CN114141369B - Method and device for calculating spasm degree, electronic equipment and storage medium - Google Patents
Method and device for calculating spasm degree, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a method and a device for calculating a spasm degree, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring dynamic characteristics and electrophysiological characteristics of a patient; inputting the dynamic characteristics and the electrophysiological characteristics into a trained spasm degree calculation model to obtain a spasm degree calculation result; the trained spasm degree calculation model is obtained by training the dynamics characteristics, the electrophysiology characteristics and the corresponding spasm degree labels of different patients. The invention aims at the dynamic characteristics and the electrophysiological characteristics of the affected limb of the patient in the passive traction movement, and realizes the comprehensive objective analysis of the spasticity of the patient by independently analyzing the nerve components and the non-nerve components causing the spasticity symptoms through the spasticity degree calculation model.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a spasm degree calculation method and device, electronic equipment and a storage medium.
Background
Stroke is an acute cerebrovascular disease with continuously damaged brain tissue function and structure caused by blockage or rupture of cerebral vessels, most patients can generate spastic complications with different degrees due to upper motor injury, and the symptoms mainly include increased muscle tension, muscle stiffness, limb pain and the like, and the acute cerebrovascular disease has great influence on daily activity and self-care ability of the patients. The existing common spasm treatment methods for stroke patients mainly comprise drug treatment, surgical treatment, physical treatment and the like. The timely quantitative evaluation of the spasm degree of the patient has important significance for formulating a treatment scheme and evaluating the treatment effect, so the method is a basic link in the whole exercise rehabilitation training process.
At present, the spasm evaluation methods generally adopted clinically comprise an Ashworth spasm evaluation method, an improved Ashworth spasm evaluation method, a Tardieu scale, an improved Tardieu scale and the like. The traditional rehabilitation evaluation methods mainly take doctor observation as an auxiliary measure data, and are completed by combining with scale scoring, so that the obtained semi-quantitative evaluation result is easily influenced by the subjective experience of the doctor and has inevitable deviation. In addition, the above evaluation method does not take into account the speed-dependent characteristics of spasticity, i.e., the patient's exhibited spastic resistance increases with increasing stretch speed, and does not distinguish between neural and non-neural components of spastic resistance.
In summary, there is a need for a method for calculating a spasm degree, which is used to solve the above-mentioned problems of the prior art.
Disclosure of Invention
Because the existing method has the problems, the invention provides a method, a device, an electronic device and a storage medium for calculating the spasm degree.
In a first aspect, the present invention provides a method for calculating a spasticity, comprising:
acquiring dynamic characteristics and electrophysiological characteristics of a patient;
inputting the dynamic characteristics and the electrophysiological characteristics into a trained spasm degree calculation model to obtain a spasm degree calculation result;
the trained spasm degree calculation model is obtained by training the dynamics characteristics, the electrophysiology characteristics and the corresponding spasm degree labels of different patients.
Further, the kinetic signature comprises mechanical impedance parameters of a joint, and the obtaining the kinetic signature of the patient comprises:
acquiring kinematic data and biomechanical data of a patient;
determining a joint angle, a joint angular velocity and a joint angular acceleration according to the kinematic data by adopting Kalman filtering;
performing low-pass filtering on the biomechanics data to obtain moment data;
modeling the patient's dynamics from the joint angle, the joint angular velocity, the joint angular acceleration, and the moment data using an inertia-damping-spring model;
and identifying the mechanical impedance parameters of the joint based on a genetic algorithm to obtain dynamic characteristics.
Further, the mechanical impedance parameters include inertia, damping and rigidity of the affected limb of the patient, and the identifying the mechanical impedance parameters of the joint based on the genetic algorithm includes:
determining a fitness function value of each individual in the population according to a preset evaluation function; wherein each individual is comprised of the inertia, the damping, and the stiffness;
sorting and screening all individuals in the population according to the fitness function value of each individual to obtain a first individual set;
determining corresponding cross probability according to the fitness function value of each individual in the first individual set;
performing cross operation on the first individual set according to the cross probability to obtain a second individual set;
determining corresponding variation probability according to the fitness function value of each individual in the second individual set;
carrying out mutation operation on the second individual set according to the mutation probability to obtain a third individual set;
and repeating the steps until the iteration times reach a preset threshold value, and obtaining an identification result of the mechanical impedance parameters of the joint.
