CN112353385A - Training mode recognition system, method and application based on variant sigmoid function classifier - Google Patents

Training mode recognition system, method and application based on variant sigmoid function classifier Download PDF

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CN112353385A
CN112353385A CN202011131699.7A CN202011131699A CN112353385A CN 112353385 A CN112353385 A CN 112353385A CN 202011131699 A CN202011131699 A CN 202011131699A CN 112353385 A CN112353385 A CN 112353385A
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冯雷
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Nanjing Vishee Medical Technology Co Ltd
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Abstract

The invention discloses a training mode recognition system and method based on a modified sigmoid function classifier and application of the training mode recognition system and method in an active and passive training rehabilitation machine.

Description

Training mode recognition system, method and application based on variant sigmoid function classifier
Technical Field
The invention relates to a training mode recognition system and algorithm of an active and passive rehabilitation training machine, in particular to a training mode recognition system, method and application of the active and passive rehabilitation training machine based on a modified sigmoid function classifier; belongs to the technical field of rehabilitation medicine.
Background
When the limbs are disabled, contracture, deformity, paralysis, or stiffness of joints or spine, the motor function may have dysfunction or partial function loss in different degrees, which is called as the limb disability. The rehabilitation device aims to help the disabled to recover the physical function as soon as possible, can be quickly and better integrated into social life, is guided by national policies, is motivated by the development of rehabilitation medical equipment, and becomes a research hotspot in the field of rehabilitation medicine by exploring how to help the disabled to recover the limb activities.
Medical research shows that the human nervous system has plasticity, and if reasonable and effective rehabilitation training can be obtained at the early stage of limb disability, the damaged cerebral motor nerve can be recovered to a great extent. However, if the optimal rehabilitation period is missed and the stump is not actively trained, the motor nerves of the brain are atrophied and recovery is more difficult. Therefore, the timely and effective rehabilitation training is very important for recovering the limb movement function.
At present, for rehabilitation training equipment for active and passive exercise of upper and lower limbs, a training mode generally comprises a function of automatically judging an active and passive training mode. The main technical realization method is that external sensors such as a position detection module, a speed detection module, a joint force acquisition module and a contact force acquisition module are added in a control system, and the corresponding mode judgment function is realized by setting a sensor threshold value.
Indeed, various types of sensors can be used to achieve different degrees of mode judgment effects, but firstly, hardware cost is increased, secondly, the product size is correspondingly adjusted in a matching manner, information interaction between a controller and the sensors needs to be additionally established, load of a control system is increased, and meanwhile, real-time performance of the system is sacrificed to a certain degree.
Disclosure of Invention
In order to solve the defects of the prior art, the invention aims to provide a training mode identification system based on a variant sigmoid function classifier;
the second purpose is to provide an identification method based on the identification system, which can quickly and accurately identify various mode changes and control motion modes;
the third purpose is to provide the application prospect of the system and the method on the active and passive training machines.
In order to achieve the above object, the present invention adopts the following technical solutions:
the invention firstly discloses a training mode identification system based on a variant sigmoid function classifier, which comprises the following steps: the device comprises a controller, a filtering processor and a modified sigmoid function classifier;
the controller collects the real-time torque of the servo motor and sends the real-time torque to the filtering processor;
the filter processor receives a torque signal from the controller, removes torque waveform noise and burrs, performs torque value smoothing processing, stabilizes a waveform and reduces variation amplitude;
the variant sigmoid function classifier judges and identifies training modes according to torque values, and the expression is as follows: sc ═ sign (x) - (1/(1+ e)(-0.01*x)) -0.5) × 2, wherein x represents the torque value and e is a constant.
Then, the invention discloses an identification method of a training mode identification system based on a modified sigmoid function classifier, which comprises the following steps:
s1, setting a passive mode as an initial training mode, and acquiring a real-time torque value of the servo motor by the controller;
s2, filtering the acquired real-time torque value, removing torque waveform noise and burrs, and smoothing the torque value;
s3, after the torque value is obtained, classifying the torque value by using a modified sigmoid function classifier, and judging the current user state; the variant sigmoid function classifier is as follows:
Sc＝sign(x)-(1/(1+e(-0.01*x)) -0.5) × 2, wherein x represents the torque value and e is a constant.
Preferably, the foregoing step S2 includes the following sub-steps:
