CN115813372A - Stroke coordination stability motion function assessment device based on kinematic characteristics - Google Patents
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Abstract
The invention discloses a stroke coordination stability motor function evaluation device based on kinematic characteristics, which comprises the following steps: calculating the gait of the patient through a stability margin model to generate the gait stability margin characteristic of the patient; acquiring gait data of a patient through a three-dimensional motion capture module to obtain angle data information of each joint of the lower limb of the patient; calculating the collected angle data of each joint of the lower limb of the patient by a Hilbert transform method to obtain the corresponding joint phase angle of the patient; calculating the corresponding joint phase angle of the patient by the following formula to generate a patient gait curve CRP; extracting the stability characteristics of the gait stability margin of the patient according to the correlation of the functional scale and establishing a stroke motion evaluation model by using the coordination characteristics matched with the gait curve CRP of the patient; the method obtains kinematic parameters through three-dimensional motion capture, can describe the relationship between stability margin, a continuous relative time phase method and functional scale scoring, simultaneously measures the relationship between coordination stability and the degree of illness of a patient, and realizes motion evaluation on a stroke patient.
Description
The technical field is as follows:
the invention belongs to a disease prediction technology, and particularly relates to a stroke coordination stability encounter function evaluation device based on kinematic characteristics.
Background art:
walking is the most common functional movement in daily life, but is a challenge for stroke patients. Cerebral apoplexy patients have motor dysfunction, and 70-80% of patients usually show hemiplegia, which can directly affect their activities of daily life. The relative balance of the body needs to be maintained in the whole gait phase for realizing stable walking, and the posture control capability of a stroke patient is poor due to reasons of muscle strength reduction, joint contracture and the like, the balance and stability capability is weaker than that of a normal person, and the risk of walking and falling is increased during walking. The motor ability evaluation for the stroke patient is particularly important, and the medical staff can be helped to accurately find the problems existing in the lower limbs of the patient, so that a more detailed rehabilitation strategy is customized. Hospitals typically assess the functional activities of stroke patients by reporting past medical history and functional scales, such as the Fugl-Meyer sensorimotor recovery scale, the Berg balance scale, and the like. However, the methods are not fine enough in grading, depend on the subjective judgment of doctors and lack of digital refinement. A large number of researches prove that gait analysis has the advantages of accuracy and precision in the assessment of the exercise function, the motor ability of a patient can be objectively assessed through the kinematic characteristics, and the gait analysis is associated with the disease degree to judge the rehabilitation condition of the patient.
The balance control in the walking process is completed by continuously adjusting the position of the mass center of the body relative to the area surrounded by the feet, and when the body mass center falls in the supporting surface, the human body is considered to be in a stable state. To calculate the relative stability during the dynamic walking phase, hof et al introduced a stability margin in 2005, defined as the distance between the extrapolated centre of mass (XCoM) and the limit of the bearing surface (BoS) (fig. 1), with the formula:
MoS＝BoS-xCoM
usually, the MOS values of the coronal plane and the sagittal plane are calculated, and when the MOS value is positive, the extrapolated centroid is considered to be in the support plane, whereas when the MOS value is negative, the extrapolated centroid exceeds the support plane and is considered to be in an unstable state, wherein xCoM considers both the position and the velocity of the centroid, and the calculation formula is:
where CoM is the spatial relative position of the body center of mass, vCoM is the velocity of the body center of mass, obtained by differentiating the CoM displacement, g is the gravitational acceleration, 1 is the height of the pendulum, defined in the human body as the length of the leg (relative height from the thigh greater trochanter position to the ground).
The stability margin measures the balance ability of the human body from the relative position between the body mass center and the supporting surface, but stable gait needs to mutually coordinate with the participation of a plurality of elements such as a nervous system, a skeletal muscle system and the like to propel the body to advance. When self-interference such as taking a step is faced, the nervous system can realize the stabilization of the body mass center through activating muscles, adjusting the joint motion range and the like. In previous researches, kinematic data such as joint angles and angular velocities are often used for describing the motion of human limbs, but the kinematic data are often used for measuring one joint independently, and the coordination performance among the joints cannot be described.