Further, the obtaining of electrophysiological characteristics comprises:
acquiring electrophysiological data; the electrophysiology data comprises electromyographic signals of a plurality of channels;
preprocessing electromyographic signals of the channels;
respectively extracting the average absolute deviation characteristic and the root-mean-square characteristic of the electromyographic signals of each channel;
determining the cooperative shrinkage rate according to the electromyographic signals of the channels;
and determining the electrophysiological characteristics according to the average absolute deviation characteristics, the root mean square characteristics and the cooperative shrinkage rate.
Further, the electromyographic signals of the multiple channels are electromyographic signals from three channels, namely a circumflex muscle, a biceps brachii muscle and a triceps brachii muscle, the cooperative contraction rates include a first cooperative contraction rate and a second cooperative contraction rate, and the determining the cooperative contraction rates according to the electromyographic signals of the multiple channels includes:
determining the first cooperative contraction rate according to an electromyographic signal from a circular pronator and an electromyographic signal from a biceps brachii;
determining the second cooperative contraction rate according to a myoelectric signal from a biceps brachii muscle and a myoelectric signal from a triceps brachii muscle.
Further, before the inputting the dynamic characteristics and the electrophysiological characteristics into the trained spasticity calculation model to obtain a spasticity calculation result, the method further includes:
acquiring a training sample set; wherein each set of training samples comprises a kinetic characteristic, an electrophysiological characteristic, and a spasticity label;
inputting each group of training samples in the training sample set to the spasm degree calculation model respectively to obtain corresponding spasm degree prediction values;
determining a loss value according to the spasm degree label and the corresponding spasm degree predicted value;
and updating the parameters of the spasm degree calculation model according to the loss value to obtain the trained spasm degree calculation model.
In a second aspect, the present invention provides an apparatus for calculating a spasticity, comprising:
the acquisition module is used for acquiring the dynamic characteristics and the electrophysiological characteristics of the patient;
the processing module is used for inputting the dynamic characteristics and the electrophysiological characteristics into a trained spasm degree calculation model to obtain a spasm degree calculation result; the trained spasm degree calculation model is obtained by training the dynamics characteristics, the electrophysiology characteristics and the corresponding spasm degree labels of different patients.
Further, the kinetic signature comprises mechanical impedance parameters of the joint, and the processing module is specifically configured to:
acquiring kinematic data and biomechanical data of a patient;
determining a joint angle, a joint angular velocity and a joint angular acceleration according to the kinematic data by adopting Kalman filtering;
performing low-pass filtering on the biomechanics data to obtain moment data;
modeling the patient's dynamics from the joint angle, the joint angular velocity, the joint angular acceleration, and the moment data using an inertia-damping-spring model;
and identifying the mechanical impedance parameters of the joint based on a genetic algorithm to obtain dynamic characteristics.
Further, the mechanical impedance parameters include inertia, damping and stiffness of the affected limb of the patient, and the processing module is specifically configured to:
determining a fitness function value of each individual in the population according to a preset evaluation function; wherein each individual is comprised of the inertia, the damping, and the stiffness;
sorting and screening all individuals in the population according to the fitness function value of each individual to obtain a first individual set;
determining corresponding cross probability according to the fitness function value of each individual in the first individual set;
performing cross operation on the first individual set according to the cross probability to obtain a second individual set;
determining corresponding variation probability according to the fitness function value of each individual in the second individual set;
carrying out mutation operation on the second individual set according to the mutation probability to obtain a third individual set;
and repeating the steps until the iteration times reach a preset threshold value, and obtaining an identification result of the mechanical impedance parameters of the joint.
Further, the processing module is specifically configured to:
acquiring electrophysiological data; the electrophysiology data comprises electromyographic signals of a plurality of channels;
preprocessing electromyographic signals of the channels;
respectively extracting the average absolute deviation characteristic and the root-mean-square characteristic of the electromyographic signals of each channel;
determining the cooperative shrinkage rate according to the electromyographic signals of the channels;
and determining the electrophysiological characteristics according to the average absolute deviation characteristics, the root mean square characteristics and the cooperative shrinkage rate.
Further, the electromyographic signals of the multiple channels are electromyographic signals from three channels, namely a circumflex muscle, a biceps brachii muscle and a triceps brachii muscle, the cooperative contraction rates include a first cooperative contraction rate and a second cooperative contraction rate, and the processing module is specifically configured to:
determining the first cooperative contraction rate according to an electromyographic signal from a circular pronator and an electromyographic signal from a biceps brachii;
determining the second cooperative contraction rate according to a myoelectric signal from a biceps brachii muscle and a myoelectric signal from a triceps brachii muscle.
