Intelligent wearable knee joint rehabilitation tracking method and system

Abstract

The invention discloses an intelligent wearable knee joint rehabilitation tracking method and system, relates to the field of rehabilitation, and solves the problem of poor tracking effect of an existing knee joint rehabilitation tracking method.The intelligent wearable knee joint rehabilitation tracking method comprises the steps that S1, periodic movement speed average deviation and periodic movement frequency average deviation are obtained respectively, and rehabilitation exercise monitoring data are obtained; s2, posture monitoring is conducted on the rehabilitation patient, multiple knee posture deviations are obtained, and rehabilitation exercise knee posture deviations are obtained by analyzing the multiple knee posture deviations; and S3, performing periodic rehabilitation tracking on the rehabilitation patient according to the rehabilitation exercise knee posture deviation and the rehabilitation exercise monitoring data, and feeding back a tracking result. According to the knee joint rehabilitation tracking method, the objectivity, the accuracy and the pertinence of the knee joint rehabilitation tracking method can be improved.

Description

Technical Field

[0001] The present invention belongs to the field of rehabilitation and relates to data monitoring technology, specifically to an intelligent wearable knee joint rehabilitation tracking method and system. Background Art

[0002] The existing knee joint rehabilitation tracking methods have the following specific defects when conducting rehabilitation tracking:

[0003] 1. Existing knee joint rehabilitation tracking methods mainly rely on doctors’ visual inspection or communication between doctors and patients, which easily leads to a lack of objectivity in the rehabilitation tracking process, resulting in a lack of accuracy in the tracking results;

[0004] 2. The existing knee joint rehabilitation tracking method cannot formulate tracking standards based on the physiological characteristics of the rehabilitation patient, resulting in a lack of pertinence in the tracking results;

[0005] To this end, we propose a smart wearable knee rehabilitation tracking method and system. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention aims to provide an intelligent wearable knee joint rehabilitation tracking method and system. The present invention is based on marking a patient rehabilitation monitoring cycle during the period of rehabilitation treatment for the rehabilitation patient, obtaining the average deviation of the periodic movement speed by monitoring the gait speed of the rehabilitation patient in the patient rehabilitation monitoring cycle, obtaining the average deviation of the periodic movement frequency by monitoring the gait frequency of the rehabilitation patient in the patient rehabilitation monitoring cycle, and obtaining rehabilitation movement monitoring data. In the process of posture monitoring of the rehabilitation patient, multiple posture monitoring cycles are marked, and posture monitoring is performed on the rehabilitation patient in each posture monitoring cycle respectively to obtain multiple knee posture deviations. Rehabilitation movement knee posture deviation is obtained by analyzing the multiple knee posture deviations, and the joint rehabilitation coefficient corresponding to each patient rehabilitation monitoring cycle is obtained according to the rehabilitation movement knee posture deviation and the rehabilitation movement monitoring data, and the joint rehabilitation coefficient threshold is obtained and compared with the joint rehabilitation coefficient for numerical comparison, and periodic rehabilitation tracking feedback is performed on the rehabilitation patient according to the numerical comparison result.

[0007] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a smart wearable knee joint rehabilitation tracking method, comprising the following specific steps:

[0008] Step S1: marking a patient rehabilitation monitoring period during the period of rehabilitation treatment for the rehabilitation patient, obtaining the average deviation of the periodic movement speed by monitoring the gait speed of the rehabilitation patient in the patient rehabilitation monitoring period, and obtaining the average deviation of the periodic movement frequency by monitoring the gait frequency of the rehabilitation patient in the patient rehabilitation monitoring period, and obtaining rehabilitation movement monitoring data;

[0009] Step S2: in the process of posture monitoring of the rehabilitation patient, multiple posture monitoring cycles are marked, posture monitoring is performed on the rehabilitation patient in each posture monitoring cycle, multiple knee posture deviations are obtained, and rehabilitation exercise knee posture deviation is obtained by analyzing the multiple knee posture deviations;

[0010] Step S3: According to the rehabilitation exercise knee posture deviation and the rehabilitation exercise monitoring data, the joint rehabilitation coefficient corresponding to each patient rehabilitation monitoring cycle is obtained respectively, and the joint rehabilitation coefficient threshold is obtained and compared with the joint rehabilitation coefficient for numerical comparison, and periodic rehabilitation tracking feedback is performed on the rehabilitation patients according to the numerical comparison results.