s2.1, adopting a first-order lag filtering algorithm to inhibit periodic interference and carrying out first smoothing treatment on a torque value;
and S2.2, carrying out second mobile smoothing treatment on the torque value within the set fluctuation range by adopting an amplitude limiting and first-order lag filtering algorithm to obtain a torque filtering value.
Preferably, the foregoing step S2.1 is specifically: let the actual filtering force value Tn＝0.3*Tl+0.7*TaThen T isu＝Tn、T5＝T4、T4＝T3、T3＝T2、T2＝T1And T1＝TuCalculating and judging | (T)u/T4-1) | 100 satisfies | (T) of 10 ≦ or | (T)u/T4-1)|*100<60, if the first torque filtering result value T is satisfied (namely, the first torque filtering result value T is in accordance with the judgment condition), performing first smoothing treatment to obtain a first torque filtering result value Tm1＝Tn*0.1+Tl0.9, then Tu＝Tm1、T5＝T4、T4＝T3、T3＝T2、T2＝T1And T1＝Tu(ii) a If not, recording Tn＝TuEnding the filtering processing; wherein, Tn: actual filtering force values; t isl: a previous cycle force value; t isa: measuring force values in the current period; t isu: current period force values to be filtered; t is1～T5: cumulative process force value, Tm1The first torque filtering result value is represented.
More preferably, the step S2.2 is specifically: calculate and judge | (T)uWhether or not the value of/T5-1/100 satisfies 2 ≦ (T)u/T5-1)|*100<60, if the first torque filtering result value T is met, obtaining a second torque filtering result value Tm2＝Tu*0.1+T2*0.9，Tu＝Tm2(ii) a If not, recording Tn＝TuEnding the filtering processing; wherein, Tm2The second torque filtering result value is represented.
Preferably, after the filtering treatment, the waveform variation amplitude is 50-75.
Further preferably, the current user state includes: passive, active, or spastic.
Finally, the invention also discloses application of the training mode identification system based on the modified sigmoid function classifier in an active and passive rehabilitation training machine. The user training modalities in the rehabilitation training machine are identified, and the four categories include passive, assistance, resistance and spasm. The normal training process can be intelligently judged in a passive mode, an assisting mode and an anti-blocking mode, the spasm mainly judges the abnormal feedback of the user in the training process, and the spasm protection is selected.
The invention has the advantages that:
according to the training mode identification system and method, the servo motor is used as a motion driving unit, the servo driver is used as an information feedback unit, the position, speed, torque and other information of the servo motor are obtained through the controller, a rehabilitation training process model is established, the real-time torque of the servo motor is taken as an object under the condition that an auxiliary sensor is not added, the motion intention of a patient is identified through a modified sigmoid function classifier, high precision and high sensitivity of the servo motor are relied on, the rapid judgment of various training modes can be realized, and the accuracy and the smoothness of mode identification are guaranteed.
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FIG. 1 is a flow chart of a training modality recognition method of the present invention;
FIG. 2 is a flow chart of the filtering process in the training mode recognition method of the present invention;
FIG. 3 is a schematic diagram illustrating a comparison of filtering effects in the training mode recognition method according to the present invention;
FIG. 4 is a schematic diagram of a sigmoid function classifier in the prior art;
FIG. 5 is a schematic diagram of a modified sigmoid function classifier of the present invention;
fig. 6 is a diagram of the classification result after the training mode recognition method of the present invention is applied.
Detailed Description
The invention is described in detail below with reference to the figures and the embodiments.
The training mode identification method is suitable for active and passive rehabilitation training machines, can efficiently and accurately identify the movement intention of a patient, and further realizes the quick judgment of various training modes, wherein the commonly used training modes comprise four types of passive, assisted, resistance and spasm. The normal training process can be intelligently judged under the passive, assisting and resisting modes, and the spasm mode is mainly used for judging the abnormal feedback of the user in the training process to carry out spasm protection.
Referring to fig. 1, a basic mode of training is a passive mode, a system runs at a set speed, and a controller obtains a real-time torque value of a servo motor. For the servo motor, maintaining a constant speed requires that the torque output by the motor shaft is constant, and if an external force (such as a rehabilitation trainer, also called a user) applies an additional torque to the motor shaft, the servo motor adjusts the torque output to balance the external force. Therefore, the change state of the limb strength of the user can be reflected according to the real-time torque value of the servo motor in the operation process of the system.
Based on the above thought, after the controller acquires the real-time torque of the servo motor, torque waveform noise and burrs are removed through the filter, periodic interference is suppressed, and torque value smoothing is performed, so that the obtained waveform is more stable, the variation amplitude is reduced, and the waveform can be conveniently classified subsequently. After the torque filtering value is obtained, a modified sigmoid function classifier is used for classifying and judging the torque value so as to determine that the training mode of the current user is passive, active or spasm. sigmoid function prototype is: 1/(1+ e) for S-x) The function graph is shown in fig. 4. In the invention, the sigmoid function is skillfully modified, and the modified sigmoid function classifier is as follows: sc ═ sign (x) - (1/(1+ e)(-0.01*x)) -0.5) × 2, the graph of the function is shown in fig. 5.
