CN114831630A - Hemiplegia patient rehabilitation data acquisition and evaluation method based on wearable intelligent device - Google Patents
Hemiplegia patient rehabilitation data acquisition and evaluation method based on wearable intelligent device Download PDFInfo
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Abstract
The invention relates to the technical field of recovery data acquisition, and provides a method for acquiring and evaluating recovery data of a hemiplegic patient based on wearable intelligent equipment, which comprises the following steps: step 100, respectively installing seven sensor nodes at the positions below the center of the waist, the middle of the thigh, the middle of the shank and the ankle of the back side of a human body; 200, acquiring human motion signals in real time during walking by using the sensor nodes; and 300, resolving gait space-time motion parameters of the collected human motion signals. The invention acquires the human body movement signal of the patient based on the wearable intelligent device, further obtains the rehabilitation data of the hemiplegic patient, and provides data support for the evaluation of the rehabilitation data of the hemiplegic patient.
Description
Technical Field
The invention relates to the technical field of rehabilitation data acquisition, in particular to a method for acquiring and evaluating rehabilitation data of a hemiplegic patient based on wearable intelligent equipment.
Background
Apoplexy, also known as stroke, is a common clinical cerebrovascular disease with high mortality and disability rate. In recent years, the incidence of stroke has increased year by year and the population is younger, which is a huge social burden. Hemiplegia is one of the most common injuries after stroke, and can cause dyskinesia of walking function of patients in different degrees, and research shows that 47-76% of patients after stroke can achieve partial or completely independent degree of self-care ability after receiving systematic and reasonable rehabilitation treatment. Therefore, it is one of the important contents of the rehabilitation therapy for stroke to perform the training with a specific purpose for the recovery of the walking function of the patient.
The hemiplegia rehabilitation data evaluation comprises qualitative analysis, semi-quantitative evaluation and quantitative evaluation, at the present stage, the qualitative analysis and the semi-quantitative evaluation are still the most common gait evaluation methods in clinic, the qualitative analysis mainly judges normal gait and abnormal gait through observation of doctors, the semi-quantitative evaluation mainly depends on observation of rehabilitation doctors and scores the executive ability, the motor ability, the balance ability and the like of patients by combining some functional test scales, and the judgment is made on the gait abnormal condition of the patients according to the scoring standard. The quantitative evaluation is mainly carried out by acquiring a kinematic signal or a dynamic signal of a patient through instrument equipment and acquiring gait parameters. The quantitative evaluation can realize the quantitative monitoring and the accurate intervention of the walking mode of the hemiplegic patient, provide scientific basis and technical support, so as to disclose key links and deep factors of rehabilitation therapy to assist clinical decision, and have important social significance and economic significance.
At present, common qualitative analysis and semi-quantitative evaluation in the hemiplegia rehabilitation evaluation process have certain subjectivity, the requirement on inspectors is high, the experience of doctors is excessively depended, and the intrinsic gait evaluation characteristics of patients are not evaluated. The quantitative analysis of the hemiplegia rehabilitation data comprises gait detection and gait parameter assessment, and common gait detection technologies can be divided into four categories, namely gait detection based on videos and images, gait detection based on pressure signals, gait detection based on electromyographic signals and gait detection based on inertial sensors. Gait detection based on videos and images has high requirements on laboratory environment, high cost of experimental equipment, high possibility of interference of collected signals by external environment, huge data of video information and extremely high requirements on the performance of a computer. Gait detection based on pressure signals can only acquire data when feet are in contact with the ground, and complete gait information cannot be acquired. The gait detection based on the electromyographic signals is weak and easy to be interfered. Quantitative standards in aspects of gait symmetry, variability, stability and the like are usually adopted for gait assessment of hemiplegic patients, but at present, accurate and consistent quantitative standards are not formed in research.
Disclosure of Invention
The invention mainly solves the technical problems that the recovery data of the hemiplegic patient in the prior art is difficult to acquire and an effective evaluation basis for the recovery data of the hemiplegic patient is lacked, and provides a recovery data acquisition and evaluation method for the hemiplegic patient based on wearable intelligent equipment.
The invention provides a hemiplegic patient rehabilitation data acquisition method based on wearable intelligent equipment, which comprises the following processes:
200, acquiring human motion signals in real time during walking by using the sensor nodes;
step 301, carrying out gait event detection and gait time phase division on the collected human motion signals; step 301 includes steps 3011 to 3013:
3011, dividing the human motion signal into a plurality of sub-periods by using a sliding window, detecting an angular velocity peak in each sub-period, and setting a one-step period between two adjacent peaks;
step 3012, after the peak point detection is finished, further dividing the gait cycle into a support phase and a swing phase according to the angular velocity signal measured by the sensor;
3013, after detecting the key points of gait, dividing the corresponding gait time phases;
step 302, generating a sensor coordinate system and a geographic coordinate system rotation matrix by using quaternions, and determining initial posture information of the human body; fusing acceleration and angular velocity data by adopting a data fusion method of a Kalman filter, and updating the attitude by utilizing a quaternion method for updating the attitude; integrating acceleration signals acquired by a sensor by using a strapdown inertial navigation algorithm to acquire speed information, and then integrating to acquire displacement information; after the posture and position parameters of the foot in the walking process are determined, the space parameters can be calculated according to the time phase parameters;
step 303, respectively installing movement data acquired by seven sensor nodes at positions below the center of the waist, the middle of the thigh, the middle of the calf and the ankle at the rear side of the human body, solving the relationship between a body coordinate system and a geographic coordinate system under an initial condition by using quaternion, and representing the posture of the lower limb movement of the human body under the geographic coordinate system by using the relationship; optimizing the solved quaternion by adopting a gradient descent data fusion algorithm, and eliminating an attitude error; the knee joint and ankle joint angles can be further calculated according to the obtained limb vector positions and the relative rotation angles of the adjacent limbs; in order to observe the dynamic change curve of the joint, the bending change of the joint in the sagittal plane is represented by a pitch angle, and the quaternion is converted into the pitch angle in the sagittal plane.
Furthermore, the sensor node is an inertial sensor, and the inertial sensor comprises a three-axis micro-accelerometer, a three-axis micro-gyroscope and a three-axis micro-magnetometer.