[0011] Smart wearable knee rehabilitation tracking system, the specific working process of each module is as follows:

[0012] Movement monitoring module: used to mark a patient rehabilitation monitoring period during the period of rehabilitation treatment for the rehabilitation patient, obtain the average deviation of the periodic movement speed by monitoring the gait speed of the rehabilitation patient in the patient rehabilitation monitoring period, and obtain the average deviation of the periodic movement frequency by monitoring the gait frequency of the rehabilitation patient in the patient rehabilitation monitoring period, so as to obtain rehabilitation movement monitoring data;

[0013] Posture monitoring module: used to mark multiple posture monitoring cycles in the process of posture monitoring of rehabilitation patients, perform posture monitoring on rehabilitation patients in each posture monitoring cycle, obtain multiple knee posture deviations, and obtain rehabilitation exercise knee posture deviation by analyzing multiple knee posture deviations;

[0014] Tracking and feedback module: It is used to obtain the joint rehabilitation coefficient corresponding to each patient's rehabilitation monitoring cycle according to the rehabilitation exercise knee posture deviation and rehabilitation exercise monitoring data, and obtain the joint rehabilitation coefficient threshold and compare it with the joint rehabilitation coefficient numerically, and conduct periodic rehabilitation tracking feedback for the rehabilitation patients according to the numerical comparison results.

[0015] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0016] 1. The present invention can quantitatively track the patient's rehabilitation status by respectively obtaining the rehabilitation exercise knee posture deviation, the average deviation of the periodic movement speed, and the average deviation of the periodic movement frequency, which can effectively improve the objectivity and accuracy of the rehabilitation tracking effect;

[0017] 2. The present invention provides a reference standard for the rehabilitation patients' periodic movement speed, periodic movement step frequency and knee posture angle by combining the rehabilitation patients' own physiological conditions, which can effectively improve the pertinence of rehabilitation tracking effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0019] Figure 1 It is a diagram of the implementation steps of the present invention;

[0020] Figure 2 is the overall system block diagram of the present invention;

[0021] Figure 3 Schematic diagram of posture monitoring in the present invention. DETAILED DESCRIPTION

[0022] 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.

[0023] Embodiment 1

[0024] See also Figure 1 The present invention provides a technical solution: a smart wearable knee joint rehabilitation tracking method, comprising the following specific steps:

[0025] Step S1: marking a patient rehabilitation monitoring period during the rehabilitation treatment period for the rehabilitation patient, acquiring the average deviation of the periodic movement speed by monitoring the gait speed of the rehabilitation patient in the patient rehabilitation monitoring period, acquiring the average deviation of the periodic movement frequency by monitoring the gait frequency of the rehabilitation patient in the patient rehabilitation monitoring period, and obtaining rehabilitation movement monitoring data;

[0026] The step S1 further includes the following specific steps:

[0027] Step S11: during the period of rehabilitation treatment for the rehabilitation patient, the time value corresponding to the current moment is marked as the first rehabilitation monitoring time point, the time value corresponding to the rehabilitation monitoring period before the first rehabilitation monitoring time point is marked as the second rehabilitation monitoring time point, and the period between the first rehabilitation monitoring time point and the second rehabilitation monitoring time point is marked as the patient rehabilitation monitoring cycle;

[0028] Step S12: monitoring the gait speed of the rehabilitation patient in the patient rehabilitation monitoring period to obtain an average deviation of the periodic movement speed;

[0029] The step S12 further includes the following specific steps:

[0030] Step S121: dividing the patient rehabilitation monitoring cycle into a plurality of speed monitoring sub-cycles of equal duration, and randomly selecting a speed monitoring sub-cycle from the obtained plurality of speed monitoring sub-cycles as a sample speed monitoring sub-cycle;

[0031] Step S122: acquiring the cycle start time value corresponding to the sample speed monitoring sub-cycle to obtain a first cycle time value, and acquiring the cycle end time value corresponding to the sample speed monitoring sub-cycle to obtain a second cycle time value;

[0032] Step S123: obtaining the moving distance value of the rehabilitation patient in the sample speed monitoring sub-period to obtain a first moving distance value;

[0033] Step S124: obtaining a periodic moving speed value of the rehabilitation patient in the sample speed monitoring sub-period by calculating the first periodic time value, the second periodic time value and the first moving distance value;

[0034] The specific formula for calculating the periodic moving speed is as follows:

[0035]

[0036] Wherein, Vzq is the periodic moving speed, Yj1 is the first moving distance value, Sjz1 is the first periodic time value, and Sjz2 is the second periodic time value;

[0037] Step S125: acquiring the periodic movement speed value of the rehabilitation patient in each speed monitoring sub-period respectively, to obtain a plurality of periodic movement speed values;

[0038] Step S126: Obtaining the baseline movement speed of the rehabilitation patient during the patient rehabilitation monitoring period to obtain the patient's baseline movement speed;

