CN118454106B - Processing method and system for intelligent wearable device to reduce upper limb tremor - Google Patents

Abstract

The present application provides a processing method and system for a smart wearable device for alleviating upper limb tremor. The method includes: the smart wearable device collects the user's tremor characteristics and sends them to a first server. The first server determines the target method for alleviating the user's tremor based on the tremor amplitude and a pre-set amplitude threshold. If the target method is a nerve stimulation method, the first server analyzes and predicts the tremor characteristics based on the tremor characteristics using a pre-trained tremor suppression model, obtains suppression parameters, and sends them to the smart wearable device. Through the above method, upper limb tremor of Parkinson's patients can be suppressed, which not only helps patients recover physically but also provides convenience for their lives.

Description

Processing method and system for intelligent wearable device for relieving tremor of upper limbs

Technical Field

The application relates to the technical field of health care, in particular to a processing method and a processing system for intelligent wearable equipment for relieving tremor of upper limbs.

Background

Parkinson's Disease (PD), also known as parkinsonism syndrome. Parkinsonism is a chronic degenerative disorder of the central nervous system. Parkinson's disease patients often have varying degrees of dyskinesia symptoms, clinically characterized by resting tremor, bradykinesia, myotonia, and postural gait disturbances. However, the above characteristics make patients unable to care for themselves normally in daily life, so that the cure means of parkinson's disease is a main research goal.

At present, the treatment of the parkinsonism is divided into drug treatment and physical treatment, wherein the drug treatment is a currently mainstream treatment mode, but the risks of misdiagnosis and drug side effects exist at the same time, and the physical treatment is a good-looking method, but lacks a proper auxiliary tool to help a patient to perform better treatment and can persist for a long time.

In view of the above, how to inhibit tremor of the upper limbs of parkinson's patients is a technical problem that needs to be solved in the art.

Disclosure of Invention

The application provides a processing method and a processing system for intelligent wearable equipment for relieving upper limb tremors, which are used for solving the problem of how to inhibit the upper limb tremors of parkinsonism patients.

In a first aspect, the application provides a processing method for an intelligent wearable device for relieving tremor of an upper limb, which is applied to a tremor suppression system, wherein the tremor suppression system comprises the intelligent wearable device, a first server, a second server, a monitoring device and terminal equipment of a user, the intelligent wearable device, the first server, the second server and the monitoring device are in communication connection, and the intelligent wearable device, the monitoring device and the first server are in communication connection with the terminal equipment, and the method comprises the following steps:

the intelligent wearable equipment collects tremor characteristics of the user and sends the tremor characteristics to the first server, wherein the tremor characteristics comprise tremor frequency and tremor amplitude;

The first server determines a target method for reducing tremor of the user according to the tremor amplitude and a preset amplitude threshold, wherein the target method comprises a voice prompt method or a nerve stimulation method;

If the target method is a nerve stimulation method, the first server analyzes and predicts the tremor characteristics through a pre-trained tremor suppression model according to the tremor characteristics, so as to obtain suppression parameters, the suppression parameters are sent to the intelligent wearable device, the suppression parameters are used for carrying out nerve stimulation on the user to relieve limb tremor, and the tremor suppression model is obtained based on random forest model training;

and the intelligent wearable device outputs a nerve stimulation signal according to the inhibition parameter.

With reference to the first aspect, in some embodiments, the method further includes:

the monitoring equipment collects tremor change information of the user in real time and sends the tremor change information to the first server;

the first server adjusts the suppression parameters through a pre-designed closed-loop control model according to the tremor change information to obtain adjusted suppression parameters, and sends the adjusted suppression parameters to the intelligent wearable equipment;

correspondingly, the intelligent wearable device performs neural stimulation on the user according to the suppression parameter, including:

and the intelligent wearable device performs nerve stimulation on the user according to the adjusted inhibition parameters.

With reference to the first aspect, in some embodiments, the adjusting, by the first server, the suppression parameter according to the tremor change information through a pre-designed closed-loop control model, to obtain an adjusted suppression parameter includes:

The first server carries out fuzzification processing on the tremor change information to obtain fuzzification information;

the first server determines an adjustment variable according to a preset fuzzy rule and the fuzzy information, wherein the adjustment variable is used for adjusting the suppression parameter;

the first server performs defuzzification processing on the adjustment variable to obtain a defuzzified adjustment variable;

And the first server adjusts the suppression parameters according to the defuzzified adjustment variables to obtain the adjusted suppression parameters.

With reference to the first aspect, in some embodiments, the method further includes:

The second server obtains a dataset comprising tremor data and neural stimulation data for at least one user;

The second server pre-trains the data set and divides the data set into a training set and a testing set according to a preset proportion;

And training the forest model by the second server through a preset optimization algorithm according to the training set to obtain an initial tremor suppression model.

With reference to the first aspect, in some embodiments, the method further includes:

And the second server tests and adjusts the initial tremor suppression model according to the test set until the mean square deviation of the output of the adjusted initial tremor suppression model is smaller than a preset mean square deviation threshold, and then the adjusted initial tremor suppression model is determined to be the tremor suppression model.

With reference to the first aspect, in some embodiments, the method further includes:

if the target method is a voice prompt method, a first server generates voice prompt information according to the tremor characteristics and sends the voice prompt information to the intelligent wearable equipment, wherein the voice prompt information is used for prompting the user to perform gesture adjustment and motion correction;

and the intelligent wearable device carries out voice prompt on the user according to the voice prompt information.