Barela et al, 2000, suggested that during exercise, the lower extremity segments could be considered as a coupled system, with the segments interacting to effectively change the position of the body, with the stability of the rhythmic movement being largely affected by the average relative phase required. Rosen considers that the behavior of a dynamic system can be described by plotting variables against their first derivative-these plots are commonly referred to as phase plots and provide qualitative utility in analyzing body motion. The walking is used as continuous dynamic motion, and the phase angle difference of two original signals can be calculated by a continuous relative time phase method to represent the coordinated coupling between two joints of the lower limb. The phase calculation method based on Hilbert transform can generate an analysis signal from a non-sinusoidal signal so as to eliminate frequency artifacts, so that the method is suitable for researching coordination between limbs and in limbs in human motion, and the calculation method comprises the following steps:
ζ(t)＝x(t)+iH(t).
according to the method, a joint angle x (t) of a hip joint, a knee joint or an ankle joint, which changes with time t, is subjected to Hilbert transform to obtain an imaginary part H (t). Calculating to obtain joint phase angle through arc tangent function
CRP is calculated by subtracting the distal joint phase angle from the proximal joint phase angle:
when both joints are at the same point in their cycles (phases) at the same time, the average relative phase is 0 °, indicating the same phase, which is the most stable state. If both joints are at opposite points in their cycle at the same time, the average relative phase is 180 ° or anti-phase, but is also a stable state, although its stability is less than in-phase at 0 °.
The invention content is as follows:
the invention provides a motor function evaluation device for coordination stability of a cerebral apoplexy patient based on kinematic characteristics. The research obtains the kinematic information of the stroke patient in a stable walking stage through a three-dimensional motion capture system, calculates the coordination among lower limb joints through a Continuous Relative Phase (CRP) method, calculates the stability of a human body through a stability Margin (MoS), and evaluates the motion function of the stroke patient by establishing a kinematic model through scoring the calculated characteristics and a functional scale, so as to evaluate the motion function of the body coordination ability and the stability ability of the stroke patient. The device estimates the motion function of the lower limb of the patient by calculating the lower limb coordination and stability parameters of the stroke patient in a 8-meter walking experiment under a self-comfortable gait condition and constructing a model between the kinematic parameters and the functional scale, and is beneficial to realizing more precise estimation of the lower limb of the patient and establishing a more targeted rehabilitation treatment means.
The invention is implemented by adopting the following technical scheme:
a stroke coordination stability motor function evaluation device based on kinematic characteristics comprises the following steps:
calculating the gait of the patient through a stability margin model to generate the gait stability margin characteristic of the patient;
wherein: coM is the relative position in space of the body's center of mass, vCoM is the speed of the body's center of mass, g is the gravitational acceleration, l is the height of the pendulum (lower limb),
acquiring gait data of a patient through a three-dimensional motion capture module to obtain time-space parameters and angle data information of each joint of the lower limb;
calculating the collected angle data of each joint of the lower limb of the patient by a Hilbert transform method to obtain the corresponding joint phase angle of the patient;
calculating corresponding joint phase angles of the patient by the following formula to generate a patient gait curve CRP;
wherein:
the phase angle of the hip joint is shown as the hip joint phase angle,
obtaining Continuous Relative Phase (CRP) through the difference between adjacent joint phase angles for the knee joint phase angle, and then continuously calculating CRP values of the knee joint and the ankle joint.
Combining a Berg balance weight scale for measuring the balance capability of a stroke patient and a Fugl-Meyer motion function scoring scale for measuring the motion function, carrying out Pearson correlation analysis on the cooperative features matched with the gait stability margin features of the patient and the gait curve CRP of the patient, finding two parameters representing stability and coordination with the maximum correlation, establishing a stroke motion evaluation model by scoring the same scale, and analyzing the contribution degree of the stability and coordination and the motion balance capability of the stroke patient.
Y＝β 0 +β 1 X1+β 2 X2
Wherein: beta is a 0 Is a constant term, β 1 And beta 2 Are regression coefficients. The independent variable X1 is a coordination feature and X2 is a stability feature.
Further, the gait curve CRP is a curve calculated by calculating a deviation from a standard deviation of a whole CRP curve point of an average gait cycle, which is defined as a process of one side heel landing to the side heel landing again when walking; putting a force measuring table in the middle part of the walking path, dividing gait cycles according to plantar pressure data of the force measuring table, calibrating the time point of heel contact of the hemiplegic side of the patient, unifying the time length of parameters in one gait cycle by a time interpolation method, and obtaining the kinematics parameters of 3 gait cycles of each walking test.