The filtering algorithm plays a crucial role in subsequent waveform classification, and the filtering principle is as follows: firstly, a first-order lag filtering algorithm is adopted to inhibit periodic interference and carry out first smoothing treatment on a torque value; and then, carrying out second mobile smoothing treatment on the torque value within the set fluctuation range by adopting an amplitude limiting and first-order lag filtering algorithm to finally obtain a torque filtering value.
The specific filtering process flow diagram is shown in fig. 2:
first, let the actual filtering force value Tn＝0.3*Tl+0.7*TaThen T isu＝Tn、T5＝T4、T4＝T3、T3＝T2、T2＝T1And T1＝Tu. Calculate and judge | (T)u/T4-1) | 100 satisfies | (T) of 10 ≦ or | (T)u/T4-1)|*100<60, if the first torque filtering result value T is satisfied (namely, the first torque filtering result value T is in accordance with the judgment condition), performing first smoothing treatment to obtain a first torque filtering result value Tm1＝Tn*0.1+Tl0.9, then Tu＝Tm1、T5＝T4、T4＝T3、T3＝T2、T2＝T1And T1＝Tu(ii) a If not, recording Tn＝TuAnd ending the filtering processing.
Then, the second smoothing process is performed to calculate and judge | (T)uWhether or not the value of/T5-1/100 satisfies 2 ≦ (T)u/T5-1)|*100<60, if the first torque filtering result value T is satisfied (i.e. the judgment condition is met), obtaining a second torque filtering result value Tm2＝Tu*0.1+T2*0.9，Tu＝Tm2(ii) a If not, recording Tn＝TuAnd ending the filtering processing.
Wherein, the meaning of each parameter is as follows:
Tm1and Tm2Are all result values of filtering the torque values meeting the judgment conditions, Tm1Representing the first torque filter result value (transition variable 1), Tm2Represents the second torque filtering result value (transition variable 2);
Tn: actual filtering force values; t isl: a previous cycle force value; t isa: measuring force values in the current period; tu: current period force values to be filtered; t is1～T5: the process force value is accumulated.
The filtering effect is shown in fig. 3, wherein fig. 3a is an original torque waveform diagram, periodic peaks and troughs can be seen, the variation amplitude is large, and the fluctuation range is 20-140; fig. 3b is a torque waveform diagram after filtering, and it is obvious that the waveform is more stable, the variation amplitude is limited between 50 and 75, and the waveform classification is more facilitated.
After the torque filtering value is obtained through filtering processing, the torque value is classified and judged to be passive, active or spasm by adopting a modified sigmoid function classifier, the judging process is more accurate and stable, and the sensitivity and the stability are greatly improved.
As is known, sigmoid function prototypes are: 1/(1+ e) for S-x) Function diagramThe form is shown in fig. 4. In the invention, in order to make the function more accord with the modal identification and classification requirements of the system, the sigmoid function is improved, and a modified sigmoid function classifier is designed: sc ═ sign (x) - (1/(1+ e)(-0.01*x)) -0.5) × 2, the graph of the function is shown in fig. 5, where x represents the torque value and e is a constant.
Based on the above modified sigmoid function classifier, the obtained torque value classification result is shown in fig. 6, where fig. 6a is a waveform diagram after filtering processing, and simulates torque values in the passive and resistive training mode judgment process; fig. 6b is a diagram of the classification result after being processed by the classifier. It can be seen from the figure that after being processed by the classifier, the controller is more stable and accurate in classifying the passive mode and the active mode, so that local jitter and abnormal judgment are avoided, and analysis and identification of the active consciousness of the user are facilitated.
For better understanding and implementing the present invention, the classification rule is as follows, with 200N as the classification criterion:
m1 (passive): -200N to 200N;
m2 (boost): > 200N;
m3 (resistance): > 400N;
m4 (spasm): < -200N.
In conclusion, the rehabilitation training device for active and passive movement of upper and lower limbs comprises the modes of passive movement, assisted movement, resistance movement, spasm detection and the like, in the mixed mode training process, the controller is required to judge the movement intention of the patient in real time, the corresponding movement mode is obtained through timely judgment, and the accuracy, rapidity and fluency of mode judgment are guaranteed. The core is that: according to the invention, a modified sigmoid function is developed as a classifier, so that the feedback torque value of the motor can be judged and classified more accurately and stably, the occurrence of states such as local jitter and abnormal judgment is avoided, the sensitivity and stability of user intention recognition are further ensured, more perfect user experience and biofeedback are obtained, and the rapid and accurate judgment of various training modes is realized.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It should be understood by those skilled in the art that the above embodiments do not limit the present invention in any way, and all technical solutions obtained by using equivalent alternatives or equivalent variations fall within the scope of the present invention.