Further, in step 3013, after detecting the gait key points, the corresponding gait phase is divided, which includes the following processes:
by the formula T Cycle (K)＝T HS (K+1)-T HS (K) Acquiring a gait cycle, namely taking a heel strike as a starting point and taking the next heel strike as a terminal point;
by the formula T HS (K) Ff (k) -hs (k) obtains the heel strike phase, i.e. the time interval from heel strike to complete standstill;
by the formula T FF (K) Ho (k) -ff (k) obtaining a time interval when the human body is completely still, i.e. a time interval when the foot is completely contacted with the ground and the human body is ready to be lifted off the ground;
by the formula T HO (K) To (k) -ho (k) to obtain heel-off phase,i.e. the time interval from heel lift to toe lift;
by the formula T SW (K) HS (K +1) -to (K) obtains the swing phase, i.e. the time interval from the foot to the heel strike, with completely no contact with the ground;
by the formula
Obtaining step frequency, namely the number of steps taken by a human body per minute;
HS (K), FF (K), HO (K), TO (K) respectively represent the time points corresponding to the heel strike, the whole foot flat placement, the heel lift and the toe lift.
Correspondingly, the present invention further provides a method for evaluating rehabilitation data of a hemiplegic patient based on the method for acquiring rehabilitation data of a hemiplegic patient based on a wearable smart device according to any embodiment of the present invention, which further includes the following steps after the method for acquiring the rehabilitation data of the hemiplegic patient based on the wearable smart device:
step 401, quantifying gait characteristics of the hemiplegic patient from the aspect of symmetry, and according to the obtained gait space-time parameters, obtaining a gait space-time parameter by a formula:
obtaining the symmetrical angle of human gait;
wherein, Y 1 、Y 2 Respectively representing gait parameters of lower limbs on two sides of a human body;
step 402, quantifying gait characteristics of the hemiplegic patient from the aspect of variability, and according to the obtained gait space-time parameters, obtaining a formula:
obtaining the variation coefficient of human gait;
wherein SD represents the standard deviation of the gait parameters, mean represents the mean value of the gait parameters;
step 403, quantifying gait characteristics of the hemiplegic patient from the aspect of stability, and according to motion data collected by sensor nodes arranged at the center of the waist, the middle part of the thigh, the middle part of the calf and the position below the ankle at the rear side of the human body, obtaining the gait characteristics through a formula:
acquiring a human gait stability index; according to the gait space-time parameters obtained by the method, the following formula is adopted:
quantifying lower limb joint motion;
wherein, V T，RMS Root mean square value, V, representing a time series of accelerations in the vertical direction of the waist F，RMS Root mean square value representing time series of acceleration of foot in vertical direction, A m (r) represents the probability of matching m +1 points in both sequences;
and 500, evaluating the rehabilitation process of the hemiplegic patient according to the gait space-time characteristic evaluation.
Further, step 500, evaluating the rehabilitation process of the hemiplegic patient according to the gait space-time characteristic evaluation, comprising the following processes:
evaluated in terms of symmetry, according to the above symmetry angles:
taking SA as an index for evaluating the gait symmetry of the left side and the right side of the lower limb of the patient, wherein when SA is 0, the gait symmetry of the patient is better;
evaluating from the aspect of variability according to the above coefficient of variation
CV is used as an index for evaluating the angle fluctuation law of the left knee joint and the right knee joint of the lower limb of the patient, and the smaller the coefficient of variation value is, the smaller the gait variability of the patient is;
evaluation from stability aspect, according to the above gait stability index:
GSI is used as index for evaluating waist-foot stability of patients when GSI>1, the lumbar acceleration variability is large relative to the foot acceleration variability; when GSI<1, the change of the waist acceleration is small relative to the change of the foot acceleration; according to the indexes:
the sample entropies of the knee joint and the ankle joint are used as the stability index of the evaluation patient, the smaller the sample entropies are, the higher the gait regularity is, and the better the rehabilitation effect is.
Further, before step 500, the method further includes: visual analysis was performed from the aspects of symmetry, variability and stability:
and (3) symmetry visualization analysis: selecting time phase parameters in a time domain, and comparing the percentages of the four time phases in a gait cycle; in a spatial domain, a step length, a step height and human knee joint angle spatial parameters are combined to draw a gait spatial displacement curve, so that the spatial symmetry of human motion is quantized;
and (3) performing variability visualization analysis: drawing a histogram by combining the duration of each step of the human and the duration of each time phase, and further quantifying the spatial variability of the human motion;
and (3) visual analysis of stability: and drawing a leg angular velocity phase diagram so as to further quantify the space stability of the human motion.
The invention provides a method for acquiring and evaluating rehabilitation data of a hemiplegic patient based on wearable intelligent equipment. The wearable intelligent equipment is used for collecting multisource motion signals of the lower limbs of the human body, rich gait parameters are extracted through a data fusion algorithm, individual differences of patients are avoided, errors are reduced, resolving accuracy is guaranteed, and comprehensive quantitative assessment is conducted on the gait of the hemiplegic patients in a targeted mode by combining symmetry, variability and stability of the gait. The rehabilitation data evaluation is an objective quantitative index for evaluating the rehabilitation effect of the hemiplegic patient, avoids deviation caused by artificial subjective factors, and provides scientific basis for the function and mechanism of rehabilitation therapy. The key links and deep factors of rehabilitation treatment are disclosed to assist clinical decision making, and the method has important social and economic meanings.
Drawings
Fig. 1 is a flow chart of an implementation of the method for acquiring rehabilitation data of a hemiplegic patient based on a wearable intelligent device according to the present invention;
fig. 2 is a schematic diagram of an installation position of a sensor node in the method for acquiring rehabilitation data of a hemiplegic patient based on the wearable intelligent device according to the present invention;
fig. 3 is a development kit used in the method for acquiring the rehabilitation data of the hemiplegic patient based on the wearable intelligent device provided by the invention;
fig. 4 is a schematic diagram of a coordinate system in the method for acquiring the rehabilitation data of the hemiplegic patient based on the wearable intelligent device;
fig. 5 is a schematic structural diagram of a strapdown inertial navigation algorithm in the method for acquiring rehabilitation data of a hemiplegic patient based on the wearable intelligent device according to the present invention;
fig. 6 is a schematic diagram of a position estimation algorithm implemented based on an IMU in the method for acquiring rehabilitation data of a hemiplegic patient based on a wearable intelligent device according to the present invention;
fig. 7 is a schematic view of a lower limb movement track in a complete gait cycle of the hemiplegia patient recovery data acquisition method based on the wearable intelligent device provided by the invention;
fig. 8 is a flow chart of the implementation of the hemiplegia patient rehabilitation data evaluation method based on the wearable intelligent device provided by the invention.
