CN112370341B - Method for realizing ear stimulation training device based on machine learning - Google Patents
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- 238000012549 training Methods 0.000 title claims abstract description 80
- 230000000638 stimulation Effects 0.000 title claims abstract description 71
- 238000010801 machine learning Methods 0.000 title claims abstract description 16
- 238000000034 method Methods 0.000 title claims abstract description 16
- 238000001467 acupuncture Methods 0.000 claims abstract description 23
- 238000003062 neural network model Methods 0.000 claims abstract description 7
- 238000013528 artificial neural network Methods 0.000 claims description 8
- 230000006870 function Effects 0.000 claims description 3
- 230000001537 neural effect Effects 0.000 claims description 2
- 208000028389 Nerve injury Diseases 0.000 abstract description 8
- 230000008764 nerve damage Effects 0.000 abstract description 8
- 238000011084 recovery Methods 0.000 abstract description 6
- 238000012545 processing Methods 0.000 abstract description 5
- 238000007781 pre-processing Methods 0.000 abstract description 2
- 230000000694 effects Effects 0.000 description 8
- 230000004936 stimulating effect Effects 0.000 description 4
- 201000010099 disease Diseases 0.000 description 3
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 3
- 238000009472 formulation Methods 0.000 description 3
- 239000000203 mixture Substances 0.000 description 3
- 206010001497 Agitation Diseases 0.000 description 2
- 230000001276 controlling effect Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 210000005069 ears Anatomy 0.000 description 2
- 210000005036 nerve Anatomy 0.000 description 2
- 241000283690 Bos taurus Species 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000009098 adjuvant therapy Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
- A61H39/00—Devices for locating or stimulating specific reflex points of the body for physical therapy, e.g. acupuncture
- A61H39/002—Using electric currents
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
- A61H2201/00—Characteristics of apparatus not provided for in the preceding codes
- A61H2201/50—Control means thereof
- A61H2201/5007—Control means thereof computer controlled
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
- A61H2205/00—Devices for specific parts of the body
- A61H2205/02—Head
- A61H2205/027—Ears
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Abstract
The invention discloses a method for realizing an ear stimulation training device based on machine learning, wherein the ear stimulation training device comprises a plurality of stimulation components for fixing on different acupuncture points on the ear, a control device which is connected with all the stimulation components and stores ear acupuncture point map information, a wireless module arranged on the control device, and a remote terminal connected with the wireless module; the implementation method comprises the following steps: establishing a database; (2) preprocessing original data; (3) missing data processing; (4) developing a neural network model; (5) generating a predictive guideline for ear stimulation training. The invention has simple structure, low cost and convenient carrying, establishes a corresponding neural network model by adopting a machine learning mode, predicts the stimulation training recovery degrees under different ear nerve injury conditions by utilizing the model, and can provide guidance for a doctor to formulate an optimal training plan. Therefore, the invention is suitable for popularization and application.
Description
Technical Field
The invention relates to the technical field of medical instruments, in particular to a method for realizing an ear stimulation training device based on machine learning.
Background
Ear acupuncture therapy (ear acupuncture) is also called ear acupuncture. The method is a treatment method for treating diseases by stimulating specific regions and acupuncture points of ears by methods such as acupuncture and the like on the basis of Chinese traditional acupuncture and combining with the bovine holographic theory law. After years of practice, the electric acupuncture has been applied to ear acupuncture for treating diseases in recent years, more experience is accumulated on the positioning, the adaptation range and the stimulation method of ear acupuncture points, the ear acupuncture has become one of the commonly used treatment methods of some clinicians, and the electric acupuncture achieves remarkable curative effect on treating a large number of children patients. The ear-needle system has wide therapeutic range, simple and convenient syndrome differentiation and acupoint selection, and is scientific. The ear needle treatment can obtain the curative effect, is easy to operate and is easily accepted by patients and families.
