a high strength training threshold G2; The adjusted strength training intensity GA is compared with the strength training threshold set to select a strength training program. The specific criteria are: Develop a balance training plan by: The balance training threshold set includes a medium balance training threshold H1 and a high balance training threshold H2; Compare the adjusted balance training difficulty HA with the balance training threshold set and select a balance training plan. The specific criteria are: The standard for selecting the difficulty level of virtual scenes according to the joint training plan, strength training plan and balance training plan is: when the joint training plan, strength training plan and balance training plan all reach the high-difficulty training plan, a high-difficulty virtual training scene is selected; When at least two of the joint training program, strength training program, and balance training program reach a moderate difficulty level, a moderate difficulty virtual training scenario is selected; In other cases, choose a low-difficulty virtual training scenario.

8. The method of movement rehabilitation based on training according to claim 7, characterized in that: According to the action accuracy Q n , qualified threshold QA and good threshold QB to adjust the difficulty level of the virtual scene: The sensor data also includes the patient's actual movement angle N (n，m) and the actual motion trajectory P (n，m) ; According to the patient's actual movement angle N (n，m) Calculate the angle error rate NC n , the formula based on is: Among them, NC n is the angle error rate of the patient's nth training cycle, where n represents the sequence number of different training cycles and is a positive integer; N (n，m) is the actual angle of the mth movement of the patient during the nth training cycle; m represents the sequence number of different movement actions, which is a positive integer; NE (n，m) is the standard angle of the mth movement in the nth training cycle; According to the patient's actual movement trajectory P (n，m) Calculate the trajectory error rate PC n , the formula based on is: Among them, PC n is the trajectory error rate of the patient in the nth training cycle; P (n，m) is the actual trajectory of the mth movement of the patient during the nth training cycle; PE (n，m) is the standard trajectory of the mth movement of the patient during the nth training cycle; According to the angle error rate NC n and trajectory error rate PC n Calculate the action accuracy Q n , the formula based on is: Q n =1-(NC n ×∈1+PC n ×∈2) Among them, Q n is the patient's movement accuracy during the nth training cycle; ∈1 is the angle accuracy NC n The weight coefficient is 0.2~0.7; ∈2 is the trajectory accuracy PC n The weight coefficient is 0.3~0.8; and ∈1+∈2=1; According to the action accuracy Q n , qualified threshold QA and good threshold QB to adjust the difficulty level of the virtual scene:

9. The method of movement rehabilitation based on training according to claim 8, characterized in that: Calculate the action improvement X n The method is: According to the action accuracy Q n Calculate the action improvement X n , the formula based on is: Among them, X n is the patient's movement accuracy in the nth training cycle; Q1 is the patient's movement accuracy in the first training cycle; QR is the target movement accuracy.

10. The method of movement rehabilitation based on training according to claim 9, characterized in that: The methods for assessing the patient's recovery are: The rehabilitation index threshold set includes a moderate threshold T1 and a high threshold T2; The criteria for evaluating the degree of patient recovery based on the patient recovery index and the recovery index threshold set are:

Images