[0039] Step S127: respectively obtaining the difference between each period movement speed value and the patient's reference movement speed to obtain a plurality of period movement speed deviations, and averaging the obtained plurality of period movement speed deviations to obtain an average period movement speed deviation;

[0040] Step S13: monitoring the gait frequency of the rehabilitation patient in the patient rehabilitation monitoring period to obtain an average deviation of the periodic movement frequency;

[0041] The step S13 further includes the following specific steps:

[0042] Step S131: dividing the patient rehabilitation monitoring cycle into a plurality of cadence monitoring sub-cycles of equal duration, and randomly selecting a cadence monitoring sub-cycle from the obtained plurality of cadence monitoring sub-cycles as a sample cadence monitoring sub-cycle;

[0043] Step S132: acquiring the cycle start time value corresponding to the sample step frequency monitoring sub-cycle to obtain the first step frequency time value, and acquiring the cycle end time value corresponding to the sample delivery monitoring sub-cycle to obtain the second step frequency time value;

[0044] Step S133: Obtain the cumulative number of steps of the rehabilitation patient in the sample step frequency monitoring sub-period to obtain a first cumulative number of steps;

[0045] Step S134: Calculate the first cadence time value, the second cadence time value and the first cumulative number of footsteps to obtain the periodic moving cadence value of the rehabilitation patient in the sample cadence monitoring sub-period;

[0046] The specific formula for calculating the cycle movement frequency value is as follows:

[0047]

[0048] Among them, Bzq is the periodic moving step frequency, Bj1 is the first cumulative number of steps, Bjz1 is the first step frequency time value, and Bjz2 is the second step frequency time value;

[0049] Step S135: acquiring the periodic movement cadence value of the rehabilitation patient in each cadence monitoring sub-period respectively, to obtain a plurality of periodic movement cadence values;

[0050] Step S136: Obtain the benchmark movement cadence of the rehabilitation patient during the patient rehabilitation monitoring period to obtain the patient's benchmark movement cadence;

[0051] Step S137: respectively obtaining the difference between each periodic movement frequency value and the patient's reference movement frequency, obtaining a plurality of periodic movement frequency deviations, and averaging the obtained plurality of periodic movement frequency deviations to obtain an average deviation of the periodic movement frequency;

[0052] Step S14: defining the average deviation of the periodic movement speed and the average deviation of the periodic movement frequency as rehabilitation exercise monitoring data;

[0053] Step S2: in the process of posture monitoring of the rehabilitation patient, multiple posture monitoring cycles are marked, posture monitoring is performed on the rehabilitation patient in each posture monitoring cycle, multiple knee posture deviations are obtained, and rehabilitation exercise knee posture deviation is obtained by analyzing the multiple knee posture deviations;

[0054] The step S2 further includes the following specific steps:

[0055] Step S21: In the patient rehabilitation monitoring cycle, a complete process of each gait movement of the rehabilitation patient is taken as a posture monitoring cycle, and multiple posture monitoring cycles are obtained, and one posture monitoring cycle is arbitrarily selected from the multiple posture monitoring cycles obtained as a sample posture monitoring cycle;

[0056] Step S22: performing posture monitoring on the rehabilitation patient in the sample posture monitoring period to obtain the knee posture deviation corresponding to the sample posture monitoring period;

[0057] The step S22 further includes the following specific steps:

[0058] Step S221: obtaining a first posture characteristic angle;

[0059] The step S221 further includes the following specific steps:

[0060] Step S2211: marking the center point of the knee joint corresponding to the rehabilitation patient as the first posture feature point, marking the center point of the ankle joint corresponding to the rehabilitation patient as the second posture feature point, and marking the center point of the hip joint corresponding to the rehabilitation patient as the third posture feature point;

[0061] Step S2212: connecting the first posture feature point with the second posture feature point to obtain a first posture feature line, and connecting the first posture feature point with the third posture feature point to obtain a second posture feature line;

[0062] Step S2213: marking the included angle between the first posture characteristic line and the second posture characteristic line at the first posture characteristic point as a first posture characteristic angle;

[0063] Step S222: In the sample posture monitoring period, a number of posture monitoring time points are randomly selected, and the interval between each two consecutive posture monitoring times is equal;

[0064] Step S223: respectively obtaining the angle values ​​corresponding to the first posture characteristic angle at each posture monitoring time point in the sample posture monitoring period, obtaining a plurality of characteristic angle values, and comparing the values ​​of the obtained plurality of characteristic angle values, naming the characteristic angle value with the largest value as the first knee posture angle value, and naming the characteristic angle value with the smallest value as the second knee posture angle value;

[0065] Step S224: respectively obtaining a first knee posture angle reference value and a second knee posture angle reference value;