In addition, direct Current Stimulation (DCS) is a non-invasive technique for regulating neuronal activity using constant, low-intensity dc electrical potentials. Attempts to treat diseases by electricity have been made as early as the 11 th century, and the technology of direct current stimulation has matured gradually with the development of knowledge. The DCS has two different electrodes and power supply battery equipment thereof, and is additionally provided with a control software for setting the output of the stimulation type. The stimulation modes include 3, namely anodal stimulation, cathodal stimulation and pseudo stimulation. Anodal stimulation generally enhances the excitability of the nerve at the stimulation site, while cathodal stimulation reduces the excitability of the nerve at the stimulation site. DCS does not cause nerve endings to discharge by suprathreshold stimulation, but rather acts by modulating the activity of the neural network.
Therefore, the existing ear stimulation training scheme is often added with direct current stimulation so as to achieve the purpose of good adjuvant therapy. However, in the existing ear stimulation training scheme, the training effect is not ideal in many times, and the expected auxiliary treatment purpose is difficult to realize finally, mainly because the lack of reference data causes the difficulty in predicting the training result or the great deviation between the prediction result and the actual training result, which brings great difficulty for a doctor to make a training plan. If the corresponding stimulation training recovery degree can be predicted under different ear nerve injury conditions, so that guidance is provided for a doctor to make an optimal training plan, the effect of ear stimulation training can be greatly promoted.
Disclosure of Invention
The invention aims to provide a method for realizing an ear stimulation training device based on machine learning, which can better predict the recovery degree of corresponding stimulation training according to different ear nerve injury conditions and provide guidance for a doctor to formulate an optimal training plan.
In order to realize the purpose, the technical scheme adopted by the invention is as follows:
the ear stimulation training device comprises a plurality of stimulation components for fixing different acupuncture points on the ear, a control device, a wireless module and a remote terminal, wherein the control device is connected with all magnetic pole components and stores ear acupuncture point map information;
the implementation method comprises the following steps:
(1) Collecting previous ear stimulation training reports, obtaining related original data, and transmitting the data to a database of a remote terminal;
(2) Assigning initial values to missing data in the database based on existing biological interpretations;
(3) Setting the initial value assigned to the missing data as the average value of the values existing in the database, then recursively applying the following equations until convergence, and finally returning the result of convergence to obtain the predicted value of the missing data:
a (s+1 )＝(1-η)f(a s )+ηa s
wherein, a (s+1 ) Representing a predicted value, and s represents the iteration number; f (a) s ) Representing a prediction obtained from a neural network for a; eta is less than 1; the function f remains fixed in each loop iteration;
(4) Establishing an ear neural network model according to the predicted values of the original data and the missing data;
(5) Using the established model to train a neural network while recording the model and evaluating performance of the prediction, wherein predictions with less uncertainty are used to generate prediction guidelines for ear stimulation training, including expected optimal training results; the generated prediction guide is used as a reference for making a training plan so as to perform stimulation training on the ear by using the control device to control the stimulation component according to the training plan.
Specifically, each stimulation component comprises an electrode plate and a patch arranged on the electrode plate; the control device is electrically connected with the electrode plate.
Further, in the step (3), η =0.6.
Compared with the prior art, the invention has the following beneficial effects:
(1) The ear stimulation training device is provided with a set of ear stimulation training device, a corresponding neural network model is established in a machine learning mode, and the stimulation training recovery degrees under different ear nerve injury conditions are predicted by using the model, so that guidance is provided for a doctor to formulate an optimal training plan, and then the ear stimulation training can be safely performed on the ear of a patient for a long time according to the formulation of the optimal training plan and the application of the ear stimulation training device, and a predictable training effect is obtained.
(2) The invention is based on the effective formulation of the best training plan, make the training device designed have compare traditional training device structure simpler, the cost is cheaper, and portable advantage, and the sticking piece can be changed, after formulating the best training plan, transfer the training plan to the controlling device to store, can utilize the controlling device to control some or all stimulating parts automatically subsequently, thus give different electrode stimulating sequence, different acupuncture point combination and different duration of stimulation to the corresponding acupuncture point on the ear, realize the ordered stimulating training, finish the auxiliary treatment, the invention is not merely easy and fast to operate, and very flexible in using, have really realized the effective combination of the software and hardware on the cost balance.