Further, the processing module is further configured to:
acquiring a training sample set before inputting the dynamic characteristics and the electrophysiological characteristics into a trained spasm degree calculation model to obtain a spasm degree calculation result; wherein each set of training samples comprises a kinetic characteristic, an electrophysiological characteristic, and a spasticity label;
inputting each group of training samples in the training sample set to the spasm degree calculation model respectively to obtain corresponding spasm degree prediction values;
determining a loss value according to the spasm degree label and the corresponding spasm degree predicted value;
and updating the parameters of the spasm degree calculation model according to the loss value to obtain the trained spasm degree calculation model.
In a third aspect, the present invention provides a system for calculating a cramp level, comprising: the traction control system comprises a traction handle, an angle sensor, a torque sensor, a myoelectricity sensor, a support frame and a spasm degree calculation model;
the traction handle is fixedly connected with the support frame and is used for traction of the affected limb of the patient under the action of external force;
the angle sensor and the torque sensor are coaxial with a joint of a patient;
the angle sensor is used for acquiring kinematic data of a patient;
the torque sensor is used for acquiring biomechanical data of a patient;
the electromyographic sensor is used for acquiring electrophysiological data of a patient;
the supporting frame is used for fixing the affected limb of the patient;
the spasm degree calculation model is used for obtaining a spasm degree calculation result after the kinematic data, the biomechanical data and the electrophysiology data are input.
In a fourth aspect, the present invention further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method for calculating the spasm degree according to the first aspect.
In a fifth aspect, the present invention also provides a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of calculating a cramp level according to the first aspect.
According to the technical scheme, the spasm degree calculation method, the spasm degree calculation device, the electronic equipment and the storage medium provided by the invention are used for independently analyzing the nerve components and the non-nerve components causing the spasm symptoms through the spasm degree calculation model aiming at the dynamic characteristics and the electrophysiological characteristics of the affected limb of the patient in the passive traction movement, so that the comprehensive objective analysis of the spasm degree of the patient is realized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a system framework of a method of spasm calculation according to the present invention;
FIG. 2 is a schematic flow chart of a method for calculating a spasm degree according to the present invention;
FIG. 3 is a flow chart illustrating a method for calculating a spasm degree according to the present invention;
FIG. 4 is a schematic flow chart of a method for calculating a spasm degree according to the present invention;
FIG. 5 is a flow chart illustrating a method for calculating a spasm degree according to the present invention;
FIG. 6 is a schematic structural diagram of a device for calculating a spasm degree according to the present invention;
fig. 7 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
The following further describes embodiments of the present invention with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The method for calculating the spasm degree provided by the embodiment of the present invention can be applied to the system architecture shown in fig. 1, where the system architecture includes a multi-modal data acquisition device 100 and a spasm degree calculation model 200.
Specifically, the multi-modal data acquisition device 100 is used for acquiring dynamic characteristics and electrophysiological characteristics of a patient;
the spasm degree calculation model 200 is used for obtaining the spasm degree calculation result after inputting the kinetic characteristics and the electrophysiological characteristics.
It should be noted that the trained spasm degree calculation model is obtained by training the dynamics characteristics and the electrophysiology characteristics of different patients and the corresponding spasm degree labels.
Further, the multimodal data acquisition apparatus 100 includes: the traction device comprises a traction handle, an angle sensor, a torque sensor, a myoelectric sensor and a support frame;
the traction handle is fixedly connected with the support frame and is used for traction of the affected limb of the patient under the action of external force;
the angle sensor and the torque sensor are coaxial with the joint of the patient;
the angle sensor is used for acquiring kinematic data of a patient;
the torque sensor is used for acquiring biomechanical data of a patient;
the electromyographic sensor is used for acquiring electrophysiological data of the patient;
in one possible embodiment, the electromyographic sensors are attached directly to the patient's circumflex, biceps brachii, and triceps brachii.
The supporting frame is used for fixing the affected limb of the patient.
According to the scheme, the angle sensor and the torque sensor are coaxial with the elbow joint of the patient, so that the accuracy of collecting the joint angle, the joint angular velocity, the joint angular acceleration and the torque data is guaranteed.
Furthermore, the multi-modal data acquisition equipment also comprises an analog-to-digital converter;
the analog-to-digital converter is used for synchronously digitizing the output data of the angle sensor and the torque sensor.
In the embodiment of the invention, the multi-mode data acquisition equipment is worn on the affected side limb, and is fixed through the self-adhesive bandage after being adjusted to a proper height.
Further, with the aid of the doctor, a low speed ratio is achieved, e.g. in the form of
Medium speed such as
High speed such as
The following three passive pulling tasks.