[0066] Step S225: obtaining a knee posture deviation corresponding to a sample posture monitoring period by calculating the first knee posture angle value, the second knee posture angle value, the first knee posture angle reference value, and the second knee posture angle reference value;

[0067] The knee posture deviation is calculated using the following formula:

[0068]

[0069] Wherein, Xpc is the knee posture deviation, Xtz1 is the first knee posture angle value, Xjz1 is the first knee posture angle reference value, Xtz2 is the second knee posture angle value, and Xjz2 is the second knee posture angle reference value;

[0070] Step S23: respectively acquiring the knee posture deviation corresponding to each posture monitoring cycle to obtain a plurality of knee posture deviations, and averaging the obtained plurality of knee posture deviations to obtain the rehabilitation exercise knee posture deviation;

[0071] Step S3: obtaining the joint rehabilitation coefficient corresponding to each patient rehabilitation monitoring period according to the rehabilitation exercise knee posture deviation and the rehabilitation exercise monitoring data, obtaining the joint rehabilitation coefficient threshold and performing numerical comparison with the joint rehabilitation coefficient, and performing periodic rehabilitation tracking feedback on the rehabilitation patient according to the numerical comparison result;

[0072] The step S3 further includes the following specific steps:

[0073] Step S31: Acquire rehabilitation exercise monitoring data, and acquire the average deviation of periodic movement speed and the average deviation of periodic movement frequency according to the rehabilitation exercise monitoring data;

[0074] Step S32: obtaining the rehabilitation exercise knee posture deviation;

[0075] Step S33: calculating the rehabilitation exercise knee posture deviation, the average deviation of the periodic movement speed and the average deviation of the periodic movement frequency to obtain the joint rehabilitation coefficient corresponding to the rehabilitation patient;

[0076] The joint rehabilitation coefficient is calculated as follows:

[0077] Kfx=Ztp 2 +Ydp+Plp;

[0078] Among them, Kfx is the joint rehabilitation coefficient, Ztp is the knee posture deviation of rehabilitation exercise, Ydp is the average deviation of periodic movement speed, and P lp is the average deviation of periodic movement frequency;

[0079] Step S34: respectively obtaining a rehabilitation exercise knee posture deviation threshold, a periodic movement speed average deviation threshold, and a periodic movement frequency average deviation threshold;

[0080] Step S35: calculating the rehabilitation exercise knee posture deviation threshold, the periodic movement speed average deviation threshold, and the periodic movement frequency average deviation threshold to obtain a joint rehabilitation coefficient threshold corresponding to the rehabilitation patient;

[0081] The joint rehabilitation coefficient threshold is calculated using the following formula:

[0082] Kfxy=Ztpy 2 +ydpy+plpy;

[0083] Among them, Kfxy is the threshold of joint rehabilitation coefficient, Ztpy is the threshold of knee posture deviation in rehabilitation exercise, Ydpy is the average deviation threshold of periodic movement speed, and P l py is the average deviation threshold of periodic movement frequency;

[0084] Step S36: numerically comparing the joint rehabilitation coefficient threshold with the joint rehabilitation coefficient, and performing periodic rehabilitation tracking feedback on the rehabilitation patient according to the numerical comparison result;

[0085] The step S36 further includes the following specific steps:

[0086] Step S361: when the joint rehabilitation coefficient is greater than or equal to the joint rehabilitation coefficient threshold, feedback is given that the patient's knee joint rehabilitation status is unqualified;

[0087] Step S362: When the joint rehabilitation coefficient is less than the joint rehabilitation coefficient threshold, it is fed back that the patient's knee joint rehabilitation status is qualified.

[0088] 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.

[0089] Embodiment 2

[0090] See also Figure 2 , based on another concept of the same invention, an intelligent wearable knee joint rehabilitation tracking system is now proposed, including a motion monitoring module, a posture monitoring module, a tracking feedback module and a server, wherein the motion monitoring module, the posture monitoring module and the tracking feedback module are respectively connected to the server, and the server controls the motion monitoring module, the posture monitoring module and the tracking feedback module respectively;

[0091] The motion monitoring module marks a patient rehabilitation monitoring cycle during the rehabilitation treatment period for the rehabilitation patient, obtains the average deviation of the periodic movement speed by monitoring the gait speed of the rehabilitation patient in the patient rehabilitation monitoring cycle, and obtains the average deviation of the periodic movement frequency by monitoring the gait frequency of the rehabilitation patient in the patient rehabilitation monitoring cycle, thereby obtaining rehabilitation motion monitoring data;

[0092] During the period of rehabilitation treatment for the rehabilitation patient, the time value corresponding to the current moment is marked as the first rehabilitation monitoring time point, the time value corresponding to the rehabilitation monitoring period before the first rehabilitation monitoring time point is marked as the second rehabilitation monitoring time point, and the period between the first rehabilitation monitoring time point and the second rehabilitation monitoring time point is marked as the patient rehabilitation monitoring cycle;