(3) When missing data processing is carried out, the invention finally returns a convergence result instead of f (a) s ) On one hand, the convergence result simultaneously contains data internal information and a data external machine learning feedback result, which is equivalent to jointly predicting by using the correlation inside the data and the result of external neural network learning; on the other hand, the parameter η being less than 1 can prevent the prediction from fluctuating. Therefore, the invention can utilize all information in the database, and provides guarantee for obtaining more reliable models and improving the prediction quality.
Drawings
Fig. 1 is a schematic structural view of an ear stimulation training device of the present invention.
Fig. 2 is a schematic flow chart of the implementation of the present invention.
Wherein, the part names corresponding to the reference numbers are:
1-a sticking block, 2-an electrode plate, 3-a control device, 4-a wireless module and 5-a remote terminal.
Detailed Description
The present invention will be further described with reference to the following description and examples, including but not limited to the following examples.
Examples
The invention provides an ear stimulation training device based on machine learning, which comprises a plurality of stimulation components, a control device 3 connected with all the stimulation components, a wireless module 4 arranged on the control device 3, and a remote terminal 5 connected with the wireless module 4, as shown in fig. 1. In this embodiment, the stimulation component is used for fixing on different acupuncture points of the ear, and includes electrode plate 2 and patch 1 arranged on electrode plate 2, and the mode of direct current stimulation is adopted to carry out stimulation training on the ear acupuncture points, and after using up, the patch can be dismantled, and new patch is changed, and then can be reused.
The training device is used as a hardware part of ear stimulation training, and a software control part of the invention is introduced below, which continuously collects original data in a machine learning mode, completes the original data by processing missing data in the original data, further generates a training model and continuously performs deep learning, and finally predicts stimulation training recovery degrees under different ear nerve injury conditions by using the model, thereby providing guidance for a doctor to formulate an optimal training plan.
As shown in fig. 2, the implementation process of the present invention is as follows:
1. database establishment
Published reports on the training situation and results of the ear stimulation are collected, raw data are obtained and classified as input and output of the data. The inputs for the raw data include condition variables such as age, gender, BMI, degree of ear nerve damage, and the like. The output of the data will be the training results, measured by the ear sensation score.
2. Preprocessing of raw data
Not all reports, the original data are complete and this will lead to "loss" of input data, the present invention makes preliminary guesses of these values based on the underlying assumptions, based on the existing biological interpretation: some initial values are assigned to missing data in the database as preliminary predicted missing values. These initial values will serve as starting points for iterative machine learning to be performed later.
3. Missing data processing
It is pointed out that due to the differences in the data collection method and the training scheme, the database may contain entries with incomplete input information, and if the results of studies with acceptable differences in design and purpose are collected to form the database, the probability of missing data is even higher.
The idea behind the present invention is that for any unknown attribute, the initial value assigned to the missing data is first set to the average of the values present in the data set. The predicted value of the missing data is obtained by estimating all values of the neural network, then recursively applying the following equations until convergence, and finally returning the result of convergence:
a (s+1) ＝(1-η)f(a s )+ηa s
wherein, a (s+1) Representing a predicted value, and s represents the iteration times; f (a) s ) Representing a prediction obtained from a neural network for a; eta is 0.6; the function f remains fixed for each iteration of the loop.
4. Model development
And after preliminary prediction and processing are carried out on the lost data, a corresponding neural network model is established based on the basis.
5. Generating predictive guidelines for ear stimulation training
The established model is used for training a neural network, and meanwhile, the model is recorded and the performance of prediction is evaluated; then, the overall performance of the prediction is obtained from the performance of all the different models, wherein the prediction with greater uncertainty will guide how to supplement the database with additional data of a particular input type; the smaller uncertainty predictions will be used to generate a series of quantitative guidelines for training on ear stimulation, the guidelines including: expected best training results for a particular patient, a training program that achieves the best results.