Has the advantages that:
according to the method, the kinematics parameters related to stability and coordination in the 8-meter walking experiment of the stroke patient are calculated and evaluated, the correlation analysis is performed on the kinematics parameters related to the functional scale evaluation, the parameters with high correlation degree and used for representing the coordination and stability of the patient are obtained, and then the function evaluation model is constructed through multiple linear regression. Previous researches are only scored through a functional scale, and the motor functions of patients cannot be described in a precise and detailed mode, and the results depend on the subjective judgment of doctors. The invention obtains the kinematics parameters through three-dimensional motion capture, and has high accuracy and good evaluation effect. The parameters used to assess stability and coordination were more convincing from previous studies. The obtained kinematics model can describe the relationship between stability margin, a continuous relative time phase method and functional scale scoring, and simultaneously measure the relationship between the coordination stability and the ill degree of the patient, so that the assessment of the motor function of the stroke patient is realized.
Description of the drawings:
fig. 1 is a schematic structural diagram of a stroke coordination stability motor function assessment device based on kinematic characteristics according to the present invention;
fig. 2 is a flow chart of the stroke coordination stability motor function evaluation device based on kinematic characteristics according to the present invention;
figure 3 is a schematic view of the present invention relating to human foot features.
And (4) a flow chart.
Detailed Description
The invention provides a stroke coordination stability motion function evaluation device based on kinematic characteristics. The following describes the implementation of the present invention in further detail with reference to fig. 1.
As shown in fig. 1 to fig. 3, the method is based on the kinematics data of the stroke patient in the 8-meter walking experiment, the parameter stability margin describing the stability of the human body and the continuous relative phase of the parameters describing the coordination of the lower limbs of the human body are obtained through analysis and processing, the problems reflected in the lower limb movement process of the stroke patient are analyzed through constructing a kinematics model, the coordination and stability capability of the stroke patient in the stable gait stage is judged, and the movement function evaluation of the stroke patient is realized. The general technical flow is shown in figure 2.
The experimental steps are as follows:
1) 8 m walk test. The experimenter remained stationary standing, looking straight ahead. After hearing the start command, the vehicle was moved straight at a comfortable self-feeling speed for a distance of 8 m. Previous studies have indicated that healthy persons generally reach a steady gait by 2-3 steps, and a distance of 8 meters can ensure that the experimenter reaches a steady gait stage, similar to a 6-minute walk test commonly used in hospitals, and is convenient to apply to practical use later. The experiment is repeated for 10 times to collect lower limb kinematics data of the experimenter, and the interval of each experiment is 5 minutes, so that the patient is prevented from feeling tired.
2) Data acquisition
Based on VICON three-dimensional motion capture equipment, the sampling rate is 100Hz, the light reflection mark points are attached to the corresponding positions of the Lower limbs of an experimenter according to a Lower limb Plug-in-gait model, the light reflection mark points are additionally attached to the second phalanx of the big toe, the second phalanx of the big toe and the second phalanx of the small toe, and the light reflection mark points at the heel of the foot form a human body supporting surface together, as shown in figure 3. And obtaining the position information of the reflecting mark points through a three-dimensional motion capture system. The human body mass center is obtained by calculating the three-dimensional position of the center of a triangle formed by the surrounding of the reflective mark points on the anterior superior iliac spine and the posterior superior iliac spine, the speed of the center of mass is obtained by differentiating the position data of the human body mass center, and the length of the human body leg is defined as the vertical distance from the thigh trochanter to the ground.
And outputting the angle information of each joint of the Lower limb through a Lower limb Plug-in-gait model in VICON software. And obtaining each joint phase angle through Hilbert transform, and subtracting the distal joint phase angle from the proximal joint phase angle to obtain a CRP curve. During each trial, the CRP curves in all gait cycles were averaged and an ensemble-averaged curve was generated. Finally, to calculate the inter-joint coordination variability, the deviation was calculated by the standard deviation of the global CRP curve points of the average gait cycle.
The gait cycle is defined as the process from landing one heel to landing the heel again while walking. Putting a force measuring table in the middle of a walking path, obtaining plantar pressure data through the ground reaction force, dividing gait cycles and calculating a plantar pressure Center (COP) according to the plantar pressure data of the force measuring table, calibrating the time point of heel touchdown of a patient on the lateral paralyzed side, unifying the time length of parameters in each gait cycle through a weight-time interpolation method, and obtaining the kinematic parameters of 3 gait cycles of each walking test.
