Patient rehabilitation effect optimization method adopting rehabilitation equipment
By collecting and optimizing patient data in lower limb rehabilitation training, the problem of lack of quantitative indicators and inaccurate correction of therapists in the prior art is solved, and more efficient and accurate rehabilitation training results are achieved, reducing treatment costs.
Patent Information
- Application Number
- CN202411755729.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-03
- Publication Date
- 2025-03-25
AI Technical Summary
The lack of quantitative indicators for existing lower limb rehabilitation training leads to inaccuracy in the therapists when correcting their movements, affecting the rehabilitation effect, and inexperienced therapists lead to inefficiency and high cost.
By collecting patient rehabilitation data, including acceleration, heading angle, geomagnetic and curvature data, error correction and optimization are performed, the acceleration of patient movement, leg bending degree and step length are designed, and health data for the next treatment are remotely guided and optimized.
Accurate acquisition and optimization of patient health effects is achieved, the effectiveness of rehabilitation equipment is improved, and the therapist is allowed to provide remote guidance and assistance, which improves treatment efficiency and reduces costs.
Smart Images
Figure CN119694491A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of rehabilitation equipment control, and in particular to a method for optimizing the health effect of a patient using rehabilitation equipment. Background Art
[0002] Stroke is a common disease that can cause paralysis and bring great inconvenience to life. Through a large amount of rehabilitation training, paralyzed stroke patients can stimulate brain plasticity, restore certain motor skills, achieve self-care, improve quality of life, and return to society to the greatest extent. At present, most lower limb rehabilitation training requires therapists to detect patients in real time and manually correct rehabilitation movements. This type of rehabilitation training lacks quantitative indicators, and there will be inaccurate corrections during the manual correction process, which seriously affects the effect of rehabilitation training. In addition, due to the lack of experienced therapists, the treatment efficiency is low, the cost is high, and the labor cost is high.
[0003] As the number of rehabilitation treatments for stroke patients increases, patients can purchase equipment and perform rehabilitation treatment at home. However, since there is no therapist to cooperate with them when they perform rehabilitation treatment on their own, even with the cooperation of caregivers or family members, it is still impossible to accurately judge the patient's recovery status, resulting in poor rehabilitation results. Summary of the invention
[0004] The technical problem to be solved by the present invention is that: the present invention provides a method for optimizing the health effects of patients using rehabilitation equipment, which can accurately obtain the patient's health conditions, improve the effects of the rehabilitation equipment, and the therapist can provide remote guidance and assistance.
[0005] In order to solve the above technical problems, the technical solution proposed by the present invention is:
[0006] A method for optimizing the health effects of patients using rehabilitation equipment, the optimization method comprising:
[0007] Step S1, collecting the patient's rehabilitation data
[0008] Collect data on patients' rehabilitation walking movements, including acceleration data, heading angle data, geomagnetic data, and curvature;
[0009] Step S2: Error correction of data
[0010] Determine the acceleration data, heading angle data and geomagnetic data corresponding to the non-zero speed state according to the zero speed state, obtain the current speed according to the data of the non-zero speed state, and correct the quaternion and heading angle data; process the acceleration data with the corrected quaternion to obtain the acceleration data under the non-zero speed state, and calculate the corresponding displacement;
[0011] Step S3, optimizing the patient's rehabilitation
[0012] The next rehabilitation exercise is planned based on the comparison of the patient's current rehabilitation exercise data with the normal person's exercise data or the expected transportation data.
[0013] The improvement of the above technical solution is as follows:
[0014] Preferably, in step S3, the method for confirming the zero speed state is:
[0015] (1) When the patient is in a slow walking mode, the acceleration modulus corresponding to the acceleration data and the heading angle modulus corresponding to the heading angle data are calculated; whether the acceleration modulus is within a first threshold range and whether the heading angle modulus is within a second threshold range is determined; when the acceleration modulus is within the first threshold range and the heading angle modulus is within the second threshold range, the patient is currently in a zero speed state;
[0016] (2) calculating the heading angle variance corresponding to the heading angle data and the acceleration variance corresponding to the acceleration data; determining whether the acceleration variance is within a third threshold range and whether the heading angle variance is within a fourth threshold range; when the acceleration variance is within the third threshold range and the heading angle variance is within the fourth threshold range, the patient is in a zero speed state;
[0017] (3) In a gait cycle, the bending degree of the knee joint reaches a minimum when the patient's heel touches the ground, and reaches a minimum when the toes leave the ground, and then the bending degree gradually increases; the state set between two adjacent minimum values is the stage of foot landing, this interval is the zero speed interval, and the foot speed is zero.