[0093] It should be noted here that:

[0094] In this application, the time value corresponding to the rehabilitation monitoring period is specifically 20 minutes;

[0095] In the present application, as the time value corresponding to the current moment changes, the first rehabilitation monitoring time point and the second rehabilitation monitoring time point also change accordingly, thereby achieving dynamic updating of the patient's rehabilitation monitoring cycle;

[0096] In this application, the rehabilitation method of the patient during the patient rehabilitation monitoring period is mainly walking exercise;

[0097] The gait speed of rehabilitation patients in the patient rehabilitation monitoring period is monitored to obtain the average deviation of the periodic movement speed;

[0098] The details are as follows:

[0099] Divide the patient rehabilitation monitoring cycle into a plurality of speed monitoring sub-cycles of equal duration, and randomly select a speed monitoring sub-cycle from the obtained plurality of speed monitoring sub-cycles as a sample speed monitoring sub-cycle;

[0100] The cycle start time value corresponding to the sample speed monitoring sub-cycle is obtained to obtain a first cycle time value, and the cycle end time value corresponding to the sample speed monitoring sub-cycle is obtained to obtain a second cycle time value;

[0101] Obtaining a moving distance value of the rehabilitation patient in a sample speed monitoring sub-period to obtain a first moving distance value;

[0102] The first cycle time value, the second cycle time value and the first moving distance value are calculated to obtain the cycle moving speed value of the rehabilitation patient in the sample speed monitoring sub-cycle;

[0103] The specific formula for calculating the periodic moving speed is as follows:

[0104]

[0105] Wherein, Vzq is the periodic moving speed, Yj1 is the first moving distance value, Sjz1 is the first periodic time value, and Sjz2 is the second periodic time value;

[0106] Repeat the process of obtaining the periodic movement speed value in the sample speed monitoring sub-period, respectively obtain the periodic movement speed value of the rehabilitation patient in each speed monitoring sub-period, and obtain multiple periodic movement speed values;

[0107] Obtaining a baseline movement speed of a rehabilitation patient during a patient rehabilitation monitoring period to obtain a patient baseline movement speed;

[0108] It should be noted here that:

[0109] The patient's baseline movement speed involved here is the optimal movement speed determined by the doctor based on the patient's physical recovery condition;

[0110] The difference between each period movement speed value and the patient's baseline movement speed is obtained respectively to obtain a plurality of period movement speed deviations, and the average of the obtained plurality of period movement speed deviations is calculated to obtain an average deviation of the period movement speed;

[0111] The gait frequency of the rehabilitation patients in the patient rehabilitation monitoring period is monitored to obtain the average deviation of the periodic movement frequency;

[0112] The details are as follows:

[0113] Divide the patient's rehabilitation monitoring cycle into a number of cadence monitoring sub-cycles of equal duration, and randomly select a cadence monitoring sub-cycle from the obtained multiple cadence monitoring sub-cycles as a sample cadence monitoring sub-cycle;

[0114] The cycle start time value corresponding to the sample step frequency monitoring sub-cycle is obtained to obtain the first step frequency time value, and the cycle end time value corresponding to the sample delivery monitoring sub-cycle is obtained to obtain the second step frequency time value;

[0115] Obtain the cumulative number of footsteps of the rehabilitation patient in the sample cadence monitoring sub-period to obtain a first cumulative number of footsteps;

[0116] The first step frequency time value, the second step frequency time value and the first cumulative number of footsteps are calculated to obtain the periodic movement frequency value of the rehabilitation patient in the sample step frequency monitoring sub-period;

[0117] The specific formula for calculating the cycle movement frequency value is as follows:

[0118]

[0119] Among them, Bzq is the periodic moving step frequency, Bj1 is the first cumulative number of steps, Bjz1 is the first step frequency time value, and Bjz2 is the second step frequency time value;

[0120] Repeat the process of obtaining the periodic moving cadence value in the sample cadence monitoring sub-cycle, respectively obtain the periodic moving cadence value of the rehabilitation patient in each cadence monitoring sub-cycle, and obtain multiple periodic moving cadence values;

[0121] Obtaining the baseline movement cadence of the rehabilitation patient during the patient rehabilitation monitoring period to obtain the patient's baseline movement cadence;

[0122] It should be noted here that:

[0123] The patient's baseline movement frequency involved here is the optimal movement frequency determined by the doctor based on the patient's physical recovery condition;