Detailed Description
In order to make the technical problems solved, technical solutions adopted and technical effects achieved by the present invention clearer, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention. It should be further noted that, for the convenience of description, only some but not all of the relevant aspects of the present invention are shown in the drawings.
As shown in fig. 1, the method for acquiring the rehabilitation data of the hemiplegic patient based on the wearable intelligent device provided by the embodiment of the invention comprises the following steps:
and step 100, respectively installing seven sensor nodes at the positions of the center of the waist, the middle of the thigh, the middle of the shank and the lower part of the ankle at the back side of the human body.
The sensor arrangement of this step can be referred to fig. 2. The sensor node is an inertial sensor which comprises a three-axis micro-accelerometer, a three-axis micro-gyroscope and a three-axis micro-magnetometer, wherein the micro-accelerometer and the micro-gyroscope meet the requirements of light weight, small size, low power consumption and low cost.
And 200, acquiring human motion signals in real time during walking by using the sensor nodes.
The human motion signals comprise acceleration signals and angular speed signals of lower limbs in a three-dimensional space when the human walks. Specifically, referring to fig. 3, data is stored in the integrated SD card by using the shim 3C onsenssys EM G development kit of shim company, ireland, and setting the sampling frequency to 400Hz by the host computer software consenssys matched with the development kit.
And 300, resolving gait space-time motion parameters of the collected human motion signals. Step 300 includes steps 301 to 303.
Step 301, performing gait event detection and gait time phase division on the collected human body motion signals. Step 301 includes steps 3011 to 3013.
Step 3011, divide the human motion signal into a plurality of sub-periods by using a sliding window, and detect an angular velocity peak in each sub-period, where a one-step period is located between two adjacent peaks.
The method comprises the steps of detecting the angular velocity peak value of a pitch axis in a human gait cycle by using a peak detection algorithm, determining a gait cycle, dividing according to the angular velocity characteristics in gait phase, and further extracting related gait time parameters.
Step 3012, after the peak point detection is finished, the gait cycle is further divided into a support phase and a swing phase according to the angular velocity signal measured by the sensor.
The specific process is as follows: the method comprises the steps that an inertial sensor is used for collecting angular speed data of a pitch axis in human gait motion for a period of time, the sampling frequency with the window size being 0.7 times is set for peak detection, after the peak detection is finished, according TO the fact that a first local trough in front of a local peak point is toe-off (TO), a first zero crossing point or a point closest TO the zero point behind the peak point is Heel Strike (HS), two gait key points of the HS and the TO are found, and then two time phases of a support phase and a swing phase are divided.
In step 3013, after detecting the gait key points, the corresponding gait phase is divided.
By the formula T Cycle (K)＝T HS (K+1)-T HS (K) Acquiring a gait cycle, namely taking a heel strike as a starting point and taking the next heel strike as a terminal point;
by the formula T HS (K) Ff (k) -hs (k) obtains the heel strike phase, i.e. the time interval from heel strike to complete standstill;
by the formula T FF (K) Ho (k) -ff (k) obtains the time interval when the human body is completely still, i.e. the time interval when the foot is completely contacted with the ground to get ready to leave the ground;
by the formula T HO (K) To (k) -ho (k) obtaining a heel-off phase, i.e., a time interval from heel-off to toe-off;
by the formula T SW (K) HS (K +1) -to (K) obtains the swing phase, i.e. the time interval from the foot to the heel strike, with completely no contact with the ground;
by the formula
The step frequency, i.e. the number of steps taken by the human body per minute, is obtained.
HS (K), FF (K), HO (K), TO (K) respectively represent the time points corresponding to the heel strike, the whole foot flat placement, the heel lift and the toe lift.
Step 302, generating a sensor coordinate system and a geographic coordinate system rotation matrix by using quaternions, and determining initial posture information of the human body; fusing acceleration and angular velocity data by adopting a data fusion method of a Kalman filter, and updating the attitude by utilizing a quaternion method for updating the attitude; integrating acceleration signals acquired by a sensor by using a strapdown inertial navigation algorithm to acquire speed information, and then integrating to acquire displacement information; after the posture and position parameters of the foot during walking are determined, the space parameters can be calculated according to the time phase parameters.
Wherein the spatial parameters include, but are not limited to, stride length, step height, toe, and pace.
The specific process is as follows: before the foot attitude parameter is resolved, a coordinate system needs to be defined, and three right-hand rectangular coordinate systems including a geographic coordinate system (GCS, G for short), a sensor coordinate system (SCS, S for short), and a body coordinate system (BCS, B for short) are referred to fig. 4. In the gait analysis process, the calculation of the gait space parameters such as the foot angle, the step length, the step height and the like is mainly based on the posture information and the displacement information of the foot. Acquiring attitude information, namely estimating the attitude of an initial state and updating the attitude of a motion state, and solving a corresponding rotation matrix by determining the deviation between two coordinate systems when a person is static so as to acquire the initial attitude information of the human body; fusing acceleration and angular velocity data by adopting a data fusion method of a Kalman filter, and updating the attitude by utilizing a quaternion method for updating the attitude; referring to fig. 5, the acceleration signal obtained by the sensor is integrated by using the strapdown inertial navigation algorithm to obtain a velocity, and then integrated to obtain a displacement; after the posture and position parameters of the foot in the walking process are determined, spatial parameters such as step length, step height, foot angle and pace can be calculated according to the time phase parameters. The gait parameters in the rehabilitation evaluation of the hemiplegic patient mainly comprise step length, step height, plantar flexion angle, step speed and the like. Because the limb movement function and the coordination and balance capacity of the cerebral palsy patient are limited, the cerebral palsy patient is difficult to walk like a normal person, and the spatial gait parameters of the cerebral palsy patient have variation in different degrees.