In one possible embodiment, multiple passive pulling movements are applied to the affected forearm of the patient at a specific speed by the pulling handle of the multimodal data acquisition apparatus under the direction of the metronome.
In the embodiment of the present invention, the interval between tasks at different speeds is set to 10s, for example, to avoid the influence of continuous muscle contraction on the spasticity.
It should be noted that fig. 1 is only an example of a system architecture according to the embodiment of the present invention, and the present invention is not limited to this specifically.
Based on the above illustrated system architecture, fig. 2 is a flowchart corresponding to a method for calculating a spasm degree according to an embodiment of the present invention, as shown in fig. 2, the method includes:
It should be noted that, the trained spasm degree calculation model is obtained by training with the dynamic characteristics, electrophysiological characteristics, and corresponding spasm degree labels of different patients.
According to the scheme, the dynamic characteristics and the electrophysiological characteristics of the limb on the affected side of the patient in the passive traction movement are independently analyzed through the spasm degree calculation model, so that the comprehensive objective analysis of the spasm degree of the patient is realized.
In the embodiment of the present invention, in acquiring the dynamic characteristics of the patient, the flow of steps is shown in fig. 3, which specifically includes the following steps:
in step 301, kinematic data and biomechanical data of a patient are acquired.
In the embodiment of the invention, the kinematic data of the patient is acquired through the angle sensor, and the biomechanical data of the patient is acquired through the torque sensor.
Furthermore, the angle sensor and the torque sensor are coaxial with the elbow joint of the patient, and the accuracy of data acquisition is guaranteed.
Specifically, the embodiment of the invention updates the estimation of the state variable by using the joint angle, the joint angular velocity, the estimated value of the joint angular acceleration and the measured value of the joint angle at the current moment based on the Kalman filtering and taking the minimum mean square error as the optimal estimation criterion, so as to obtain the estimated values of the joint angle, the joint angular velocity and the joint angular acceleration at the current moment.
And 303, performing low-pass filtering on the biological and mechanical data to obtain moment data.
In the embodiment of the invention, the torque value applied to the forearm of the affected side of the patient by a doctor by pulling the armrest is acquired by the torque sensor.
In one possible embodiment, the biomechanical data is low-pass filtered using a butterworth low-pass filter.
It should be noted that a chebyshev filter or the like may also be used, and this is not particularly limited in the embodiment of the present invention.
For example, the biomechanical data is low-pass filtered using a butterworth low-pass filter with a cut-off frequency of 20 Hz.
Specifically, a second-order inertia-damping-spring model is used for modeling the dynamic characteristics of the patient, and the method specifically comprises the following steps:
wherein tau (t) represents the resistance moment generated by the affected limb in the passive traction movement process, theta,
And
respectively representing joint angle, joint angular velocity and joint angular acceleration, theta 0 Is the equilibrium position of the elbow joint, I, B and K are the mechanical impedance parameters (inertia, damping and rigidity) of the elbow joint, respectively, and G and L are the gravity and moment arm of the affected limb, respectively.
Further, the moment τ applied by the physician to the pulling handle p (t) the relationship between the corresponding resistive torque produced by the affected limb of the patient is as follows:
τ p (t)＝τ(t)+GLcos(θ)
based on this, the impedance relationship model of the affected limb is rewritten as:
wherein G and L are the gravity and arm of force of the affected limb respectively.
And 305, identifying the mechanical impedance parameters of the joint based on a genetic algorithm to obtain dynamic characteristics.
According to the scheme, phase delay caused by differential operation is avoided by adopting Kalman filtering, the stability of the system is improved, and the accuracy of dynamic characteristics is improved by adopting an inertia-damping-spring model.
In the embodiment of the invention, the mechanical impedance parameters comprise inertia, damping and rigidity of the affected limb of the patient.
Specifically, a fitness function value of each individual in the population is determined according to a preset evaluation function;
it should be noted that each individual is made up of inertia, damping, and stiffness.
In one possible embodiment, the evaluation function used is the root mean square error between the estimated value and the actual value of the externally applied torque.
Sequencing and screening all individuals in the population according to the fitness function value of each individual to obtain a first individual set;
determining corresponding cross probability according to the fitness function value of each individual in the first individual set;
performing cross operation on the first individual set according to the cross probability to obtain a second individual set;
determining corresponding variation probability according to the fitness function value of each individual in the second individual set;
carrying out mutation operation on the second individual set according to the mutation probability to obtain a third individual set;
and repeating the steps until the iteration times reach a preset threshold value, and obtaining an identification result of the mechanical impedance parameters of the joint.