[0124] The difference between each cycle movement frequency value and the patient's baseline movement frequency is obtained respectively to obtain a plurality of cycle movement frequency deviations, and the average of the obtained plurality of cycle movement frequency deviations is calculated to obtain an average deviation of the cycle movement frequency;

[0125] The average deviation of periodic movement speed and the average deviation of periodic movement frequency are defined as rehabilitation movement monitoring data;

[0126] The motion monitoring module acquires the rehabilitation motion monitoring data and transmits it to the tracking feedback module;

[0127] The posture monitoring module marks multiple posture monitoring cycles during posture monitoring of rehabilitation patients, performs posture monitoring on rehabilitation patients in each posture monitoring cycle, obtains multiple knee posture deviations, and obtains rehabilitation exercise knee posture deviations by analyzing the multiple knee posture deviations;

[0128] The details are as follows:

[0129] In the patient rehabilitation monitoring cycle, the complete process of each gait movement of the rehabilitation patient is taken as a posture monitoring cycle, and multiple posture monitoring cycles are obtained, and one posture monitoring cycle is arbitrarily selected from the multiple posture monitoring cycles obtained as a sample posture monitoring cycle;

[0130] Performing posture monitoring on rehabilitation patients in a sample posture monitoring period to obtain a knee posture deviation corresponding to the sample posture monitoring period;

[0131] The details are as follows:

[0132] See also Figure 3 , marking the center point of the knee joint corresponding to the rehabilitation patient as the first posture feature point, marking the center point of the ankle joint corresponding to the rehabilitation patient as the second posture feature point, and marking the center point of the hip joint corresponding to the rehabilitation patient as the third posture feature point;

[0133] Connecting the first posture feature point with the second posture feature point to obtain a first posture feature line, and connecting the first posture feature point with the third posture feature point to obtain a second posture feature line;

[0134] Marking the included angle between the first posture characteristic line and the second posture characteristic line at the first posture characteristic point as a first posture characteristic angle;

[0135] In the sample posture monitoring cycle, several posture monitoring time points are randomly selected, and the interval between each two consecutive posture monitoring times is equal;

[0136] Respectively obtain the angle values ​​corresponding to the first posture characteristic angle at each posture monitoring time point in the sample posture monitoring period to obtain multiple characteristic angle values, and compare the values ​​of the obtained multiple characteristic angle values, name the characteristic angle value with the largest value as the first knee posture angle value, and name the characteristic angle value with the smallest value as the second knee posture angle value;

[0137] respectively obtaining a first knee posture angle reference value and a second knee posture angle reference value;

[0138] It should be noted here that:

[0139] The first knee posture angle reference value involved here is the maximum knee angle value of the rehabilitation patient during walking, and the second knee posture angle reference value involved here is the minimum knee angle value of an adult during walking;

[0140] The first knee posture angle value, the second knee posture angle value, the first knee posture angle reference value and the second knee posture angle reference value are calculated to obtain a knee posture deviation corresponding to a sample posture monitoring period;

[0141] The knee posture deviation is calculated using the following formula:

[0142]

[0143] Wherein, Xpc is the knee posture deviation, Xtz1 is the first knee posture angle value, Xjz1 is the first knee posture angle reference value, Xtz2 is the second knee posture angle value, and Xjz2 is the second knee posture angle reference value;

[0144] Acquire the knee posture deviation corresponding to each posture monitoring cycle respectively to obtain multiple knee posture deviations, and average the obtained multiple knee posture deviations to obtain the rehabilitation exercise knee posture deviation;

[0145] The posture monitoring module acquires the knee posture deviation during rehabilitation exercise and transmits it to the tracking feedback module;

[0146] The tracking and feedback module obtains the joint rehabilitation coefficient corresponding to each patient's rehabilitation monitoring cycle according to the rehabilitation exercise knee posture deviation and rehabilitation exercise monitoring data, obtains the joint rehabilitation coefficient threshold and compares it with the joint rehabilitation coefficient, and performs periodic rehabilitation tracking and feedback on the rehabilitation patients according to the numerical comparison results;

[0147] Acquire rehabilitation exercise monitoring data, and acquire a periodic movement speed average deviation and a periodic movement frequency average deviation according to the rehabilitation exercise monitoring data;

[0148] Obtain knee posture deviation during rehabilitation exercises;

[0149] The knee posture deviation, the average deviation of the periodic movement speed and the average deviation of the periodic movement frequency of the rehabilitation exercise are calculated to obtain the corresponding joint rehabilitation coefficient of the rehabilitation patient;

[0150] The joint rehabilitation coefficient is calculated as follows:

[0151] Kfx=Ztp 2 +Ydp+Plp;

[0152] Among them, Kfx is the joint rehabilitation coefficient, Ztp is the knee posture deviation of rehabilitation exercise, Ydp is the average deviation of periodic movement speed, and P lp is the average deviation of periodic movement frequency;