With reference to the first aspect, in some embodiments, the first server determines a target method for reducing tremor of the user according to the tremor amplitude and a preset amplitude threshold, including:

If the tremor amplitude is less than the amplitude threshold, the first server determines the voice prompt method as the target method;

if the tremor amplitude is greater than the amplitude threshold, the first server determines the neural stimulation method as the target method.

With reference to the first aspect, in some embodiments, the smart wearable device collects tremor characteristics of the user, including:

the intelligent wearable equipment acquires sensor data in real time, wherein the sensor data is upper limb movement data of the user, which are acquired based on a preset fusion sensor;

the intelligent wearable device preprocesses the sensor data to obtain processed sensor data;

the intelligent wearable device performs feature extraction on the processed sensor data through a wavelet transformation algorithm to obtain the tremor feature.

In a second aspect, the present application provides a processing device for an intelligent wearable apparatus for alleviating tremor of an upper limb, comprising:

The first acquisition module is used for acquiring tremor characteristics of a user by the intelligent wearable equipment and sending the tremor characteristics to the first server, wherein the tremor characteristics comprise tremor frequency and tremor amplitude;

the determining module is used for determining a target method for reducing tremors of the user according to the tremors and a preset amplitude threshold value by the first server, wherein the target method comprises a voice prompt method or a nerve stimulation method;

The prediction module is used for analyzing and predicting the tremor characteristics through a pre-trained tremor suppression model according to the tremor characteristics if the target method is a nerve stimulation method, obtaining suppression parameters, and sending the suppression parameters to the intelligent wearable equipment, wherein the suppression parameters are used for carrying out nerve stimulation on the user to relieve limb tremor, and the tremor suppression model is obtained based on random forest model training;

and the output module is used for outputting a nerve stimulation signal by the intelligent wearable equipment according to the inhibition parameter.

With reference to the second aspect, in some embodiments, the apparatus further includes:

the second acquisition module is used for acquiring tremor change information of the user in real time by the monitoring equipment and sending the tremor change information to the first server;

the adjustment module is used for adjusting the suppression parameters through a pre-designed closed-loop control model according to the tremor change information by the first server to obtain adjusted suppression parameters and sending the adjusted suppression parameters to the intelligent wearable equipment;

Correspondingly, the output module comprises:

And the output unit is used for outputting a nerve stimulation signal by the intelligent wearable equipment according to the adjusted inhibition parameter.

With reference to the second aspect, in some embodiments, the adjusting module includes:

The blurring unit is used for blurring the tremor change information by the first server to obtain blurring information;

the determining unit is used for determining an adjusting variable according to a preset fuzzy rule and the fuzzy information by the first server, wherein the adjusting variable is used for adjusting the suppression parameter;

The defuzzification unit is used for performing defuzzification processing on the adjustment variable by the first server to obtain a defuzzified adjustment variable;

And the adjusting unit is used for adjusting the suppression parameters by the first server according to the defuzzified adjusting variables to obtain the adjusted suppression parameters.

With reference to the second aspect, in some embodiments, the apparatus further includes:

The acquisition module is used for acquiring a data set by the second server, wherein the data set comprises tremor data and nerve stimulation data of at least one user;

the processing module is used for preprocessing the data set by the second server and dividing the data set into a training set and a testing set according to a preset proportion;

The training module is used for training the forest model through a preset optimization algorithm by the second server according to the training set to obtain an initial tremor suppression model.

With reference to the second aspect, in some embodiments, the apparatus further includes:

The test adjustment module is used for carrying out test adjustment on the initial tremor suppression model by the second server according to the test set until the mean square error of the output of the adjusted initial tremor suppression model is smaller than a preset mean square error threshold value, and determining the adjusted initial tremor suppression model as the tremor suppression model.

With reference to the second aspect, in some embodiments, the apparatus further includes:

the generation module is used for generating voice prompt information according to the tremor characteristics and sending the voice prompt information to the intelligent wearable equipment if the target method is a voice prompt method, wherein the voice prompt information is used for prompting the user to perform gesture adjustment and motion correction;

The voice prompt module is used for the intelligent wearable device to carry out voice prompt on the user according to the voice prompt information.

With reference to the second aspect, in some embodiments, the determining module includes:

A first determining unit configured to determine the voice prompt method as the target method if the tremor amplitude is smaller than the amplitude threshold;

And a second determining unit configured to determine the neural stimulation method as the target method if the tremor amplitude is greater than the amplitude threshold.

With reference to the second aspect, in some embodiments, the first acquisition module includes:

The intelligent wearable device comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring sensor data in real time by the intelligent wearable device, and the sensor data are upper limb movement data of the user acquired based on a preset fusion sensor;

the preprocessing unit is used for preprocessing the sensor data by the intelligent wearable equipment to obtain processed sensor data;

the feature extraction unit is used for the intelligent wearable equipment to perform feature extraction on the processed sensor data through a wavelet transformation algorithm to obtain the tremor feature.

In a third aspect, the application provides an intelligent wearable device comprising a processor, a memory, a fusion sensor and a control module, wherein the memory is in communication connection with the processor;

the memory stores computer-executable instructions;

The processor executes computer-executable instructions stored by the memory to implement the processing method for an intelligent wearable device for alleviating upper limb tremor of any of the first aspect.

In a fourth aspect, the application provides a tremor control system, which comprises an intelligent wearable device, a first server, a second server, monitoring equipment and terminal equipment of a user, wherein the intelligent wearable device, the first server, the second server and the monitoring equipment are in communication connection, the intelligent wearable device, the monitoring equipment and the first server are in communication connection with the terminal equipment, and the processing method for the intelligent wearable device for alleviating tremor of the upper limbs is used for executing any one of the first aspect.