For example, the steps are as follows:
s1: initializing a population, defining the size of the population as T, and obtaining individuals consisting of genes in a genetic space by adopting a floating point number coding mode.
It should be noted that the genes may be inertia, damping, rigidity, equilibrium position of the joint, and the like.
S2: calculating the fitness function value of each individual in the initial population, performing descending order on the fitness values of all the individuals in the population, and selecting the individual with the highest fitness as the elite in the population.
S3: and carrying out cross operation on the population to generate T new individuals, wherein the cross probability of the individuals in the parent population is determined according to the fitness function values of the individuals, calculating the fitness function values of the parent individuals and the offspring individuals, sequentially selecting the individuals with the highest fitness in the parent population and the offspring population to compare, and reserving the individuals with the best fitness as elite to form a new generation population.
S4: carrying out mutation operation on the population to generate T new individuals, wherein the mutation probability of the individuals in the parent population is determined according to the fitness function value of the individuals, calculating the fitness function values of the parents and the offspring individuals, sequentially selecting the individuals with the highest fitness in the parent population and the offspring population to compare, and reserving the individuals with the best fitness as elite to form a new population;
s5: and repeating the step S2, and when the iteration number reaches the maximum evolution algebra N, terminating the iteration process, otherwise, returning to S3 for loop execution.
According to the scheme, the accuracy of identifying the mechanical impedance parameters of the joints is improved by improving the genetic algorithm, so that the accuracy of the calculating result of the spasm degree is improved.
In the embodiment of the invention, in acquiring the electrophysiological characteristics, the flow of steps is shown in fig. 4, which specifically includes the following steps:
at step 401, electrophysiological data is acquired.
Note that the electrophysiological data contains electromyographic signals of a plurality of channels.
In one possible embodiment, the electromyographic signals of the plurality of channels are electromyographic signals from three channels of the circular muscle of pronation, the biceps brachii and the triceps brachii.
It should be noted that the myoelectric signals from the anterior portion of the deltoid muscle, the middle portion of the deltoid muscle, the posterior portion of the deltoid muscle, the brachioradial muscle, the extensor digitorum muscle, and the like may be used, and the embodiment of the present invention is not particularly limited thereto.
Specifically, the electromyographic signals of each channel are processed by a band-pass filter, for example, the electromyographic signals are processed by a band-pass filter with a cutoff frequency of 20Hz and 200Hz, a direct current offset and a high frequency noise are removed, full-wave rectification and low-pass filtering are performed on the electromyographic signals of each channel to obtain an envelope curve of the signals, and finally normalization processing is performed on the envelope curve.
And step 403, respectively extracting the average absolute deviation characteristic and the root-mean-square characteristic of the electromyographic signals of each channel.
Respectively extracting average absolute deviation characteristics v of each channel k And root mean square characteristic σ k The calculation formula of (a) is specifically as follows:
wherein M represents the total number of sampling points of the Kth channel electromyogram signal, sEMG k (t) represents a myoelectric signal of a Kth channel.
It should be noted that the total number of sampling points is determined by the sampling frequency and the sampling time duration.
And step 404, determining the cooperative shrinkage rate according to the electromyographic signals of the channels.
In the embodiment of the present invention, the cooperative shrinkage rate includes a first cooperative shrinkage rate and a second cooperative shrinkage rate.
Specifically, a first cooperative contraction rate is determined according to an electromyographic signal from a circular pronator and an electromyographic signal from a biceps brachii muscle;
the second cooperative contraction rate is determined from the myoelectric signal from the biceps brachii muscle and the myoelectric signal from the triceps brachii muscle.
In the present example, 2 pairs of synergistic shrinkages CR between the voluntary and antagonistic muscles were extracted 1 、CR 2 The specific calculation formula is as follows:
wherein, sEMG 1 (t)，sEMG 2 (t) and sEMG 3 (t) shows myoelectric signals of the circumflex muscle, biceps brachii and triceps brachii, respectively, t 1 And t 2 Respectively representing the initial and end moments of a single preset speed drawing movement.
And step 405, determining the electrophysiological characteristics according to the average absolute deviation characteristics, the root-mean-square characteristics and the cooperative shrinkage rate.
According to the scheme, the muscle activity characteristics are quantized by extracting the average absolute deviation characteristics, the root mean square characteristics and the cooperative shrinkage rate of the electromyographic signals of all the channels, and the accuracy of the electrophysiology characteristics is improved.