6. Making an optimal training plan and performing stimulation training by a training device
The generated prediction guide is used as a reference for a doctor to make a training plan, the made training plan is transmitted to the control device by the remote terminal through the wireless module to be stored, then the patient automatically fixes the stimulation components to the ear according to the ear acupoint map displayed on the control device, the control device is started, the control device automatically controls part or all of the stimulation components to work according to the training plan, so that corresponding acupuncture points on the foot are stimulated by different electrode stimulation sequences, different acupuncture point combinations and different time lengths, and finally stimulation training is realized on the ear in order.
The invention adopts a machine learning mode to establish a corresponding neural network model, and utilizes the model to predict the stimulation training recovery degree under different ear nerve injuries, thereby providing guidance for a doctor to formulate an optimal training plan, and then can safely and long-term use the training device to stimulate and train the ears of a patient according to the formulation of the optimal training plan, and better obtain the expected training effect. Therefore, the invention can greatly improve the effect of ear stimulation training and provide reliable auxiliary training for ear nerve injury treatment.
The above-mentioned embodiment is only one of the preferred embodiments of the present invention, and should not be used to limit the scope of the present invention, but any insubstantial modifications or changes made in the spirit and the spirit of the main design of the present invention, which still solves the technical problems consistent with the present invention, should be included in the scope of the present invention.
Claims (3)
1. The ear stimulation training device based on machine learning is characterized by comprising a plurality of stimulation components for fixing different acupuncture points on the ear, a control device (3) which is connected with all the stimulation components and stores ear acupuncture point map information, a wireless module (4) arranged on the control device (3), and a remote terminal (5) connected with the wireless module (4);
the implementation method of the ear stimulation training device based on machine learning comprises the following steps:
a. collecting previous ear stimulation training reports, obtaining original data, classifying the original data into data input and data output, and then establishing a database;
b. assigning initial values to missing data in the database based on existing biological interpretations;
c. setting the initial value assigned to the missing data as the average value of the values existing in the database, then recursively applying the following equations until convergence, and finally returning the result of convergence to obtain the predicted value of the missing data:
a (s+1 )＝(1-η)f(a s )+ηa s
wherein, a (s+1 ) Representing a predicted value, and s represents the iteration times; f (a) s ) Representing a value obtained from a neural networkPredicting; eta is a constant and the value is less than 1; the function f remains fixed in each loop iteration;
d. establishing an ear neural network model according to the predicted values of the original data and the missing data;
e. using the established model to train a neural network while recording the model and evaluating performance of the prediction, wherein predictions with less uncertainty are used to generate prediction guidelines for ear stimulation training, including expected optimal training results; the generated prediction guide is used as a reference for making a training plan;
the wireless module (4) is used for sending the formulated training plan to the control device (3);
the control device (3) is used for controlling the stimulation component to work according to the training plan.
2. The machine-learning based ear stimulation training device according to claim 1, characterized in that each stimulation component comprises an electrode pad (2) and a patch (1) arranged on the electrode pad (2); the control device (3) is electrically connected with the electrode plate (2).
3. The machine-learning based ear stimulation training device of claim 1 or 2, wherein η =0.6.
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| CN102133139B (en) * | 2011-01-21 | 2013-05-15 | 华南理工大学 | Artificial hand control system and method |
| JP2018068752A (en) * | 2016-10-31 | 2018-05-10 | 株式会社Ｐｒｅｆｅｒｒｅｄ Ｎｅｔｗｏｒｋｓ | Machine learning device, machine learning method and program |
| EP3541279B1 (en) * | 2016-11-15 | 2023-09-06 | The Regents of the University of California | Apparatuses for improving peripheral nerve function |
| CN211050139U (en) * | 2019-11-19 | 2020-07-21 | 四川大学华西医院 | Hand nerve rehabilitation training device based on target regulation |
| CN110693691B (en) * | 2019-11-19 | 2022-03-01 | 四川大学华西医院 | Limb distal nerve repair system based on nerve targeted regulation and control and implementation method thereof |
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