[0018] Preferably, in step S2, the gravity vector and the geomagnetic vector in the geographic coordinate system are obtained, and converted into the carrier coordinate system to obtain a first standard vector corresponding to the gravity vector, and a second standard vector corresponding to the geomagnetic vector; the first standard vector is compared with the gravity acceleration measured in the carrier coordinate system to obtain a first error vector, and the second standard vector is compared with the geomagnetic vector measured in the carrier coordinate system to obtain a first error vector; the quaternion and the heading angle data are corrected by the first error vector and the second error vector; the collected acceleration data is processed by the corrected quaternion to obtain the acceleration data in the non-zero speed state in the geographic coordinate system.
[0019] Preferably, in step S2, the position of the patient is estimated to calculate the corresponding displacement, and the patient's velocity can be expressed in the geographic coordinate system as:
[0020]
[0021] In the formula, v n represents the patient's speed, is the Columbia acceleration, represents the centripetal acceleration, gn Represents the local gravity acceleration value, and C represents the rotation matrix;
[0022] When calculating the displacement of the patient's rehabilitation exercise, it is necessary to compensate for the acceleration error. The patient's position is expressed as:
[0023]
[0024] in, The patient's current location, is the patient's position in the previous sampling cycle, is the patient's current speed, is the patient's speed in the previous sampling period, and Δt is the sampling period.
[0025] The method for optimizing the health effect of patients using rehabilitation equipment provided by the present invention has the following advantages compared with the prior art:
[0026] The method for optimizing the health effects of patients using rehabilitation equipment of the present invention first designs the acceleration of the patient's movement, the degree of leg bending and the length of each step according to the patient's condition; then monitors the acceleration, degree of leg bending and step length of the patient during rehabilitation, compares them with the designed data, and then optimizes and adjusts the health data of the next course of treatment, so as to provide accurate rehabilitation treatment for each patient. In addition, the optimization method of the present invention is used for a variety of health exercises such as walking, climbing stairs, climbing slopes, etc., with a wide range of applications and a simple and accurate control process. BRIEF DESCRIPTION OF THE DRAWINGS
[0027] Figure 1 It is a schematic diagram of rehabilitation exercise for patients of the present invention. DETAILED DESCRIPTION
[0028] The specific embodiments of the present invention are described in detail below. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.
[0029] The method for optimizing the health effect of patients using rehabilitation equipment of the present invention is mainly used for patients who are undergoing health training indoors, especially patients who are undergoing lower limb health training at home. The optimization system includes a main control unit and a detection unit, the detection unit includes a magnetic sensor, a bending sensor, an acceleration sensor and a gyroscope sensor, and the detection unit communicates wirelessly with the main control unit. The acceleration sensor, the gyroscope sensor and the magnetometer are designed as one body, or can be installed together.
[0030] In this embodiment, the gyro sensor and the acceleration sensor are installed on a structure located at the tip of the patient's shoe in the rehabilitation equipment. If there is no such structure, a simple shoe cover structure can be designed and installed on the shoe cover. The gyro sensor and the acceleration sensor collect the patient's heading angle data and acceleration data respectively. The bending sensor is installed on a structure worn at the knee joint position in the rehabilitation equipment. If the existing rehabilitation equipment does not have a structure at the knee joint position, a knee pad can be worn and the bending sensor can be installed on the knee pad. The bending sensor collects the bending degree of the patient's knee joint when walking.
[0031] In this embodiment, the main control unit is provided with a controller and a memory card. The controller can be integrated with the gyroscope sensor, acceleration sensor and magnetometer, or can be installed separately on the rehabilitation device. The memory card is integrated with the controller to record motion data.