Step-span length: also known as unilateral stride length or stride, refers to the projected length in the horizontal plane of the displacement from the first heel strike to the next ipsilateral heel strike during a complete gait cycle. The step length of a healthy person is 1.0-1.6 m, which is in a normal range, and compared with a healthy person, the step length of a single step of a hemiplegic patient should be shorter.
Step-up: the step height represents the maximum height of the foot from the ground during the foot contour cleaning exercise, and it is presumed that the step height of the affected side of the hemiplegic patient is smaller than that of the healthy side.
Angle of plantar flexion: the plantar flexion and dorsiflexion of the ankle joint correspond to the included angles between the foot and the horizontal ground in the processes of standing on the tiptoe and hooking the tiptoe respectively, the normal range of the plantar flexion angle is generally 40-50 degrees, the normal range of the dorsiflexion angle is 20-30 degrees, and the plantar flexion angle of the affected side of the hemiplegic patient is presumed to be lower than the normal value.
The pace speed is as follows: the walking ability of the patient is reflected, and important parameters closely related to the falling risk are reflected, the walking speed of a sound person is about 60-95 m/min, and the walking speed of a hemiplegic patient is lower than a normal value due to dyskinesia.
The invention researches the gait motion characteristics of the hemiplegic patient and the gait motion characteristics of a sound person, and utilizes the foot gait space parameters solved by the motion signals when the human body walks as the experimental basis for evaluating the rehabilitation level of the hemiplegic patient. Seven sensor nodes are arranged in the center of the waist, the middle of the thigh, the middle of the shank and the lower position of the ankle at the rear side of the human body, acceleration signals and angular velocity signals of a patient and a healthy person in a plurality of continuous step periods are collected, and then the foot gait space parameters are further calculated.
The invention provides a Strapdown Inertial Navigation System (SINS) -based method for solving attitude information, speed information and displacement information, and further calculating foot gait parameters to analyze the difference of foot motion parameters between a healthy person and a hemiplegic patient, and can obtain the following conclusion: hemiplegic patients have lower stride length, step height, plantar flexion, dorsiflexion angle and step speed than healthy people.
Step 303, respectively installing movement data acquired by seven sensor nodes at positions below the center of the waist, the middle of the thigh, the middle of the calf and the ankle at the rear side of the human body, solving the relationship between a body coordinate system and a geographic coordinate system under an initial condition by using quaternion, and representing the posture of the lower limb movement of the human body under the geographic coordinate system by using the relationship; optimizing the solved quaternion by adopting a gradient descent data fusion algorithm, and eliminating an attitude error; the knee joint and ankle joint angles can be further calculated according to the obtained limb vector positions and the relative rotation angles of the adjacent limbs; in order to facilitate observation of a dynamic change curve of the joint, the joint buckling change of a sagittal plane is represented by a pitch angle, and a quaternion is converted into the pitch angle of the sagittal plane;
wherein, the lower limb joints of the human body mainly comprise hip joints, knee joints and ankle joints, and the three joints determine that the human body can maintain body balance in the process of walking, running or jumping. The walking process of a human body is accompanied by the flexion and extension of hip joints, knee joints and ankle joints, and the movement characteristics of the lower limbs of the human body can be reflected by describing the change of the angles of the joints. The gait of a hemiplegic patient often shows the external characteristics of the pronation and the hyperextension of the knee, however, if the abnormal condition of the patient needs to be objectively and quantitatively analyzed, the kinematics parameters containing the gait information need to be obtained. Because the motion range of the affected joint of the hemiplegic patient is limited and is usually represented by the reduction of the flexion angle, the abnormal conditions of the angles of the hip joint, the knee joint and the ankle joint can reflect the motion state of the lower limb of the hemiplegic patient, and provide a basis for the assessment of the recovery condition of the walking ability of the patient.
The specific process is as follows: the coordinate system needs to be defined as before the foot motion parameter is resolved, and the lower limb posture resolution also relates to three right-hand rectangular coordinate systems of a geographic coordinate system (GCS, G system for short), a sensor coordinate system (SCS, S system for short) and a body coordinate system (BCS, B system for short); in order to effectively avoid the problem of nonuniform direction of a shaft system caused by different fixed positions of the measuring sensors each time, an experimenter is enabled to stand still in a north direction initially; the relation between the B system and the G system under the initial condition can be obtained through the accelerometer and magnetometer data,
wherein, gamma, theta,
Respectively indicate initial restThe pitch angle, the roll angle and the yaw angle of the carving,
measurements representing three axes of the accelerometer;
representing measurements of three axes of a magnetometer, the superscript S indicating that the measurements are defined in a sensor coordinate system, which is converted into a quaternion
Wherein, quaternion
Representing the relationship between the sensor coordinate system and the body coordinate system at the initial stationary time, since the body coordinate system coincides with the geographic coordinate system in north direction, it can be considered that
Because the sensor is fixed on the lower limb of the human body in the whole experiment process, the mapping relation between the sensor coordinate system and the body coordinate system can be assumed to be unchanged, namely
Thus, the pose of the lower limbs of the human body in the geographic coordinate system during exercise can be expressed as:
optimizing the solved quaternion by using a gradient descent data fusion algorithm to eliminate attitude errors, and referring to fig. 6; sequentially estimating the position of each limb vector by taking the waist node as a root node,
wherein the content of the first and second substances,
indicating the position of the limb at a time t-0, each timeThe length and the coordinates of each limb vector are preset manually;
representing the position of the limb in the geographical coordinate system at the next moment;
the displacement of limbs in a body coordinate system is expressed by a formula due to the fact that the limbs of the human body are mutually linked
The relative rotation of two adjacent limbs is obtained and converted into a rotation matrix by the formula γ' ═ arctan (c) 32 /c 33 ) A quaternion
Converted into a sagittal elevation angle γ'; to facilitate the observation of the dynamic curve of the joint, referring to FIG. 7, the change in joint flexion in the sagittal plane is represented by the pitch angle, and the quaternion is converted to the pitch angle in the sagittal plane.