In a fifth aspect, the present application provides a computer-readable storage medium having stored therein computer-executable instructions for implementing the processing method for an intelligent wearable device for alleviating tremor of the upper limb of any one of the first aspects when executed by a processor.

In a sixth aspect, the present application provides a computer program product comprising a computer program which when executed by a processor implements the processing method for an upper limb tremor reducing smart wearable device of any of the first aspects.

According to the processing method and the processing system for the intelligent wearable device for relieving the tremor of the upper limbs, provided by the application, the intelligent wearable device collects tremor characteristics of a user and sends the tremor characteristics to the first server, the first server determines a target method for relieving tremor of the user according to tremor amplitude and a preset amplitude threshold, if the target method is a nerve stimulation method, the first server analyzes and predicts the tremor characteristics through a pre-trained tremor suppression model according to the tremor characteristics to obtain suppression parameters and sends the suppression parameters to the intelligent wearable device. Through the method, the inhibition of the tremor of the upper limbs of the parkinsonism patient is realized, and the recovery of the body of the patient is assisted, and meanwhile, the living convenience is provided for the patient.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.

Fig. 1 is an application scenario diagram of a processing method for an intelligent wearable device for alleviating upper limb tremor, provided by an embodiment of the present application;

Fig. 2 is a schematic flow chart of an embodiment of a processing method for an intelligent wearable device for alleviating tremor of an upper limb according to an embodiment of the present application;

Fig. 3 is a schematic flow chart of a second embodiment of a processing method for an intelligent wearable device for alleviating tremor of an upper limb according to an embodiment of the present application;

Fig. 4 is a schematic flow chart of a third embodiment of a processing method for an intelligent wearable device for alleviating tremor of an upper limb according to an embodiment of the present application;

fig. 5 is a schematic flow chart of a fourth embodiment of a processing method for an intelligent wearable device for alleviating tremor of an upper limb according to the embodiment of the present application;

fig. 6 is a schematic flow chart of a fifth embodiment of a processing method for an intelligent wearable device for alleviating tremor of an upper limb according to the embodiment of the present application;

fig. 7 is a schematic structural diagram of a first embodiment of a processing apparatus for an intelligent wearable device for alleviating tremor of an upper limb according to an embodiment of the present application;

fig. 8 is a schematic structural diagram of a second embodiment of a processing device for an intelligent wearable apparatus for alleviating tremor of an upper limb according to an embodiment of the present application;

Fig. 9 is a schematic structural diagram of a third embodiment of a processing apparatus for an intelligent wearable device for alleviating tremor of an upper limb according to an embodiment of the present application;

Fig. 10 is a schematic structural diagram of a fourth embodiment of a processing apparatus for an intelligent wearable device for alleviating tremor of an upper limb according to an embodiment of the present application;

fig. 11 is a schematic structural diagram of a fifth embodiment of a processing apparatus for an intelligent wearable device for alleviating tremor of an upper limb according to an embodiment of the present application;

fig. 12 is a schematic structural diagram of a sixth embodiment of a processing apparatus for an intelligent wearable device for alleviating tremor of an upper limb according to an embodiment of the present application;

fig. 13 is a schematic structural diagram of an intelligent wearable device according to an embodiment of the present application;

Fig. 14 is a schematic diagram of the architecture of the tremor control system according to the embodiment of the present application.

Specific embodiments of the present application have been shown by way of the above drawings and will be described in more detail below. The drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but rather to illustrate the inventive concepts to those skilled in the art by reference to the specific embodiments.

Detailed Description

Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.

Parkinson's Disease (PD), also known as parkinsonism syndrome. Parkinsonism is a chronic degenerative disorder of the central nervous system. Parkinson's disease patients often have varying degrees of dyskinesia symptoms, clinically characterized by resting tremor, bradykinesia, myotonia, and postural gait disturbances. However, the above characteristics make patients unable to care for themselves normally in daily life, so that the cure means of parkinson's disease is a main research goal. At present, the treatment of the parkinsonism is divided into drug treatment and physical treatment, wherein the drug treatment is a currently mainstream treatment mode, but the risks of misdiagnosis and drug side effects exist at the same time, and the physical treatment is a good-looking method, but lacks a proper auxiliary tool to help a patient to perform better treatment and can persist for a long time. Therefore, how to inhibit tremor of the upper limbs of parkinson patients is a technical problem to be solved in the art.

In view of the above, the present application provides a treatment method and system for an intelligent wearable device for alleviating upper limb tremor, thereby inhibiting upper limb tremor in parkinsonian patients. In particular, parkinson's disease has severely compromised the health of the middle-aged and elderly population. Patients with parkinson's disease often have different degrees of dyskinesia symptoms, not only affect the physical posture of the patient, but also have great negative effects on the daily life capacity and quality of life of the patient, such as reduced activity of upper limbs, physical discomfort, limited daily activity, emotional communication, cognitive impairment, and the like. The treatment of parkinson's disease is divided into medication and physical therapy, which are currently the mainstream treatment methods, but there is also a risk of misdiagnosis and side effects of medication, and physical therapy, although a seemingly better method, lacks suitable auxiliary tools to help patients to perform better treatment and can persist for a long time. In view of the problems, the inventor researches whether the tremor characteristics of the patient acquired in real time can be analyzed and predicted through the tremor suppression system so as to generate suppression parameters capable of suppressing tremor of the upper limbs of the patient, and designs intelligent wearable equipment which performs nerve stimulation on the patient according to the suppression parameters to achieve tremor suppression, so that daily life of the patient is facilitated.