[0032] When patients are doing lower limb rehabilitation exercises, their lower limbs have a characteristic of performing a periodic or non-periodic reciprocating motion, which can be roughly divided into two states: zero-speed state and swing state. From the time when any position of the foot begins to touch the ground to the time when the toes completely leave the ground, the patient's lower limb speed is zero, and the patient's position does not change. This state can be considered as the zero-speed state; outside the zero-speed state, the state of the lower limbs can be called the swing state.
[0033] The method for optimizing the health effect of patient rehabilitation equipment of the present invention comprises the following steps:
[0034] Step S1: Patient wears rehabilitation equipment
[0035] The wearable device should have structure for the foot and knee area.
[0036] Step S2, collecting motion data
[0037] Data collection of patients' walking information during rehabilitation, including acceleration data, heading angle data, geomagnetic data and curvature. Gyroscope sensors and acceleration sensors collect heading angle data and acceleration data, magnetic sensors collect geomagnetic data, and curvature sensors collect curvature.
[0038] Step S3: error correction of the collected data
[0039] Ideally, the output of the acceleration sensor and gyroscope sensor will not change when they are stationary. However, in actual use, because the walking state of rehabilitation patients is different from that of normal people, they will start slowly, and the state of their legs when walking is not completely in a periodic swing state, which will cause the sensor to drift and produce errors, resulting in unstable sensor output. Therefore, this error needs to be corrected to accurately understand the recovery of the patient's lower limbs during rehabilitation treatment.
[0040] The error correction method of this embodiment specifically includes the following steps:
[0041] Step S3-1, determine the zero speed state
[0042] (1) When the patient is in a slow walking mode, the acceleration modulus corresponding to the acceleration data and the heading angle modulus corresponding to the heading angle data are calculated; and it is determined whether the acceleration modulus is within a first threshold range and whether the heading angle modulus is within a second threshold range. When the acceleration modulus is within the first threshold range and the heading angle modulus is within the second threshold range, the patient is currently in a zero speed state.
[0043] In this embodiment, the periodic changes of the three-axis data output by the acceleration sensor and the three-axis data output by the gyroscope sensor are used to perform variance processing on the output sensor data and the three-axis data to comprehensively judge the gait of the patient.
[0044] In the zero speed state, the speed of the human foot is zero. Obviously, in an ideal situation, the modulus of the acceleration sensor output should be the local gravity acceleration, and the output of the gyroscope is zero. However, in actual tests, the sensor output is not like this, so the patient's zero speed judgment is performed by setting the corresponding threshold. The modulus of the acceleration sensor output is:
[0045]
[0046] In the formula, as well as They are the acceleration values output by the x, y, and z axes in the carrier coordinate system. The modulus of the three-axis angular velocity output is:
[0047]
[0048] In the formula, as well as They are the angular velocity values output on the x, y, and z axes in the carrier coordinate system respectively.
[0049] The zero speed state is judged by setting the corresponding threshold:
[0050]
[0051] In the formula, th amin and amax are the minimum and maximum values of the thresholds set for acceleration, respectively. ω The threshold value is set for the angular velocity, and the size of the threshold value can be set according to the gait characteristics of different patients. If the threshold value is within the range, the patient is considered to be in a zero-speed state, represented by 1, otherwise the patient is considered to be in a swinging state, represented by 0.
[0052] (2) Calculate the heading angle variance corresponding to the heading angle data and the acceleration variance corresponding to the acceleration data; determine whether the acceleration variance is within a third threshold range and whether the heading angle variance is within a fourth threshold range; when the acceleration variance is within the third threshold range and the heading angle variance is within the fourth threshold range, the patient is in a zero speed state.
[0053] When the variance of acceleration is lower than the set threshold value and the variance of angular velocity is lower than the set threshold value during a certain period of time, the patient is in a zero-speed state. The variance of acceleration is:
[0054]
[0055] In the formula, is the mean value of the modulus of the data measured by the accelerometer, and s is the set window size. The zero-speed state is judged by setting the corresponding threshold in a certain interval:
[0056]
[0057] In the formula, is the acceleration variance threshold, is the angular velocity variance threshold, and its size can also be set according to the gait of different patients.