[0153] Obtaining the threshold value of joint rehabilitation coefficient;

[0154] The details are as follows:

[0155] Respectively obtain the rehabilitation exercise knee posture deviation threshold, the periodic movement speed average deviation threshold and the periodic movement frequency average deviation threshold;

[0156] It should be noted here that:

[0157] In the present application, the rehabilitation exercise knee posture deviation threshold, the periodic movement speed average deviation threshold and the periodic movement frequency average deviation threshold involved here are all the minimum rehabilitation exercise knee posture deviation, the minimum periodic movement speed average deviation and the minimum periodic movement frequency average deviation corresponding to patients with qualified knee joint rehabilitation status;

[0158] The rehabilitation exercise knee posture deviation threshold, the periodic movement speed average deviation threshold and the periodic movement frequency average deviation threshold are calculated to obtain the joint rehabilitation coefficient threshold corresponding to the rehabilitation patient;

[0159] The joint rehabilitation coefficient threshold is calculated using the following formula:

[0160] Kfxy=Ztpy 2 +ydpy+plpy;

[0161] Among them, Kfxy is the threshold of joint rehabilitation coefficient, Ztpy is the threshold of knee posture deviation in rehabilitation exercise, Ydpy is the average deviation threshold of periodic movement speed, and P l py is the average deviation threshold of periodic movement frequency;

[0162] The joint rehabilitation coefficient threshold is numerically compared with the joint rehabilitation coefficient, and periodic rehabilitation tracking feedback is provided to the rehabilitation patients according to the numerical comparison results;

[0163] The details are as follows:

[0164] When the joint rehabilitation coefficient is greater than or equal to the joint rehabilitation coefficient threshold, it is fed back that the patient's knee joint rehabilitation status is unqualified;

[0165] When the joint rehabilitation coefficient is less than the joint rehabilitation coefficient threshold, it is fed back that the patient's knee joint rehabilitation status is qualified.

[0166] 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 smart wearable knee joint rehabilitation tracking method, characterized in that: The specific steps include: Step S1: marking a patient rehabilitation monitoring period during the period of rehabilitation treatment for the rehabilitation patient, obtaining the average deviation of the periodic movement speed by monitoring the gait speed of the rehabilitation patient in the patient rehabilitation monitoring period, and obtaining the average deviation of the periodic movement frequency by monitoring the gait frequency of the rehabilitation patient in the patient rehabilitation monitoring period, and obtaining rehabilitation movement monitoring data; Step S2: in the process of posture monitoring of the rehabilitation patient, multiple posture monitoring cycles are marked, posture monitoring is performed on the rehabilitation patient in each posture monitoring cycle and the knee posture deviation is obtained, and the rehabilitation exercise knee posture deviation is obtained by analyzing the multiple knee posture deviations; Step S3: According to the rehabilitation exercise knee posture deviation and the rehabilitation exercise monitoring data, the joint rehabilitation coefficient corresponding to each patient rehabilitation monitoring cycle is obtained respectively, and the joint rehabilitation coefficient threshold is obtained and compared with the joint rehabilitation coefficient numerically. According to the numerical comparison result, periodic rehabilitation tracking is performed on the rehabilitation patients and the tracking results are fed back.

2. The smart wearable knee joint rehabilitation tracking method according to claim 1, characterized in that: The step S1 further includes the following specific steps: Step S11: during the period of rehabilitation treatment for the rehabilitation patient, the time value corresponding to the current moment is marked as the first rehabilitation monitoring time point, the time value corresponding to the rehabilitation monitoring period before the first rehabilitation monitoring time point is marked as the second rehabilitation monitoring time point, and the period between the first rehabilitation monitoring time point and the second rehabilitation monitoring time point is marked as the patient rehabilitation monitoring cycle; Step S12: monitoring the gait speed of the rehabilitation patient in the patient rehabilitation monitoring period to obtain an average deviation of the periodic movement speed; Step S13: monitoring the gait frequency of the rehabilitation patient in the patient rehabilitation monitoring period to obtain an average deviation of the periodic movement frequency; Step S14: defining the average deviation of the periodic movement speed and the average deviation of the periodic movement frequency as rehabilitation exercise monitoring data.

3. The smart wearable knee joint rehabilitation tracking method according to claim 2, characterized in that: The step S12 further includes the following specific steps: step S121: dividing the patient rehabilitation monitoring cycle into a plurality of speed monitoring sub-cycles of equal duration, and randomly selecting a speed monitoring sub-cycle from the obtained plurality of speed monitoring sub-cycles as a sample speed monitoring sub-cycle; Step S122: acquiring the cycle start time value corresponding to the sample speed monitoring sub-cycle to obtain a first cycle time value, and acquiring the cycle end time value corresponding to the sample speed monitoring sub-cycle to obtain a second cycle time value.