Fig. 1 is an application scenario diagram of a processing method for an intelligent wearable device for alleviating upper limb tremors, where the scenario includes at least one parkinson patient and a tremor suppression system, as shown in fig. 1, where the tremor suppression system includes at least one intelligent wearable device, a first server, a monitoring device and a terminal device, the parkinson patient may wear the intelligent wearable device, the intelligent wearable device may be an intelligent glove, an intelligent bracelet, an intelligent finger ring, etc., the parkinsonism patient also holds the terminal device, may perform data communication with the intelligent wearable device and the monitoring device, the terminal device may be an electronic device such as an intelligent mobile phone, etc., a fusion sensor, for example, a gyroscope, an acceleration sensor, etc., is configured in the intelligent wearable device, tremor data of an upper limb of the parkinsonism patient may be acquired in real time, and then sent to the first server, the first server may analyze and predict tremor data according to a preconfigured tremor suppression model, thereby obtain suppression parameters and send the suppression parameters to the intelligent wearable device, and the intelligent wearable device may output neural stimulation signals according to the suppression parameters, and the neural stimulation signals may stimulate the patient to the suppression parameters, thereby implement suppression of tremors.

In one possible implementation, while the neural stimulation signal inhibits tremor of the parkinson patient, the monitoring device monitors tremor change of the parkinson patient in real time, and then sends the tremor change of the patient to the first server, and the first server can adjust the inhibition parameters according to the tremor change of the patient, so that insufficient tremor inhibition or excessive tremor inhibition is prevented, daily life of the patient is facilitated, and rehabilitation assistance can be provided for the patient.

It should be noted that, the terminal device can acquire tremor data of the parkinsonism patient and relevant records for restraining tremors in real time, so that the patient can check own health information conveniently, and the patient can adjust configuration information of the intelligent wearable device through the terminal device.

Optionally, the tremor suppression system may further include a second server, the second server may configure the tremor suppression model for the first server, and may pre-train the tremor suppression model.

The specific form of the specific entity device mentioned above is not specifically limited in this embodiment, and each device in the tremor suppression system may be an independent device, or the tremor suppression system may be configured in an intelligent wearable device or a terminal device, and other devices are modules therein.

The following describes the technical scheme of the present application and how the technical scheme of the present application solves the above technical problems in detail with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.

Fig. 2 is a schematic flow chart of an embodiment of a processing method for an intelligent wearable device for alleviating tremor of an upper limb, which is provided by the embodiment of the application, and the method is applied to a tremor suppression system, wherein the tremor suppression system comprises an intelligent wearable device, a first server, a second server, a monitoring device and a terminal device of a user, the intelligent wearable device, the first server, the second server and the monitoring device are in communication connection, and the intelligent wearable device, the monitoring device and the first server are in communication connection with the terminal device, and the method specifically comprises the following steps:

And S201, the intelligent wearable equipment collects tremor characteristics of the user and sends the tremor characteristics to the first server.

In this step, in order to help parkinsonian patients get rid of the inconvenience of life caused by tremors of upper limbs, the tremors of the users can be restrained by some methods, analysis of tremors of the users is needed, and tremors of the users can be acquired in real time through intelligent wearable equipment worn by the users and then sent to the first server, wherein the tremors comprise tremors frequency and tremors amplitude.

Specifically, the intelligent wearable device acquires sensor data in real time, then pre-processes the sensor data to obtain processed sensor data, and finally performs feature extraction on the processed sensor data through a wavelet transformation algorithm to obtain tremor features.

S202, the first server determines a target method for reducing tremor of the user according to tremor amplitude and a preset amplitude threshold.

In this step, after receiving the tremor feature sent by the smart wearable device, in order to be able to suppress tremor of the user, the first server determines a target method for reducing tremor of the user according to the tremor amplitude and a preset amplitude threshold, where the target method includes a voice prompt method or a neural stimulation method.

Specifically, the tremor amplitude represents the tremor degree of the user, and if the tremor degree of the user is light, that is, the tremor amplitude is smaller than the amplitude threshold, it indicates that the symptom of the user is light, and the user can further inhibit the tremor by adjusting the movement form or posture of the user, so that the voice prompt method is determined as the target method.

If the tremor degree of the user is heavy, that is, the tremor amplitude is larger than the amplitude threshold, which indicates that the symptom of the user is serious, the user cannot realize the suppression of tremor by adjusting the movement form or posture of the user, and then the nerve stimulation method is determined as a target method.

And S203, if the target method is a nerve stimulation method, the first server analyzes and predicts the tremor characteristics through a pre-trained tremor suppression model according to the tremor characteristics, obtains suppression parameters and sends the suppression parameters to the intelligent wearable equipment.

In the step, if the user cannot realize the suppression of tremors by adjusting the movement form or posture of the user, namely, the target method is a nerve stimulation method, the first server analyzes and predicts tremor characteristics through a pre-trained tremor suppression model to obtain suppression parameters, and sends the suppression parameters to the intelligent wearable equipment. The inhibition parameters are used for carrying out nerve stimulation on the user to relieve limb tremors, and the tremor inhibition model is obtained based on random forest model training.

And S204, the intelligent wearable equipment outputs a nerve stimulation signal according to the inhibition parameter.

In this step, after obtaining the suppression parameter, the intelligent wearable device receives the suppression parameter sent by the first server, and generates a neural stimulation signal capable of performing neural stimulation on the user through analysis of the suppression parameter.