[0058] If the value is within the threshold range, the patient is considered to be in a zero-speed state, which is represented by 1; otherwise, the patient is considered to be in a swinging state, which is represented by 0.
[0059] (3) In this embodiment, the bending sensor uses piezoelectric material. When the bending degree of the object under test changes, the resistance of the sensor also changes accordingly, showing a characteristic that the greater the bending, the smaller the resistance. After the bending sensor is installed at the knee joint position, the change in the knee joint angle of the patient during rehabilitation exercise is measured. There is interference noise in the measurement process. This embodiment uses a moving average filter algorithm to smooth the signal. The moving average filter convolution is implemented in the form of:
[0060]
[0061] Where x(i+j) represents the input signal, y(i) represents the output signal, and M is the number of points required to obtain the average.
[0062] In a gait cycle, the knee flexion reaches a minimum when the patient's heel touches the ground, and reaches a minimum when the toes leave the ground, and then the flexion gradually increases. The state set between two adjacent minimum values is the stage of foot landing. This interval is the zero speed interval, and the foot speed is zero.
[0063] The rehabilitation exercises of patients are different from normal people's walking. Each patient has a different walking speed and each step may be irregular. Therefore, in the first few rehabilitation exercises, the therapist needs to give a suitable threshold value based on the patient's condition, and the therapist needs to change the threshold value at intervals according to the patient's rehabilitation condition. The same value cannot be used all the time.
[0064] Step S3-2, calculating the patient's stride length
[0065] When patients are undergoing health training, the faster the walking frequency, the longer the corresponding stride length will be, and the slower the walking frequency, the shorter the corresponding stride length will be. The corresponding stride length can be obtained from the obtained stride frequency.
[0066] After obtaining the zero-speed state, the acceleration data, heading angle data and geomagnetic data corresponding to the non-zero-speed state can be determined. The main control unit can process these data to obtain the current speed, and obtain the corresponding displacement based on the current speed to determine the current position.
[0067] In this embodiment, the data of the non-zero speed state can be clarified based on the zero speed state, and the speed of the current state is obtained based on the speed of the previous state and the acceleration data of the non-zero speed state in the geographic coordinate system; the current displacement is obtained by integrating the current speed.
[0068] Step S3-3, correcting the motion data
[0069] The gravity vector and the geomagnetic vector in the geographic coordinate system are obtained, and converted into the carrier coordinate system to obtain a first standard vector corresponding to the gravity vector and a second standard vector corresponding to the geomagnetic vector; the first standard vector is compared with the gravity acceleration measured in the carrier coordinate system to obtain a first error vector, and the second standard vector is compared with the geomagnetic vector measured in the carrier coordinate system to obtain a first error vector; the quaternion and the heading angle data are corrected by the first error vector and the second error vector; the acceleration sensor and the collected acceleration data are processed by the corrected quaternion to obtain the acceleration data in the non-zero speed state in the geographic coordinate system.
[0070] Define a vector in the carrier coordinate system It can be expressed as: Then it can be expressed as r in the geographic coordinate system n There is r n =qr b q * ,in *
[0071] q is the conjugate complex number of q.