Further, before step 202, the step flow in the embodiment of the present invention is shown in fig. 5, which specifically includes the following steps:
It should be noted that, each set of training samples includes a dynamic feature, an electrophysiological feature, and a spasm degree label.
For example, a spasticity label of 0 indicates a normal state, and a spasticity label of 1 indicates an abnormal state.
And 502, respectively inputting each group of training samples in the training sample set to the spasm degree calculation model to obtain corresponding spasm degree prediction values.
In one possible embodiment, the spasticity calculation model is constructed by a Feed Forward Neural Network (FFNN).
For example, the FFNN has one input layer, one hidden layer and one output layer, whose neurons employ hyper-bolic range and linear transfer activation functions, respectively.
And step 503, determining a loss value according to the spasm degree label and the corresponding spasm degree predicted value.
And step 504, updating parameters of the spasm degree calculation model according to the loss value to obtain the trained spasm degree calculation model.
The input vectors based on the above-described construction of the multi-level fusion are as follows:
wherein eta is n The first cooperative contraction rate or the second cooperative contraction rate is expressed by representing inertia, damping and rigidity of the affected limb of the patient, and mean absolute deviation characteristics, root mean square characteristics and the first cooperative contraction rate or the second cooperative contraction rate of electromyographic signals of all channels.The dimension of the input vector is 11.
Further, the resulting output vector is represented as follows:
S i ＝F(X i )
in one possible embodiment, the results obtained in the pulling task at different speeds are further fused:
wherein the content of the first and second substances,
indicating the calculated spasticity of the patient.
For example, when the spasticity label is labeled 1 for a patient and 0 for a non-patient, the closer the value is to 0, the less severe the spastic symptoms in the patient's upper limb, the closer the motor function is to normal.
Based on the same inventive concept, fig. 6 exemplarily illustrates an apparatus for calculating a spasm degree according to an embodiment of the present invention, which may be a flow of a method for calculating a spasm degree.
The apparatus, comprising:
an acquisition module 601 for acquiring kinetic and electrophysiological characteristics of a patient;
the processing module 602 is configured to input the dynamic characteristics and the electrophysiological characteristics into a trained spasm degree calculation model to obtain a spasm degree calculation result; the trained spasm degree calculation model is obtained by training the dynamics characteristics, the electrophysiology characteristics and the corresponding spasm degree labels of different patients.
Further, the dynamic characteristics include mechanical impedance parameters of the joint, and the processing module 602 is specifically configured to:
acquiring kinematic data and biomechanical data of a patient;
determining a joint angle, a joint angular velocity and a joint angular acceleration according to the kinematic data by adopting Kalman filtering;
performing low-pass filtering on the biomechanics data to obtain moment data;
modeling the patient's dynamics from the joint angle, the joint angular velocity, the joint angular acceleration, and the moment data using an inertia-damping-spring model;
and identifying the mechanical impedance parameters of the joint based on a genetic algorithm to obtain dynamic characteristics.
Further, the mechanical impedance parameters include inertia, damping, and stiffness of the affected limb of the patient, and the processing module 602 is specifically configured to:
determining a fitness function value of each individual in the population according to a preset evaluation function; wherein each individual is comprised of the inertia, the damping, and the stiffness;
sorting and screening all individuals in the population according to the fitness function value of each individual to obtain a first individual set;
determining corresponding cross probability according to the fitness function value of each individual in the first individual set;
performing cross operation on the first individual set according to the cross probability to obtain a second individual set;
determining corresponding variation probability according to the fitness function value of each individual in the second individual set;
carrying out mutation operation on the second individual set according to the mutation probability to obtain a third individual set;
and repeating the steps until the iteration times reach a preset threshold value, and obtaining an identification result of the mechanical impedance parameters of the joint.
Further, the processing module 602 is specifically configured to:
acquiring electrophysiological data; the electrophysiology data comprises electromyographic signals of a plurality of channels;
preprocessing electromyographic signals of the channels;
respectively extracting the average absolute deviation characteristic and the root-mean-square characteristic of the electromyographic signals of each channel;
determining the cooperative shrinkage rate according to the electromyographic signals of the channels;
and determining the electrophysiological characteristics according to the average absolute deviation characteristics, the root mean square characteristics and the cooperative shrinkage rate.