Optionally, the intelligent wearable device may perform neural stimulation on the user according to the neural stimulation signal, thereby implementing suppression of tremors.

Optionally, the method further comprises:

S205, if the target method is a voice prompt method, the first server generates voice prompt information according to tremor characteristics and sends the voice prompt information to the intelligent wearable equipment.

In this step, if the tremble amplitude of the user is smaller, the tremble can be suppressed by adjusting the movement form or posture of the user, that is, the target method is a voice prompt method, so that the first server generates voice prompt information according to the tremble characteristics and sends the voice prompt information to the intelligent wearable device, wherein the voice prompt information is used for prompting the user to adjust the posture and correct the movement.

S206, the intelligent wearable device prompts the voice of the user according to the voice prompt information.

In this step, after the intelligent wearable device receives the voice prompt information, the user can be voice-prompted according to the voice prompt information, so as to inhibit tremors.

For example, the smart wearable device may issue a vibration or audible cue that alerts the patient to adjust posture or perform a particular exercise. These cues can help patients alleviate the discomfort caused by tremors and help them better control their own symptoms.

According to the processing method for the intelligent wearable device for relieving the tremor of the upper limbs, provided by the embodiment, the intelligent wearable device collects tremor characteristics of a user and sends the tremor characteristics to the first server, the first server determines a target method for relieving tremor of the user according to tremor amplitude and a preset amplitude threshold, if the target method is a nerve stimulation method, the first server analyzes and predicts the tremor characteristics through a pre-trained tremor suppression model according to the tremor characteristics to obtain suppression parameters and sends the suppression parameters to the intelligent wearable device. Through the method, the inhibition of the tremor of the upper limbs of the parkinsonism patient is realized, and the recovery of the body of the patient is assisted, and meanwhile, the living convenience is provided for the patient.

Fig. 3 is a schematic flow chart of a second embodiment of a processing method for an intelligent wearable device for alleviating tremor of an upper limb, provided in an embodiment of the present application, as shown in fig. 3, where based on the foregoing embodiment, the processing method specifically includes:

And S301, the monitoring equipment acquires tremor change information of the user in real time and sends the tremor change information to the first server.

In this step, based on the above embodiment, after the intelligent wearable device generates the neural stimulation signal, the upper limb tremor of the patient user is inhibited by the neural stimulation signal, so that tremor information of the patient user changes, and the monitoring device monitors tremor change of the patient user in real time, collects tremor change information of the user in real time, and sends the tremor change information to the first server.

Specifically, the monitoring device is configured with a sensor, tremor change information is collected through the sensor, and in order to accurately monitor tremor change of a patient user, the sensor with higher sensitivity is required to be configured.

S302, the first server adjusts the inhibition parameters according to tremor change information through a pre-designed closed-loop control model to obtain adjusted inhibition parameters, and sends the adjusted inhibition parameters to the intelligent wearable equipment.

In this step, after the tremor change information of the patient user is obtained, in order to accurately inhibit tremor of the upper limb of the patient user, insufficient inhibition or excessive inhibition is avoided, the generated inhibition parameters are adjusted through a pre-designed closed-loop control model, and then the adjusted inhibition parameters are generated and sent to the intelligent wearable device.

Specifically, firstly, blurring processing is carried out on tremble change information to obtain blurring information, then, according to preset blurring rules and blurring information, an adjusting variable is determined, then, defuzzifying processing is carried out on the adjusting variable to obtain a defuzzified adjusting variable, and finally, according to the defuzzified adjusting variable, the suppression parameter is adjusted to obtain the adjusted suppression parameter.

Correspondingly, after the intelligent wearable device receives the adjusted suppression parameter sent by the first server, step S204 includes:

and S303, the intelligent wearable equipment outputs a nerve stimulation signal according to the adjusted suppression parameter.

In the step, after the suppression parameters are adjusted through the closed-loop control model, the tremor of the upper limbs of the patient user can be better suppressed, and excessive suppression is avoided. And then the intelligent wearable equipment outputs a nerve stimulation signal according to the adjusted inhibition parameter.

According to the processing method for the intelligent wearable device for relieving the tremor of the upper limbs, provided by the embodiment, the monitoring device collects tremor change information of a user in real time and sends the tremor change information to the first server, the first server adjusts the inhibition parameters according to the tremor change information through the pre-designed closed-loop control model to obtain the adjusted inhibition parameters and sends the adjusted inhibition parameters to the intelligent wearable device, and the wearable device can output nerve stimulation signals according to the adjusted inhibition parameters. Through the method, the inhibition parameters are dynamically adjusted, so that the tremor of the upper limbs of the patient is well inhibited, and convenience is brought to the daily life of the patient.

Fig. 4 is a schematic flow chart of a third embodiment of a processing method for an intelligent wearable device for alleviating tremor of an upper limb, provided in an embodiment of the present application, as shown in fig. 4, on the basis of the foregoing embodiments, step S302 specifically includes:

s401, the first server carries out blurring processing on tremor change information to obtain blurring information.

In this step, after the monitoring device sends the tremor change information to the first server, in order to avoid excessive or insufficient tremor suppression of the user, adaptive adjustment of the suppression parameters is required, and then optimization adjustment can be performed through a pre-designed closed-loop control model, and preferably, blurring processing is required to be performed on the tremor change information to obtain blurring information.

Specifically, the tremor change information is subjected to blurring treatment, that is, specific numerical values are converted into blurring concepts, such as "low", "medium", "high", and the like.