The method for acquiring the rehabilitation data of the hemiplegic patient based on the wearable intelligent device can acquire complete gait information by using the inertial sensor, does not depend on a test environment, and has the advantages of low cost, small volume, portability and the like. In the foot motion analysis part, two data fusion methods of Kalman filtering and complementary filtering are compared, the results of the two data fusion algorithms adopted for the data of healthy people are basically consistent, but for hemiplegia patients, obvious individual differences exist due to different biased degrees of the patients, and finally, a Kalman filtering method is selected for resolving and updating the spatial attitude of the foot, so that errors are reduced, and the gait parameter accuracy is improved. In the lower limb motion analysis part, a gradient descent algorithm is adopted to solve the motion attitude of the lower limb, and the attitude solved by the quaternion differential equation of the gyroscope and the attitude quaternion solved by the gradient descent method are fused, so that the accuracy of the solved parameters is higher, a theoretical basis is provided for extracting the joint parameters of the lower limb, the calculation amount of the algorithm is small, and the parameter adjustment process is simple and convenient.
Example two
The invention also provides a hemiplegia patient rehabilitation data evaluation method based on the hemiplegia patient rehabilitation data acquisition method of the wearable intelligent device.
As shown in fig. 8, after the method for acquiring the rehabilitation data of the hemiplegic patient based on the wearable intelligent device according to any embodiment of the present invention, the following process is further included:
and 400, extracting corresponding gait evaluation indexes from the extracted gait space-time parameters containing the gait information, and quantizing the gait characteristics of the hemiplegic patients in the aspects of symmetry, variability and stability.
On the basis of solving the gait space-time parameters, corresponding gait evaluation indexes need to be further extracted from the gait space-time parameters containing the gait information, and the quantification of the gait characteristics can be realized. Gait evaluation indexes are different, and if evaluation indexes are extracted from a certain angle or only one of the indexes is adopted to carry out quantitative evaluation on gait, comprehensive evaluation information cannot be obtained. Step 400 includes steps 401 to 403:
step 401, quantifying gait characteristics of the hemiplegic patient from the aspect of symmetry, and according to the obtained gait space-time parameters, obtaining a gait space-time parameter by a formula:
obtaining a symmetrical angle of human gait, wherein Y 1 、Y 2 Respectively representing the gait parameters of the lower limbs on both sides of the human body.
In the present invention, Y is for the patient 1 Representing the parameters of the lower limbs on the hemiplegic side, Y 2 Representing the non-hemiplegic lower limb parameters.
The specific process is as follows: the gait symmetry is particularly sensitive to the difference between a healthy individual and a gait disorder individual, the gait symmetry situation reveals the health condition and pathological features of a human body to a certain extent, and for the rehabilitation training of the hemiplegic patient, the gait is abnormal due to the dyskinesia of the patient caused by stroke, and the gait symmetry is damaged, so that the gait symmetry can effectively quantify the rehabilitation training evaluation of the hemiplegic patient. The invention adopts a discrete gait parameter quantitative analysis method to analyze the symmetry of the lower limbs according to two evaluation indexes of a symmetry index and a symmetry angle. In addition, in order to make up for the deficiency of the discrete indexes, the comprehensive analysis is carried out by combining the left-right time phase symmetry, the stride length and the periodicity of the stride height.
Step 402, quantifying the gait characteristics of the hemiplegic patient in the aspect of variability, and according to the obtained gait space-time parameters, obtaining the gait characteristics of the hemiplegic patient by a formula:
obtaining the variation coefficient of human gait.
Wherein SD represents the standard deviation of the gait parameter, mean represents the mean of the gait parameter.
The specific process is as follows: the human motion process can be regarded as a dynamic system, and the coordinated motion of all limbs is needed to realize the control of the human posture. Therefore, the variation degree of the limb coordination condition of the patient relative to normal people can be more accurately reflected by describing the fluctuation degree and variability of the gait space-time parameters from step to step, and the basis can be provided for clinical diagnosis of diseases by quantifying and analyzing the gait difference caused by pathological reasons. The gait variability is evaluated by two quantitative analysis methods, namely a variation coefficient method and a visual analysis method.
Step 403, quantifying gait characteristics of the hemiplegic patient from the aspect of stability, and according to motion data collected by sensor nodes arranged at the center of the waist, the middle part of the thigh, the middle part of the calf and the position below the ankle at the rear side of the human body, obtaining the gait characteristics through a formula:
acquiring a human gait stability index; according to the gait space-time parameters obtained by the method, the following formula is adopted:
quantifying the joint movement of the lower limbs.
Wherein, V T，RMS Root mean square representing time series of acceleration in vertical direction of waistValue V F，RMS Root mean square value representing time series of acceleration of foot in vertical direction, A m (r) represents the probability of matching m +1 points in both sequences.
The specific process is as follows: the gait stability is the ability of keeping stable walking state and avoiding falling under various interference conditions in the walking process of people, and has important clinical significance in evaluating the falling risk of patients, particularly in the rehabilitation evaluation of patients with nervous system diseases. At present, the commonly used parameters for quantitatively evaluating the gait stability of the human body are a mass center, a pressure center and the like, and the precision and the reliability are higher. However, the data collection depends on a set of complete three-dimensional gait analysis equipment, and is difficult to popularize in clinical environment at present due to the defects of high cost, limited experimental site, requirement of professional operation and the like. To compensate for these deficiencies, this document uses high cost performance, easy to operate wearable inertial sensors to acquire acceleration, angular velocity signals, joint flexion to assess gait stability. The section analyzes the original inertial data by adopting the stability index, and further analyzes the lower limb resolving parameters by adopting the sample entropy and the phase diagram.
The joint stability is evaluated by the sample entropy of the affected joint, the entropy is a method for describing the disorder degree of the system, and the sample entropy becomes one of the most common nonlinear methods for researching human motion because of no influence of time sequence length, good consistency and less calculation amount. Because the total lengths of the collected gait data are not consistent each time, the selected time sequence is subjected to linear interpolation for normalization processing, and sample points are selected to solve the sample entropy SampEn of the sequence Ankle 、SampEn Ankle . In the analysis process, in order to keep useful signals as much as possible, filtering processing is not performed on the knee joint and the ankle joint which are calculated.
And 500, evaluating the rehabilitation process of the hemiplegic patient according to the gait space-time characteristic evaluation.