[0072] Euler angles can be expressed as quaternions:
[0073]
[0074] If a quaternion can be used to represent the relationship between the carrier coordinate system and the geographic coordinate system, the quaternion can be corrected by using the different data of the accelerometer and gyroscope sensors in the two coordinate systems, that is, the rotation matrix. The gravity vector can be represented as (0,0,g) in the geographic coordinate system, which can be transformed to the carrier coordinate system through the rotation matrix. The rotation matrix can be corrected by comparing the measured data with the transformed data. The specific process is as follows:
[0075] 1) Use h x 、h y 、h z Respectively represent the geomagnetic measurement data rotated to the geographic coordinate system, where:
[0076] h x =2*m x (0.5f-q2q2-q3q3)+2*m y (q1q2-q0q3)+2*m z (q1q3-q0q2) (10)
[0077] h y =2*m y (0.5f-q1q1-q3q3)+2*m x (q1q2-q0q3)+2*m z (q2q3-q0q1) (11)
[0078] h z =2*m z (0.5f-q1q1-q2q2)+2*m x (q1q3-q0q2)+2*m y (q2q3-q0q1) (12)
[0079] Use b x 、b y 、b z They represent the components in the x, y, and z directions respectively, q0, q1, q2, and q3 represent the four elements of the quaternion, m represents the geomagnetic measurement data in the carrier coordinate system, and m x 、m y 、m z Respectively represent the components in the x, y, and z axis directions, where b y is 0, b z and h z same:
[0080] b x =sqrt((hx *h x )+(h y *h y )) (13)
[0081] b y =0 (14)
[0082] b z =h z (15)
[0083] 2) Perform left multiplication on the gravity vector (0,0,g) and the magnetic field vector and transform them to the carrier coordinate system:
[0084] v x =2*(q1q3-q0q2) (16)
[0085] v y =2*(q0q1+q2q3) (17)
[0086] v z =q0q0-q1q1-q2q2+q3q3 (18)
[0087] w x =2*b x *(0.5-q2q2-q3q3)+2*b z *(q1q3-q0q2) (19)
[0088] w y =2*b x *(q1q2-q0q3)+2*b z *(q0q1+q2q3) (20)
[0089] w z =2*b x *(q0q2+q1q3)+2*b z *(0.5-q1q1-q2q2) (21)
[0090] 3) Compare the vector obtained in 2) with the measurement data of the acceleration sensor and the gyroscope sensor to obtain the error vector, and then correct the quaternion and the gyroscope sensor data, where a represents the acceleration measurement data in the carrier coordinate system, ax, ay, as represent the components in the x, y, s axis directions respectively, m represents the gyroscope sensor measurement data in the carrier coordinate system, and m x 、m y 、m z They represent the components in the x, y, and z axis directions respectively, and e1, e2, e3, e4, e5, and e6 are error vectors.
[0091] e1=a x -v x (twenty two)
[0092] e2=a y -v y (twenty three)
[0093] e3=a z -v z (twenty four)
[0094] e4=m x -w x (25)
[0095] e5=m y -w y (26)
[0096] e6=m z -w z (27)
[0097] 4) The error of the gyroscope sensor is proportional to the size of the error vector. The quaternion and the gyroscope sensor data can be corrected by multiplying the error vector obtained in 3) by a gain matrix K.
[0098] T=[e1 e2 e3 e4 e5 e6] (28)
[0099] Y=K*T′ (29)
[0100]
[0101]
[0102] Normalize the quaternion:
[0103]
[0104] but:
[0105]
[0106] 5) Convert quaternion to Euler angle:
[0107]
[0108] The position estimation algorithm of this embodiment is described in detail below:
[0109] The patient's velocity can be expressed in the geographic coordinate system as:
[0110]
[0111] In the formula, v nrepresents the patient's speed, is the Columbia acceleration, represents the centripetal acceleration, g n Represents the local gravity acceleration value, and C represents the rotation matrix.
[0112] When calculating the displacement of the patient's rehabilitation exercise, it is necessary to compensate for the acceleration error to reduce the system error.
[0113] When calculating the patient's velocity, the Coriolis acceleration and centripetal acceleration can be ignored, and the velocity update expression is:
[0114]
[0115] Where Δt is the sampling period.
[0116] The patient's position is obtained by integrating the velocity, which can be expressed as:
[0117]
[0118] The position update formula can be further obtained as:
[0119]
[0120] The above formula can be used to obtain the position estimation data of the patient's current position. When a normal person does not change the route when walking, the stride length of each step is the same and the difference is not large. However, each step of a rehabilitation patient may be different, so it is necessary to obtain the stride length of each step of the patient based on the position estimation data.
[0121] Step S4, optimizing the rehabilitation design for the patient
[0122] Based on the corrected and accurate motion data, that is, according to the degree of knee flexion, walking acceleration and stride length of the patient's lower limb rehabilitation exercises, the therapist can remotely formulate rehabilitation goals for the next stage based on the patient's rehabilitation situation in one stage. It can remotely adjust according to the different situations of each patient, thereby improving the patient's rehabilitation effect at home.