S402, the first server determines an adjustment variable according to a preset fuzzy rule and fuzzy information.

In this step, after the blurring information is obtained, the suppression parameter can be adjusted according to the blurring information to obtain an adjustment variable.

Specifically, fuzzy rules are preset, which describe the relationship between the input variable and the output variable. For example, the input variable may be the frequency and amplitude of tremors and the output variable may be the amount of adjustment of the suppression parameter. Based on the defined fuzzy rule, fuzzy reasoning is carried out, and the fuzzy value of the output variable, namely the adjustment variable, is deduced according to the fuzzy information. Typically implemented using fuzzy logic operations such as fuzzy and, fuzzy or the like.

And S403, the first server performs defuzzification processing on the adjustment variable to obtain the defuzzified adjustment variable.

And S404, the first server adjusts the suppression parameters according to the defuzzified adjustment variables to obtain the adjusted suppression parameters.

After the adjustment variable is obtained, in order to adjust the suppression parameter according to the adjustment variable, the adjustment parameter needs to be defuzzified, and the suppression parameter is adjusted by the defuzzified adjustment variable, so that the adjusted suppression parameter is obtained.

Optionally, the specific implementation method for converting the adjustment variable obtained by fuzzy reasoning into the specific parameter adjustment quantity comprises the following steps:

In the first way, the membership functions of the tuning variables are converted into a standard digital form. Membership functions describe the distribution of fuzzy output values over the range of values of the tuning variables, typically expressed in the form of triangles, trapezoids, gaussian curves, or the like.

In the second mode, the product of the fuzzy output value under each membership function and the corresponding value point is calculated, and then the product values are weighted and averaged to obtain a weighted average value as the final output.

It should be noted that, the specific embodiment of the defuzzification is not limited in particular.

According to the processing method for the intelligent wearable equipment for relieving the tremor of the upper limbs, the first server carries out fuzzification processing on tremor change information to obtain fuzzification information, then determines an adjustment variable according to a preset fuzzification rule and the fuzzification information, further carries out defuzzification processing on the adjustment variable to obtain a defuzzified adjustment variable, and finally adjusts the suppression parameter according to the defuzzified adjustment variable to obtain the adjusted suppression parameter. The suppression parameters are adjusted through fuzzy control by designing a closed-loop control model, so that the suppression parameters can be more accurately used for suppressing the tremor of the upper limbs of the patient.

Fig. 5 is a schematic flow chart of a fourth embodiment of a processing method for an intelligent wearable device for alleviating tremor of an upper limb, provided in an embodiment of the present application, as shown in fig. 5, and on the basis of the foregoing embodiments, the method includes:

s501, the second server acquires the data set.

In the step, in order to obtain a model capable of accurately predicting the suppression parameters, an adaptive basic model is required to be selected for training. Training is required based on the relevant data of the user.

Specifically, the second server is used for training the model, and then the second server acquires a data set through the terminal equipment of the user, wherein the data set comprises tremor data and nerve stimulation data of at least one user.

Alternatively, the tremor data may include tremor data of a parkinsonism patient and associated physiological signal data, such as electromyography, accelerometer data, and the like.

S502, the second server preprocesses the data set and divides the data set into a training set and a testing set according to a preset proportion.

In this step, after the data set is obtained, the second server needs to pre-process the data in the data set in advance, and perform proportion division to obtain a training set and a testing set.

Specifically, preprocessing includes operations such as data cleaning, denoising, feature extraction, and the like, and data division is performed according to a predetermined ratio, for example, 50% is used as a training set, and 50% is used as a test set.

And S503, training the forest model by the second server through a preset optimization algorithm according to a training set to obtain an initial tremor suppression model.

In the step, the random forest model has the characteristics of high accuracy, overfitting resistance, multi-feature processing and the like, and further has good prediction capability for inhibiting tremors of parkinsonism patients, so that the random forest model is trained through a training set, and model parameters such as a particle swarm algorithm, a genetic algorithm, an ant colony algorithm, a simulated annealing algorithm and the like can be optimized through a preset optimization algorithm in the training process.

Specifically, according to the setting and the actual effect of the suppression parameters, the training data are marked as corresponding suppression parameters. For example, the data may be divided into different categories or labels according to the intensity, frequency, etc. of the suppression parameters, and the random forest model is trained using a labeled training set, where during the training process, the random forest learns the complex relationships between the tremor data and the physiological signal data and the suppression parameters according to the input characteristics, thereby establishing an initial tremor suppression model for predicting the suppression parameters.

And S504, the second server tests and adjusts the initial tremor suppression model according to the test set until the mean square error of the output of the adjusted initial tremor suppression model is smaller than a preset mean square error threshold, and then the adjusted initial tremor suppression model is determined to be the tremor suppression model.

In the step, after the initial tremor suppression model is obtained, in order to enable the model prediction capability to be more accurate, the initial tremor model is verified and adjusted through a test set, so that the tremor suppression model is obtained.

Specifically, the test set is input into the initial tremor suppression model for analysis and prediction to obtain an output suppression parameter, the output suppression parameter and the actual parameter are subjected to mean square error calculation, if the mean square error is larger than a preset mean square error threshold, the prediction capacity of the model is not capable of meeting the user requirement, the initial tremor suppression model needs to be continuously adjusted until the mean square error is smaller than the mean square error threshold, and finally the tremor suppression model is obtained.