3) Parameter calculation and modeling
According to the displacement and speed data of the reflecting mark points of the feet of the patient, the spatiotemporal parameters such as the pace speed, the step length, the step width and other related information are calculated through matlab software. The plantar pressure center parameter is obtained through the local acting force calculation of the force measuring plate, and the difference between a patient and a healthy person in the walking task is found through the statistical analysis of independent sample T test and healthy person data.
Calculation of stability margin: the minimum value of the MoS data of the front side, the rear side, the inner side and the outer side and the value of the heel at the time of touchdown are calculated according to the formula, and because the stability of the time points is lower, the patient can fall down more easily.
CRP calculation: the mean and standard deviation of the overall CRP curves generated in all experiments were calculated as shown in the following formula to measure the differences in amplitude and variability of CRP from the dysmorphic cycle.
Wherein MARP measures the average amplitude of the CRP curve, DP measures the difference of CRP between different gait cycles, N is the number of gait cycles, and SD (t) is the standard deviation between different gait cycles.
The difference between the stability and coordination of the stroke patient and the healthy person is found by performing statistical analysis on the obtained MoS parameters and CRP parameters in different directions through independent sample T test and data of the healthy person, and the subsequent rehabilitation training is formulated and implemented according to the specific direction.
And (3) performing correlation analysis on the obtained stability margin characteristics of 4 directions and 2 CRP characteristics and functional scale scores to obtain a harmony characteristic X1 and a stability characteristic X2 which have the highest correlation with the patient scale score (Berg balance scale, fugl-Meyer movement function score scale), and constructing a multiple linear regression model by using the two characteristics and the functional scale score Y.
Y＝β 0 +β 1 X1+β 2 X2
Finally, the relation between the coordination and the stability of the stroke patient and the degree of illness is obtained, the internal relation between the stability and the coordination of the stroke patient and the motor function is explained, the index for measuring the balance ability of the patient is found, and the objective assessment of the motor function of the stroke patient is obtained.
The present invention is not limited to the above-described embodiments. The foregoing description of the specific embodiments is intended to describe and illustrate the technical solutions of the present invention, and the above specific embodiments are merely illustrative and not restrictive. Those skilled in the art can make various changes in form and details without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (2)
1. A stroke coordination stability motor function evaluation device based on kinematic characteristics is characterized by comprising the following steps:
calculating the gait of the patient through a stability margin model to generate the gait stability margin characteristic of the patient;
wherein: coM is the relative position in space of the center of mass of the body, vCoM is the velocity of the center of mass of the body, g is the acceleration of gravity, l is the height of the pendulum (lower limb),
acquiring gait data of a patient through a three-dimensional motion capture module to obtain space-time parameters and angle data information of each joint of the lower limb; calculating the angle data of each joint of the lower limb of the collected patient by a Hilbert transform method to obtain the corresponding joint phase angle of the patient; calculating the corresponding joint phase angle of the patient by the following formula to generate a patient gait curve CRP;
wherein:
the phase angle of the hip joint is shown as the hip joint phase angle,
obtaining Continuous Relative Phase (CRP) for the knee joint phase angle through the difference between adjacent joint phase angles, and subsequently continuously calculating CRP values of the knee joint and the ankle joint;
extracting coordination characteristics matched with patient gait curve CRP (common reflection point) from patient gait stability margin characteristics according to functional scale correlation to establish stroke assessment model
Y＝β 0 +β 1 X1+β 2 X2
Wherein: beta is a 0 Is a constant term, β 1 And beta 2 Is a regression coefficient; the independent variable X1 is a coordination feature and X2 is a stability feature.
2. The device for assessing coordinated stroke stability and motor function according to claim 1, wherein the gait curve CRP is calculated by the standard deviation of the points of the overall CRP curve of the average gait cycle defined as the process of landing one lateral heel to landing the lateral heel again when walking; putting a force measuring table in the middle part of the walking path, dividing gait cycles according to plantar pressure data of the force measuring table, calibrating the time point of heel contact of the hemiplegic side of the patient, unifying the time length of parameters in one gait cycle by a time interpolation method, and obtaining the kinematics parameters of 3 gait cycles of each walking test.
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