Further, the electromyographic signals of the multiple channels are electromyographic signals from three channels, namely a circumflex muscle, a biceps brachii muscle and a triceps brachii muscle, the cooperative contraction rates include a first cooperative contraction rate and a second cooperative contraction rate, and the processing module 602 is specifically configured to:
determining the first cooperative contraction rate according to an electromyographic signal from a circular pronator and an electromyographic signal from a biceps brachii;
determining the second cooperative contraction rate according to a myoelectric signal from a biceps brachii muscle and a myoelectric signal from a triceps brachii muscle.
Further, the processing module 602 is further configured to:
acquiring a training sample set before inputting the dynamic characteristics and the electrophysiological characteristics into a trained spasm degree calculation model to obtain a spasm degree calculation result; wherein each set of training samples comprises a kinetic characteristic, an electrophysiological characteristic, and a spasticity label;
inputting each group of training samples in the training sample set to the spasm degree calculation model respectively to obtain corresponding spasm degree prediction values;
determining a loss value according to the spasm degree label and the corresponding spasm degree predicted value;
and updating the parameters of the spasm degree calculation model according to the loss value to obtain the trained spasm degree calculation model.
Based on the same inventive concept, another embodiment of the present invention provides an electronic device, which specifically includes the following components, with reference to fig. 7: a processor 701, a memory 702, a communication interface 703 and a communication bus 704;
the processor 701, the memory 702 and the communication interface 703 complete mutual communication through the communication bus 704; the communication interface 703 is used for implementing information transmission between the devices;
the processor 701 is configured to call a computer program in the memory 702, and the processor implements all the steps of the method for calculating the jerk when executing the computer program, for example, the processor implements the following steps when executing the computer program: acquiring dynamic characteristics and electrophysiological characteristics of a patient; inputting the dynamic characteristics and the electrophysiological characteristics into a trained spasm degree calculation model to obtain a spasm degree calculation result; the trained spasm degree calculation model is obtained by training the dynamics characteristics, the electrophysiology characteristics and the corresponding spasm degree labels of different patients.
Based on the same inventive concept, a further embodiment of the present invention provides a non-transitory computer readable storage medium, on which a computer program is stored, which when executed by a processor performs all the steps of the method for calculating a jerk, for example, the processor performs the following steps when executing the computer program: acquiring dynamic characteristics and electrophysiological characteristics of a patient; inputting the dynamic characteristics and the electrophysiological characteristics into a trained spasm degree calculation model to obtain a spasm degree calculation result; the trained spasm degree calculation model is obtained by training the dynamics characteristics, the electrophysiology characteristics and the corresponding spasm degree labels of different patients.
In addition, the logic instructions in the memory may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention or a part thereof, which essentially contributes to the prior art, can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a device for calculating a spasm degree, or a network device, etc.) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. Based on such understanding, the technical solutions mentioned above may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes several instructions for enabling a computer device (which may be a personal computer, a device for calculating the jerk degree, or a network device, etc.) to execute the method for calculating the jerk degree according to the embodiments or some parts of the embodiments.
In addition, in the present invention, terms such as "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Moreover, in the present invention, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Furthermore, in the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. A method of calculating a spasticity, comprising:
acquiring a kinetic signature of a patient, the kinetic signature including mechanical impedance parameters of a joint, the acquiring the kinetic signature of the patient comprising:
acquiring kinematic data and biomechanical data of a patient;
determining a joint angle, a joint angular velocity and a joint angular acceleration according to the kinematic data by adopting Kalman filtering;
performing low-pass filtering on the biomechanics data to obtain moment data;
modeling the patient's dynamics from the joint angle, the joint angular velocity, the joint angular acceleration, and the moment data using an inertia-damping-spring model;
identifying mechanical impedance parameters of the joint based on a genetic algorithm to obtain dynamic characteristics;
acquiring electrophysiological data; the electrophysiology data comprises electromyographic signals of a plurality of channels;
preprocessing electromyographic signals of the channels;
respectively extracting the average absolute deviation characteristic and the root-mean-square characteristic of the electromyographic signals of each channel;
determining the cooperative shrinkage rate according to the electromyographic signals of the channels;
determining an electrophysiological characteristic according to the average absolute deviation characteristic, the root mean square characteristic and the cooperative shrinkage rate;
inputting the dynamic characteristics and the electrophysiological characteristics into a trained spasm degree calculation model to obtain a spasm degree calculation result;
the trained spasm degree calculation model is obtained by training the dynamics characteristics, the electrophysiology characteristics and the corresponding spasm degree labels of different patients.