According to the processing method for the intelligent wearable device for relieving the tremor of the upper limbs, the second server acquires the data set, carries out pretreatment on the data set, divides the data set into the training set and the testing set according to the preset proportion, trains the forest model according to the training set through the preset optimization algorithm to obtain the initial tremor suppression model, carries out test adjustment on the initial tremor suppression model according to the testing set until the mean square deviation of the output of the adjusted initial tremor suppression model is smaller than the preset mean square deviation threshold, and then determines the adjusted initial tremor suppression model as the tremor suppression model. By training the random forest model, the suppression parameters of the patient user can be accurately predicted, and the suppression of the tremor of the upper limb of the patient user can be realized through the suppression parameters.

Fig. 6 is a schematic flow chart of a fifth embodiment of a processing method for an intelligent wearable device for alleviating tremor of an upper limb, as shown in fig. 6, and step S201 specifically includes, based on the foregoing embodiments:

S601, the intelligent wearable device acquires sensor data in real time.

In this step, in order to be able to accurately restrain the tremor of the upper limbs of the patient user, thereby ensuring that the patient can live normally, and assisting in the rehabilitation of the patient, the tremor information of the user needs to be acquired in real time.

Specifically, the intelligent wearable device is provided with a sensor, and further sensor data are acquired in real time through the sensor, wherein the sensor data are upper limb movement data of a user acquired based on a preset fusion sensor, and tremor information of the user is contained in the upper limb movement data.

It should be noted that, the fusion sensor configured by the smart wearable device may include an acceleration sensor, a gyroscope, and the like.

S602, the intelligent wearable equipment preprocesses the sensor data to obtain processed sensor data.

And S603, the intelligent wearable equipment performs feature extraction on the processed sensor data through a wavelet transformation algorithm to obtain tremor features.

After the sensor data are obtained, in order to ensure the accuracy of the data, the sensor data are preprocessed, for example, duplicate data, missing data and abnormal data are removed, normalization processing is further performed, so that processed sensor data are obtained, and in order to obtain accurate tremor data, feature extraction is performed on the processed sensor data through wavelet transformation, tremor features are obtained, wherein the tremor features comprise tremor frequency and tremor amplitude.

Specifically, for the feature extraction process, a wavelet basis function, such as Haar wavelet, daubechies wavelet, morlet wavelet, etc., is selected first, and then the processed sensor data is subjected to signal decomposition through the selected wavelet basis function to obtain wavelet coefficients under different scales, so that tremble features are extracted from the wavelet coefficients.

It should be noted that, the specific type of the wavelet base function is not specifically limited in this embodiment, and may be selected according to the actual scenario.

According to the processing method for the intelligent wearable device for relieving the tremor of the upper limbs, the intelligent wearable device acquires sensor data in real time, then pre-processes the sensor data to obtain processed sensor data, and finally performs feature extraction on the processed sensor data through a wavelet transformation algorithm to obtain tremor features. Through the processing and feature extraction of the sensor data acquired by the patient user, the tremor condition of the patient user is accurately determined, and an accurate and powerful data basis is provided for the subsequent tremor suppression of the patient user.

Optionally, when designing the closed-loop control model, a neuron model may be further introduced, and then a simulation experiment is performed by using the neuron model, so as to evaluate the influence of different inhibition parameters on the neuron activity of the user, and further predict the influence of the inhibition parameters on tremor of the user, and instruct the closed-loop control model to adjust the inhibition parameters based on the result of the neuron model. By simulating the change of neuron activity under different parameters, the parameter adjustment strategy of the closed-loop control model can be optimized, so that the inhibition effect is improved.

Specifically, a neuron model is first built, and commonly used models include a biologically reasonable neuron model, such as a Hodgkin-Huxley model or a simplified neuron model, such as an Izhikevich model, and parameters of the model are adjusted according to clinical data and neurological characteristics of a user so as to ensure that the neuron model accurately reflects the neuron activity state of a patient. A neuronal network is constructed using the selected neuronal model to simulate neuronal activity in the brain of a parkinson's disease patient. And (3) running the neural network simulation, and observing simulation results, including discharge modes, synaptic transmission and the like of the neurons. Based on the simulation result of the neuron model, the parameters of the closed-loop control model are adjusted and optimized.

Fig. 7 is a schematic structural diagram of a first embodiment of a processing apparatus for an intelligent wearable device for alleviating upper limb tremor, as shown in fig. 7, a processing apparatus 700 for an intelligent wearable device for alleviating upper limb tremor, including:

The first collection module 701 is configured to collect tremor characteristics of a user by using the intelligent wearable device, and send the tremor characteristics to the first server, where the tremor characteristics include tremor frequency and tremor amplitude.

The determining module 702 is configured to determine, by the first server, a target method for alleviating tremors of the user according to the tremor amplitude and a preset amplitude threshold, where the target method includes a voice prompt method or a neural stimulation method.

The prediction module 703 is configured to, if the target method is a neural stimulation method, perform analysis and prediction on the tremor characteristics by using a pre-trained tremor suppression model according to the tremor characteristics, obtain suppression parameters, and send the suppression parameters to the intelligent wearable device, where the suppression parameters are used to perform neural stimulation on a user to reduce limb tremor, and the tremor suppression model is obtained based on random forest model training.

And the output module 704 is used for outputting the nerve stimulation signal according to the inhibition parameter by the intelligent wearable device.

Optionally, the processing device 700 for reducing upper limb tremor of the intelligent wearable apparatus further includes:

The generating module 705 is configured to generate, by using the first server, a voice prompt according to the tremor feature if the target method is a voice prompt method, and send the voice prompt to the intelligent wearable device, where the voice prompt is used to prompt the user to perform gesture adjustment and motion correction.