[0123] The patient's geomagnetic data is collected, and the data collected by the inertial measurement unit, magnetic sensor and bending sensor are processed by the main control chip, and the positioning results are stored in the memory card. The main control chip calculates and processes the collected data to obtain the patient's indoor positioning data; in the present invention, the inertial sensor is connected to the patient's toes to collect the patient's movement information. The above processing can significantly reduce the error caused by the drift of the inertial sensor, effectively improve the patient's indoor positioning accuracy, and can judge the patient's recovery status by the patient's speed and bending degree.
[0124] The above implementation cases are only preferred embodiments of the present invention and are not intended to limit the present invention in any form. Although the present invention has been disclosed as above with preferred embodiments, they are not intended to limit the present invention. Therefore, any simple modification, equivalent changes and modifications made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention shall fall within the scope of protection of the technical solution of the present invention.
Claims
1. A method for optimizing the health effects of patients using rehabilitation equipment, characterized in that: The optimization method comprises: Step S1, collecting the patient's rehabilitation data Collect data on patients' rehabilitation walking movements, including acceleration data, heading angle data, geomagnetic data, and curvature; Step S2: Error correction of data Determine the acceleration data, heading angle data and geomagnetic data corresponding to the non-zero speed state according to the zero speed state, obtain the current speed according to the data of the non-zero speed state, and correct the quaternion and heading angle data; process the acceleration data with the corrected quaternion to obtain the acceleration data under the non-zero speed state, and calculate the corresponding displacement; Step S3, optimizing the patient's rehabilitation The next rehabilitation exercise is planned based on the comparison of the patient's current rehabilitation exercise data with the normal person's exercise data or the expected transportation data.
2. The method for optimizing the health effect of patients using rehabilitation equipment according to claim 1, characterized in that: In step S3, the method for confirming the zero speed state is: (1) When the patient is in a slow walking mode, the acceleration modulus corresponding to the acceleration data and the heading angle modulus corresponding to the heading angle data are calculated; whether the acceleration modulus is within a first threshold range and whether the heading angle modulus is within a second threshold range is determined; when the acceleration modulus is within the first threshold range and the heading angle modulus is within the second threshold range, the patient is currently in a zero speed state; (2) calculating the heading angle variance corresponding to the heading angle data and the acceleration variance corresponding to the acceleration data; determining whether the acceleration variance is within a third threshold range and whether the heading angle variance is within a fourth threshold range; when the acceleration variance is within the third threshold range and the heading angle variance is within the fourth threshold range, the patient is in a zero speed state; (3) In a gait cycle, the bending degree of the knee joint reaches a minimum when the patient's heel touches the ground, and reaches a minimum when the toes leave the ground, and then the bending degree gradually increases; the state set between two adjacent minimum values is the stage of foot landing, this interval is the zero speed interval, and the foot speed is zero.
3. The method for optimizing the health effect of patients using rehabilitation equipment according to claim 2, characterized in that: In the step S2, the gravity vector and the geomagnetic vector in the geographic coordinate system are obtained, and converted into the carrier coordinate system to obtain a first standard vector corresponding to the gravity vector, and a second standard vector corresponding to the geomagnetic vector; the first standard vector is compared with the gravity acceleration measured in the carrier coordinate system to obtain a first error vector, and the second standard vector is compared with the geomagnetic vector measured in the carrier coordinate system to obtain a first error vector; the quaternion and the heading angle data are corrected by the first error vector and the second error vector; the collected acceleration data is processed by the corrected quaternion to obtain the acceleration data in the non-zero speed state in the geographic coordinate system.
4. The method for optimizing the health effect of patients using rehabilitation equipment according to claim 3, characterized in that: In step S2, the position of the patient is estimated to calculate the corresponding displacement, and the patient's velocity can be expressed in the geographic coordinate system as: In the formula, v n represents the patient's speed, is the Columbia acceleration, represents the centripetal acceleration, g n Represents the local gravity acceleration value, and C represents the rotation matrix; When calculating the displacement of the patient's rehabilitation exercise, it is necessary to compensate for the acceleration error. The patient's position is expressed as: in, The patient's current location, is the patient's position in the previous sampling cycle, is the patient's current speed, is the patient's speed in the previous sampling period, and Δt is the sampling period.