When the rehabilitation process of the hemiplegic patient is evaluated, the treatment cycle of the patient is divided into three stages, each stage collects the motion signals of the patient for many times, the foot motion parameters and the lower limb motion parameters of the patient are obtained according to the steps, and then the hemiplegic treatment effect is evaluated according to the result:
evaluated in terms of symmetry, according to the above symmetry angles:
and SA is used as an index for evaluating the gait symmetry of the left side and the right side of the lower limb of the patient, and when SA is 0, the gait symmetry of the patient is better, namely the recovery effect of the patient is better.
Evaluating from the aspect of variability according to the above coefficient of variation
CV is used as an index for evaluating the angle fluctuation law of the left knee joint and the right knee joint of the lower limb of the patient, and the smaller the coefficient of variation value is, the smaller the gait variability of the patient is, namely, the better the recovery effect of the patient is.
Evaluation from stability aspect, according to the above gait stability index:
GSI is used as index for evaluating the lumbar-foot stability of patients when GSI>1, the change of the lumbar vertebra acceleration relative to the change of the foot acceleration is large, and the limb stability is poor; when GSI<1, the change of the waist acceleration is small relative to the change of the foot acceleration, and the limb stability is good. According to the above indexes
SampEn Ankle The stability of knee joints and ankle joints of patients is represented, the sample entropies of the knee joints and the ankle joints are used as stability indexes of the patients to be evaluated, the smaller the sample entropies are, the higher the gait regularity is, and the better the rehabilitation effect is. Specifically, SampEn Knee And SampEn Ankle Comparing with Brunnstrom and Hoffer as scale index, SampEn Knee And SampEn Ankle The smaller the value is, the stronger the activity of knee joint and ankle joint of the patient is, and the better the rehabilitation effect is.
The invention provides a self-adaptive peak detection algorithm for gait detection, and in view of the fact that the pitch angle speed of the foot has good periodic characteristics when a human body walks, the angular speed of the shaft is selected for peak detection. However, due to the interference of noise or shaking and the abnormal gait of the patient, the stability of the lower limbs is poor during walking, the shaking condition is aggravated, and the condition of initial local multi-peak false detection is easily caused. Therefore, in order to improve the detection precision, a pseudo peak needs to be discriminated during step counting, and a real peak is obtained to realize step counting. In order to achieve the purpose of improving the accuracy of peak detection, the size of the sliding window is set as the product of the sampling frequency and the gait cycle, and the motion process is divided into a plurality of sub-cycles.
In addition, aiming at the problems of improving the accuracy and reliability of parameters in the gait space-time parameter resolving process, the invention provides a corresponding solution to improve the quality of resolving parameters and further improve the hemiplegia degree of a patient and the accuracy of rehabilitation assessment, and particularly, the foot posture is resolved by using a data fusion algorithm of a Kalman filter, and the lower limb motion capture is performed by using a gradient descent data fusion algorithm.
Before step 500, the method may further include: visual analysis was performed from the aspects of symmetry, variability and stability.
And (3) symmetry visualization analysis: selecting time phase parameters in a time domain, and comparing the percentages of the four time phases in a gait cycle; in a spatial domain, a step length, a step height and human knee joint angle spatial parameters are combined to draw a gait spatial displacement curve, so that the spatial symmetry of human motion is quantized;
and (3) performing variability visualization analysis: drawing a histogram by combining the duration of each step of the human and the duration of each time phase, and further quantifying the spatial variability of the human motion;
and (3) visual analysis of stability: and drawing a leg angular velocity phase diagram so as to further quantify the space stability of the human motion.
The specific process is as follows: in the symmetry analysis visualization, time domain and space domain are mainly analyzed, time phase parameters FF, HO, SW and HS in the time domain are selected, the percentages of four time phases in a gait cycle are compared, and a pie chart is drawn, so that the symmetry of limbs can be conveniently and visually reflected through analysis. Parameters are subdivided into a flat gait parameter and a rotary gait parameter in the spatial domain analysis, and a spatial displacement curve of gait can be drawn by combining the spatial parameters such as step length, foot clearance, step height and the like, so that the spatial symmetry of human motion is quantized. Taking the knee joint angle as an example, selecting the knee joint angle of a healthy person as a comparison, carrying out normalization processing on a plurality of gait cycles, and drawing an error band diagram of the joint angle in a complete gait cycle.
In the visual analysis of variability, under the condition of non-interference straight line walking, the steps have repeatability and show a certain fluctuation rule, a histogram is drawn according to the duration of the FF, HO, SW and HS time phase parameters in the gait cycle of a patient, and the comparison is carried out by combining the duration of the time phase of a sound person, so that the gait variability of the patient is visually reflected.
In the visual analysis of the stability, a phase diagram is drawn according to the human shank angular velocity and the shank swing angle, and the analysis is carried out by combining the shank angular velocity. The gait events are arranged clockwise on the phase diagram to form a closed curve, reflecting that healthy gait is periodic. The motion characteristics of the left and right cruses of the healthy person and the hemiplegic patient are contrasted and analyzed, and then the gait stability of the patients is visually reflected.
The scheme provided by the present embodiment is illustrated below by way of example:
in order to verify the hemiplegia patient recovery data evaluation method provided by the invention, the hemiplegia patient recovery data evaluation method provided by the invention is applied to carry out a gait experiment on the hemiplegia patient. In the experiment, the influence of rehabilitation therapy on gait symmetry, variability and stability of a cerebral palsy patient is observed as an example, the motion signals of the hemiplegic patient in the treatment and rehabilitation training process are collected and calculated, and data support is provided for verifying and quantitatively evaluating the effect of the hemiplegic therapy. Through the experimental research, the effectiveness of the hemiplegia patient rehabilitation data evaluation method is verified, the real-time hemiplegia treatment effect feedback and quantitative evaluation method are verified, and meanwhile, a scientific basis is provided for the motion mechanism of hemiplegia treatment.
Establishing an experimental platform:
in the experiment, sensor nodes are bound at seven key limb positions including the center of the back waist, the middle of the thigh, the middle of the shank and the lower part of the ankle of a hemiplegic patient, and a Shimmer3 Consenssys EMG development kit of Shimmer company of Ireland is a sensor.