2. The method of calculating spasticity according to claim 1, wherein the mechanical impedance parameters comprise inertia, damping and stiffness of an affected limb of the patient, and the identifying the mechanical impedance parameters of the joint based on a genetic algorithm comprises:
determining a fitness function value of each individual in the population according to a preset evaluation function; wherein each individual is comprised of the inertia, the damping, and the stiffness;
sorting and screening all individuals in the population according to the fitness function value of each individual to obtain a first individual set;
determining corresponding cross probability according to the fitness function value of each individual in the first individual set;
performing cross operation on the first individual set according to the cross probability to obtain a second individual set;
determining corresponding variation probability according to the fitness function value of each individual in the second individual set;
carrying out mutation operation on the second individual set according to the mutation probability to obtain a third individual set;
and repeating the steps until the iteration times reach a preset threshold value, and obtaining an identification result of the mechanical impedance parameters of the joint.
3. The method of calculating a spasticity according to claim 1, wherein the electromyographic signals for the plurality of channels are electromyographic signals from three channels of a circular pronation muscle, a biceps brachii muscle, and a triceps brachii muscle, and wherein the cooperative contraction rate includes a first cooperative contraction rate and a second cooperative contraction rate, and wherein determining the cooperative contraction rate based on the electromyographic signals for the plurality of channels comprises:
determining the first cooperative contraction rate according to an electromyographic signal from a circular pronator and an electromyographic signal from a biceps brachii;
determining the second cooperative contraction rate according to a myoelectric signal from a biceps brachii muscle and a myoelectric signal from a triceps brachii muscle.
4. The method of calculating a cramp level according to claim 1, wherein before inputting the dynamic characteristics and the electrophysiological characteristics into the trained cramp level calculation model to obtain the cramp level calculation result, the method further comprises:
acquiring a training sample set; wherein each set of training samples comprises a kinetic characteristic, an electrophysiological characteristic, and a spasticity label;
inputting each group of training samples in the training sample set to the spasm degree calculation model respectively to obtain corresponding spasm degree prediction values;
determining a loss value according to the spasm degree label and the corresponding spasm degree predicted value;
and updating the parameters of the spasm degree calculation model according to the loss value to obtain the trained spasm degree calculation model.
5. A system for calculating a cramp level, the system for calculating a cramp level being a system to which the method according to any one of claims 1 to 4 is applied, comprising: the device comprises a traction handle, an angle sensor, a torque sensor, a myoelectric sensor, a support frame, a band-pass filter and a spasm degree calculation model;
the traction handle is fixedly connected with the support frame and is used for traction of the affected limb of the patient under the action of external force;
the angle sensor and the torque sensor are coaxial with a joint of a patient;
the angle sensor is used for acquiring kinematic data of a patient;
the torque sensor is used for acquiring biomechanical data of a patient;
the electromyographic sensor is used for acquiring electrophysiology data of a patient, and the electrophysiology data comprises electromyographic signals of a plurality of channels;
the band-pass filter is used for preprocessing electromyographic signals of the channels;
the support frame is used for fixing a wounded limb of a patient;
the spasm degree calculation model is used for obtaining a spasm degree calculation result after the kinematic data, the biomechanical data and the electrophysiology data are input.
6. An apparatus for calculating a spasticity, comprising:
an acquisition module for acquiring a kinetic signature of a patient, the kinetic signature including mechanical impedance parameters of a joint, the acquiring the kinetic signature of the patient comprising: acquiring kinematic data and biomechanical data of a patient; determining a joint angle, a joint angular velocity and a joint angular acceleration according to the kinematic data by adopting Kalman filtering; performing low-pass filtering on the biomechanics data to obtain moment data; modeling the patient's dynamics from the joint angle, the joint angular velocity, the joint angular acceleration, and the moment data using an inertia-damping-spring model; identifying mechanical impedance parameters of the joint based on a genetic algorithm to obtain dynamic characteristics; acquiring electrophysiological data; the electrophysiology data comprises electromyographic signals of a plurality of channels; preprocessing electromyographic signals of the channels; respectively extracting the average absolute deviation characteristic and the root-mean-square characteristic of the electromyographic signals of each channel; determining the cooperative shrinkage rate according to the electromyographic signals of the channels; determining an electrophysiological characteristic according to the average absolute deviation characteristic, the root mean square characteristic and the cooperative shrinkage rate;
the processing module is used for inputting the dynamic characteristics and the electrophysiological characteristics into a trained spasm degree calculation model to obtain a spasm degree calculation result; the trained spasm degree calculation model is obtained by training the dynamics characteristics, the electrophysiology characteristics and the corresponding spasm degree labels of different patients.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1 to 4 are implemented when the processor executes the program.
8. A non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 4.
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