Claims (8)

1. Training mode identification system based on variant sigmoid function classifier, which is characterized by comprising:
the device comprises a controller, a filtering processor and a modified sigmoid function classifier;
the controller collects the real-time torque of the servo motor and sends the real-time torque to the filtering processor;
the filtering processor receives a torque signal from the controller, removes torque waveform noise and burrs,
carrying out torque value smoothing treatment, stabilizing the waveform and reducing the variation amplitude;
the variant sigmoid function classifier judges and identifies training modes according to torque values, and the expression is as follows: sc ═ sign (x) - (1/(1+ e)(-0.01*x)) -0.5) × 2, wherein x represents the torque value and e is a constant.
2. The recognition method of the training modality recognition system based on the modified sigmoid function classifier according to claim 1, comprising the steps of:
s1, setting a passive mode as an initial training mode, and acquiring a real-time torque value of the servo motor by the controller;
s2, filtering the acquired real-time torque value, removing torque waveform noise and burrs, and smoothing the torque value;
s3, after the torque value is obtained, classifying the torque value by using a modified sigmoid function classifier, and judging the current user state; the variant sigmoid function classifier is as follows:
Sc＝sign(x)-(1/(1+e(-0.01*x)) -0.5) × 2, wherein x represents the torque value and e is a constant.
3. The method of claim 1 for recognition of a training modality based on a modified sigmoid function classifier,
the step S2 includes the following substeps:
s2.1, adopting a first-order lag filtering algorithm to inhibit periodic interference and carrying out first smoothing treatment on a torque value;
and S2.2, carrying out second mobile smoothing treatment on the torque value within the set fluctuation range by adopting an amplitude limiting and first-order lag filtering algorithm to obtain a torque filtering value.
4. The method for recognizing training modalities based on the modified sigmoid function classifier according to claim 3, wherein the step S2.1 is specifically as follows: let the actual filtering force value Tn＝0.3*Tl+0.7*TaThen T isu＝Tn、T5＝T4、T4＝T3、T3＝T2、T2＝T1And T1＝TuCalculating and judging | (T)u/T4-1) | 100 satisfies | (T) of 10 ≦ or | (T)u/T4-1)|*100<60, if the first torque filtering result value T is satisfied (namely, the first torque filtering result value T is in accordance with the judgment condition), performing first smoothing treatment to obtain a first torque filtering result value Tm1＝Tn*0.1+Tl0.9, then Tu＝Tm1、T5＝T4、T4＝T3、T3＝T2、T2＝T1And T1＝Tu(ii) a If not, recording Tn＝TuEnding the filtering processing; wherein, Tn: actual filtering force values; t isl: a previous cycle force value; t isa: measuring force values in the current period; t isu: current period force values to be filtered; t is1～T5: cumulative process force value, Tm1The first torque filtering result value is represented.
5. The method for recognizing training modalities based on the modified sigmoid function classifier according to claim 4, wherein the step S2.2 is specifically as follows: calculate and judge | (T)uWhether or not the value of/T5-1/100 satisfies 2 ≦ (T)u/T5-1)|*100<60, if the condition is satisfied,a second torque filtering result value T is obtainedm2＝Tu*0.1+T2*0.9，Tu＝Tm2(ii) a If not, recording Tn＝TuEnding the filtering processing; wherein, Tm2The second torque filtering result value is represented.
6. The method for recognizing the training mode based on the modified sigmoid function classifier according to claim 5, wherein after the filtering process, the waveform variation amplitude is 50-75.
7. The method of claim 2, wherein the current user state comprises: passive, active, or spastic.
8. Use of the modified sigmoid function classifier based training modality recognition system of claim 1 in an active and passive rehabilitation training machine.
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