Design of experimental contents:
determining a treatment team: therapists and accompanying persons. The therapist was specifically responsible for assessing the Brunnstrom motor function recovery staging scale, the Hoffer walking function staging scale and instructing the patient to execute the training program, with two accompanying persons on either side of the patient to protect the patient's safety and maintain correct posture. During the treatment process, the team members work separately and cooperate to complete the treatment task together.
Selecting an experimental object: 18 volunteers, including 6 healthy persons and 12 hemiplegic patients, were recruited in this experiment. Among them, 6 healthy subjects were designated as normal reference groups and were numbered H1-H6, respectively, and the height and weight of the healthy subjects were 174.67. + -. 7.39(cm) and 71.67. + -. 14.57 (kg). 12 hemiplegic patients meet the following requirements of a Brunnstrom scale score of 3-5; rating on the Hoffer scale is 2-4; the subject can walk more than 10m indoors with or without the aid of auxiliary equipment; the subject can stand on his or her own with the aid of a crutch or an aid. The patients are numbered P1-P12, wherein the paralyzed side of the patients P1-P7 is the right side, and the paralyzed side of the patients P8-P12 is the left side. The hemiplegic patient group had a height of 169.25 + -5.77 (cm) and a weight of 73.25 + -8.75 (kg).
A training flow is formulated: with the assistance of experimenters, seven inertial sensor nodes are respectively worn on the waist and the lower limbs on the left side and the right side, and the node fixing positions are respectively the center of the waist, the middle of the thigh, the middle of the shank and the lower part of the ankle on the rear side. The experimental environment is an unobstructed open corridor with the length of about 10m, and the experimental subject is required to walk back and forth along a straight line at a comfortable pace, so that a 10m × 2 walking test is completed.
And (3) evaluating the rehabilitation effect: according to experimental data collected in the training process, gait space parameters of the collected data are resolved, and according to the gait space parameters and gait indexes, gait symmetry, variability and stability are evaluated quantitatively.
And (3) acquiring experimental data: the experiment selects an open corridor with the length of about 10m and without shelter, and the hemiplegic patient is required to walk back and forth along a straight line at a comfortable pace, so as to complete the walking test of 10m multiplied by 2. Each hemiplegia patient generally needs to be hospitalized for six weeks, the treatment period is divided into three stages in the experiment, and data acquisition is carried out every two weeks. The patient is required to complete a 10m multiplied by 2 walking test each time, and data of human motion signals are collected in real time, stored, analyzed and processed.
Experimental data processing and result analysis: the rehabilitation data evaluation of the hemiplegic patient needs to be carried out according to the flow of the traditional rehabilitation training. The data acquisition and the initial rehabilitation evaluation of the hemiplegic patient are carried out for the first time, the data acquisition and the rehabilitation evaluation at the last stage are completed when the whole treatment course is finished, and the data acquisition and the rehabilitation evaluation are carried out regularly at one time in the middle three treatment stages. In the regular rehabilitation evaluation of the three treatment stages of rehabilitation, in addition to the objective evaluation of the rehabilitation degree of a patient by adopting the quantitative evaluation method of symmetry, variability and stability, the rehabilitation evaluation table can be used for subjectively evaluating the motor function of the patient, and the subjective evaluation comprises a Brunnstrom motor function recovery stage scale and a Hoffer walking function grading scale, and the walking ability (the stepping frequency, the stepping speed and the walking function grading), the lower limb motor function, the balance function and the daily living activity ability of the patient are evaluated. After the whole treatment course is finished, the evaluator can compare the collected and processed evaluation data with the evaluation data of the two rehabilitation evaluation tables, and analyze the consistency and the correlation between the two evaluation tables. If the quantitative evaluation method has the correlation and is positively correlated, the accuracy and the objectivity of the quantitative evaluation method are verified from the aspect of clinical rehabilitation evaluation tables.
The invention provides a hemiplegia patient rehabilitation data evaluation method based on wearable intelligent equipment, and aims to solve the technical problem that an evaluation basis reflecting a rehabilitation effect is lacked in rehabilitation evaluation in the prior art; meanwhile, a portable method for feeding back the rehabilitation treatment effect is provided, and a scientific basis is provided for the function and mechanism of rehabilitation treatment. The key links and deep factors of rehabilitation treatment are disclosed to assist clinical decision making, and the method has important social and economic meanings.
According to the hemiplegia patient rehabilitation data evaluation method based on the wearable intelligent device, the motion information of the patient is acquired based on the inertial sensor, objective quantitative indexes for evaluating the rehabilitation effect of the hemiplegia patient are provided, data support is provided for evaluating the rehabilitation treatment effect, and deviation caused by artificial subjective factors is avoided; meanwhile, a portable method for feeding back the rehabilitation treatment effect is provided, the reliability for real-time feedback of the hemiplegia treatment effect evaluation is improved, and a scientific basis is provided for the function and mechanism of rehabilitation treatment.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: modifications of the technical solutions described in the embodiments or equivalent replacements of some or all technical features may be made without departing from the scope of the technical solutions of the embodiments of the present invention.
Claims (6)
1. A hemiplegia patient rehabilitation data acquisition method based on wearable intelligent equipment is characterized by comprising the following processes:
step 100, respectively installing seven sensor nodes at the positions below the center of the waist, the middle of the thigh, the middle of the shank and the ankle of the back side of a human body;
200, acquiring human motion signals in real time during walking by using the sensor nodes;
step 300, resolving gait space-time motion parameters of the collected human motion signals; step 300 includes steps 301 to 303:
step 301, carrying out gait event detection and gait time phase division on the collected human motion signals; step 301 includes steps 3011 to 3013:
3011, dividing the human motion signal into a plurality of sub-periods by using a sliding window, detecting an angular velocity peak in each sub-period, and setting a one-step period between two adjacent peaks;
step 3012, after the peak point detection is finished, further dividing the gait cycle into a support phase and a swing phase according to the angular velocity signal measured by the sensor;
3013, after detecting the key points of gait, dividing the corresponding gait time phases;
step 302, generating a sensor coordinate system and a geographic coordinate system rotation matrix by using quaternions, and determining initial posture information of the human body; fusing acceleration and angular velocity data by adopting a data fusion method of a Kalman filter, and updating the attitude by utilizing a quaternion method for updating the attitude; integrating acceleration signals acquired by a sensor by using a strapdown inertial navigation algorithm to acquire speed information, and then integrating to acquire displacement information; after the posture and position parameters of the foot in the walking process are determined, the space parameters can be calculated according to the time phase parameters;
step 303, respectively installing movement data acquired by seven sensor nodes at positions below the center of the waist, the middle of the thigh, the middle of the calf and the ankle at the rear side of the human body, solving the relationship between a body coordinate system and a geographic coordinate system under an initial condition by using quaternion, and representing the posture of the lower limb movement of the human body under the geographic coordinate system by using the relationship; optimizing the solved quaternion by adopting a gradient descent data fusion algorithm, and eliminating an attitude error; the knee joint and ankle joint angles can be further calculated according to the obtained limb vector positions and the relative rotation angles of the adjacent limbs; in order to observe the dynamic change curve of the joint, the bending change of the joint in the sagittal plane is represented by a pitch angle, and the quaternion is converted into the pitch angle in the sagittal plane.