4. The smart wearable knee joint rehabilitation tracking method according to claim 3, characterized in that: The step S12 further includes the following specific steps: Step S123: obtaining the moving distance value of the rehabilitation patient in the sample speed monitoring sub-period to obtain a first moving distance value; Step S124: obtaining a periodic moving speed value of the rehabilitation patient in the sample speed monitoring sub-period by calculating the first periodic time value, the second periodic time value and the first moving distance value; Calculate the periodic moving speed value.

5. The smart wearable knee joint rehabilitation tracking method according to claim 4, characterized in that: The step S12 further includes the following specific steps: Step S125: acquiring the periodic movement speed value of the rehabilitation patient in each speed monitoring sub-period respectively, to obtain a plurality of periodic movement speed values; Step S126: acquiring a baseline movement speed of the rehabilitation patient during the patient rehabilitation monitoring period to obtain a baseline movement speed of the patient; Step S127: respectively obtaining the difference between each period movement speed value and the patient's reference movement speed to obtain a plurality of period movement speed deviations, and averaging the obtained plurality of period movement speed deviations to obtain an average period movement speed deviation.

6. The smart wearable knee joint rehabilitation tracking method according to claim 5, characterized in that: The step S13 further includes the following specific steps: Step S131: Divide the patient rehabilitation monitoring cycle into a plurality of cadence monitoring sub-cycles of equal duration, and randomly select a cadence monitoring sub-cycle from the obtained plurality of cadence monitoring sub-cycles as a sample cadence monitoring sub-cycle.

7. The smart wearable knee joint rehabilitation tracking method according to claim 6, characterized in that: The step S13 further includes the following specific steps: Step S132: Acquire the cycle start time value corresponding to the sample cadence monitoring sub-cycle to obtain the first cadence time value, and acquire the cycle end time value corresponding to the sample delivery monitoring sub-cycle to obtain the second cadence time value.

8. The smart wearable knee joint rehabilitation tracking method according to claim 7, characterized in that: The step S13 further includes the following specific steps: Step S133: Obtain the cumulative number of steps of the rehabilitation patient in the sample step frequency monitoring sub-period to obtain a first cumulative number of steps; Step S134: Calculate the first cadence time value, the second cadence time value and the first cumulative number of footsteps to obtain the periodic moving cadence value of the rehabilitation patient in the sample cadence monitoring sub-period; Calculate the value of the periodic movement frequency.

9. The smart wearable knee joint rehabilitation tracking method according to claim 8, characterized in that: The step S13 further includes the following specific steps: Step S135: acquiring the periodic movement cadence value of the rehabilitation patient in each cadence monitoring sub-period respectively, to obtain a plurality of periodic movement cadence values; Step S136: Obtain the benchmark movement cadence of the rehabilitation patient during the patient rehabilitation monitoring period to obtain the patient's benchmark movement cadence; Step S137: respectively obtain the difference between each periodic movement frequency value and the patient's reference movement frequency, obtain multiple periodic movement frequency deviations, average the obtained multiple periodic movement frequency deviations, and obtain the periodic movement frequency average deviation.

10. An intelligent wearable knee joint rehabilitation tracking system, applicable to the intelligent wearable knee joint rehabilitation tracking method according to any one of claims 1 to 9, characterized in that: The specific working process of each module of the rehabilitation tracking system is as follows: Movement monitoring module: used to mark a patient rehabilitation monitoring period during the period of rehabilitation treatment for the rehabilitation patient, obtain the average deviation of the periodic movement speed by monitoring the gait speed of the rehabilitation patient in the patient rehabilitation monitoring period, and obtain the average deviation of the periodic movement frequency by monitoring the gait frequency of the rehabilitation patient in the patient rehabilitation monitoring period, so as to obtain rehabilitation movement monitoring data; Posture monitoring module: used to mark multiple posture monitoring cycles in the process of posture monitoring of rehabilitation patients, perform posture monitoring on rehabilitation patients in each posture monitoring cycle, obtain multiple knee posture deviations, and obtain rehabilitation exercise knee posture deviation by analyzing multiple knee posture deviations; Tracking and feedback module: It is used to obtain the joint rehabilitation coefficient corresponding to each patient's rehabilitation monitoring cycle according to the rehabilitation exercise knee posture deviation and rehabilitation exercise monitoring data, and obtain the joint rehabilitation coefficient threshold and compare it with the joint rehabilitation coefficient numerically, and conduct periodic rehabilitation tracking feedback for the rehabilitation patients according to the numerical comparison results.

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