2. The wearable intelligent device-based hemiplegic patient rehabilitation data acquisition method according to claim 1, wherein the sensor nodes are inertial sensors, and the inertial sensors comprise a three-axis micro-accelerometer, a three-axis micro-gyroscope and a three-axis micro-magnetometer.
3. The method for acquiring rehabilitation data of hemiplegic patients based on wearable intelligent equipment according to claim 1, wherein after detecting the gait key points, the corresponding gait phase is divided in step 3013, and the method comprises the following steps:
by the formula T Cycle (K)＝T HS (K+1)-T HS (K) Acquiring a gait cycle, namely taking a heel strike as a starting point and taking the next heel strike as a terminal point;
by the formula T HS (K) Ff (k) -hs (k) obtains the heel strike phase, i.e. the time interval from heel strike to complete standstill;
by the formula T FF (K) Ho (k) -ff (k) obtaining a time interval when the human body is completely still, i.e. a time interval when the foot is completely contacted with the ground and the human body is ready to be lifted off the ground;
by the formula T HO (K) To (k) -ho (k) obtaining a heel-off phase, i.e., a time interval from heel-off to toe-off;
by the formula T SW (K) HS (K +1) -to (K) obtains the swing phase, i.e. the time interval from the foot to the heel strike, with completely no contact with the ground;
by the formula
Obtaining step frequency, namely the number of steps taken by a human body per minute;
HS (K), FF (K), HO (K), TO (K) respectively represent the time points corresponding to the heel strike, the whole foot flat placement, the heel lift and the toe lift.
4. A hemiplegia patient recovery data evaluation method based on the hemiplegia patient recovery data collection method of the wearable smart device as claimed in any one of claims 1 to 3, further comprising the following processes after the hemiplegia patient recovery data collection method based on the wearable smart device:
step 400, extracting corresponding gait evaluation indexes from the extracted gait space-time parameters containing gait information, and quantizing the gait characteristics of the hemiplegic patients in the aspects of symmetry, variability and stability; step 400 includes steps 401 to 403:
step 401, quantifying gait characteristics of the hemiplegic patient from the aspect of symmetry, and according to the obtained gait space-time parameters, obtaining a gait space-time parameter by a formula:
obtaining the symmetrical angle of human gait;
wherein, Y 1 、Y 2 Respectively representing gait parameters of lower limbs on two sides of a human body;
step 402, quantifying gait characteristics of the hemiplegic patient from the aspect of variability, and according to the obtained gait space-time parameters, obtaining a formula:
obtaining the variation coefficient of human gait;
wherein SD represents the standard deviation of the gait parameters, mean represents the mean value of the gait parameters;
step 403, quantifying gait characteristics of the hemiplegic patient from the aspect of stability, and according to motion data collected by sensor nodes arranged at the center of the waist, the middle part of the thigh, the middle part of the calf and the position below the ankle at the rear side of the human body, obtaining the gait characteristics through a formula:
acquiring a human gait stability index; according to the gait space-time parameters obtained by the method, the following formula is adopted:
quantifying lower limb joint movement;
wherein, V T，RMS Waist displayRoot mean square value of time series of acceleration in vertical direction, V F，RMS Root mean square value representing time series of acceleration of foot in vertical direction, A m (r) represents the probability of matching m +1 points in both sequences;
and 500, evaluating the rehabilitation process of the hemiplegic patient according to the gait space-time characteristic evaluation.
5. The method for evaluating the rehabilitation data of the hemiplegic patient according to claim 4, wherein the step 500 of evaluating the rehabilitation progress of the hemiplegic patient according to the gait space-time characteristic evaluation comprises the following processes:
evaluated in terms of symmetry, according to the above symmetry angles:
taking SA as an index for evaluating the gait symmetry of the left side and the right side of the lower limb of the patient, wherein when SA is 0, the gait symmetry of the patient is better;
evaluating from the aspect of variability according to the above coefficient of variation
CV is used as an index for evaluating the angle fluctuation law of the left knee joint and the right knee joint of the lower limb of the patient, and the smaller the coefficient of variation value is, the smaller the gait variability of the patient is;
evaluation from stability aspect, according to the above gait stability index:
GSI is used as index for evaluating waist-foot stability of patients when GSI>1, the lumbar acceleration variability is large relative to the foot acceleration variability; when GSI<1, the change of the waist acceleration is small relative to the change of the foot acceleration; according to the indexes:
the sample entropies of the knee joint and the ankle joint are used as the stability index of the evaluation patient, the smaller the sample entropies are, the higher the gait regularity is,the better the rehabilitation effect.
6. The hemiplegia patient recovery data evaluation method of claim 5 further comprising, before step 500: visual analysis was performed from the aspects of symmetry, variability and stability:
and (3) symmetry visualization analysis: selecting time phase parameters in a time domain, and comparing the percentages of the four time phases in a gait cycle; in a spatial domain, a step length, a step height and human knee joint angle spatial parameters are combined to draw a gait spatial displacement curve, so that the spatial symmetry of human motion is quantized;
and (3) performing variability visualization analysis: drawing a histogram by combining the duration of each step of the human and the duration of each time phase, and further quantifying the spatial variability of the human motion;
and (3) visual analysis of stability: and drawing a leg angular velocity phase diagram so as to further quantify the space stability of the human motion.
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