The voice prompt module 706 is configured to prompt the user with voice according to the voice prompt information.

Fig. 8 is a schematic structural diagram of a second embodiment of a processing apparatus for an intelligent wearable device for alleviating upper limb tremor, provided in an embodiment of the present application, as shown in fig. 8, the processing apparatus 700 for alleviating upper limb tremor of the intelligent wearable device further includes:

the second acquisition module 801 is configured to acquire tremor change information of the user in real time by using the monitoring device, and send the tremor change information to the first server.

The adjustment module 802 is configured to adjust the suppression parameter by using a pre-designed closed-loop control model according to the tremor change information, obtain an adjusted suppression parameter, and send the adjusted suppression parameter to the intelligent wearable device.

Accordingly, the output module 704 includes:

and the output unit is used for outputting the nerve stimulation signal by the intelligent wearable equipment according to the adjusted inhibition parameters.

Fig. 9 is a schematic structural diagram of a third embodiment of a processing apparatus for an intelligent wearable device for alleviating tremor of an upper limb, provided in an embodiment of the present application, as shown in fig. 9, an adjustment module 802, including:

And the blurring unit 901 is used for blurring the tremor change information by the first server to obtain blurring information.

The determining unit 902 is configured to determine, by using the first server, an adjustment variable according to a preset fuzzy rule and fuzzy information, where the adjustment variable is used to adjust the suppression parameter.

The defuzzification unit 903 is configured to perform defuzzification processing on the adjustment variable by using the first server, so as to obtain a defuzzified adjustment variable.

The adjusting unit 904 is configured to adjust the suppression parameter according to the defuzzified adjustment variable by the first server, so as to obtain an adjusted suppression parameter.

Fig. 10 is a schematic structural diagram of a fourth embodiment of a processing apparatus for an intelligent wearable device for alleviating upper limb tremor, provided in an embodiment of the present application, as shown in fig. 10, a processing apparatus 700 for an intelligent wearable device for alleviating upper limb tremor, further including:

an acquisition module 1001 for a second server to acquire a dataset comprising tremor data and neural stimulation data of at least one user.

The processing module 1002 is configured to preprocess the data set by using a second server, and divide the data set into a training set and a testing set according to a preset proportion.

The training module 1003 is configured to train the forest model according to the training set by using the second server through a preset optimization algorithm, so as to obtain an initial tremor suppression model.

The test adjustment module 1004 is configured to perform test adjustment on the initial tremor suppression model by using the second server according to the test set until a mean square error of an output of the adjusted initial tremor suppression model is less than a preset mean square error threshold, and determine the adjusted initial tremor suppression model as a tremor suppression model.

Fig. 11 is a schematic structural diagram of a fifth embodiment of a processing apparatus for an intelligent wearable device for alleviating tremor of an upper limb, where, as shown in fig. 11, a determining module 702 includes:

The first determining unit 1101 is configured to determine, by the first server, a voice prompt method as a target method if the tremor amplitude is smaller than the amplitude threshold.

The second determining unit 1102 is configured to determine, by the first server, the neural stimulation method as the target method if the tremor amplitude is greater than the amplitude threshold.

Fig. 12 is a schematic structural diagram of a sixth embodiment of a processing apparatus for an intelligent wearable device for alleviating tremor of an upper limb, provided in an embodiment of the present application, as shown in fig. 12, a first acquisition module 701 includes:

The acquiring unit 1201 is configured to acquire sensor data in real time by using the intelligent wearable device, where the sensor data is upper limb movement data of the user acquired by using a preset fusion sensor.

The preprocessing unit 1202 is configured to preprocess the sensor data by using the intelligent wearable device, so as to obtain processed sensor data.

The feature extraction unit 1203 is configured to perform feature extraction on the processed sensor data by using the intelligent wearable device through a wavelet transformation algorithm, so as to obtain tremor features.

The processing device for alleviating the intelligent wearable device for tremor of the upper limb provided by the above embodiments is used for executing the processing method for alleviating the intelligent wearable device for tremor of the upper limb in any of the above method embodiments, and its implementation principle and technical effect are similar, and are not repeated here.

Fig. 13 is a schematic structural diagram of an intelligent wearable device provided by an embodiment of the present application, where the intelligent wearable device 1300 includes a processor 1302, a memory 1301 communicatively connected to the processor 1302, and a fusion sensor 1303;

memory 1301 stores computer-executable instructions.

Processor 1302 executes computer-executable instructions stored in memory 1301 to implement the processing method for a smart wearable device for alleviating upper limb tremor in any of the method embodiments described above.

And the fusion sensor 1303 is used for collecting tremor characteristics and tremor change information of the user in real time.

Fig. 14 is a schematic diagram of an architecture of a tremor control system 1400 provided in an embodiment of the present application, including:

The intelligent wearable device 1300, the first server 1401, the second server 1402, the monitoring device 1403 and the terminal device 1404 of the user, wherein the intelligent wearable device 1300, the first server 1401, the second server 1402 and the monitoring device 1403 are in communication connection, the intelligent wearable device 1300, the monitoring device 1403 and the first server 1401 are in communication connection with the terminal device 1404, and the processing method for alleviating tremor of the upper limbs in any of the foregoing method embodiments is used for executing the processing method of the intelligent wearable device for alleviating tremor of the upper limbs, and the technical principle and the technical effect of the implementation are similar and are not repeated herein.

The embodiment of the application also provides a computer readable storage medium, wherein computer execution instructions are stored in the computer readable storage medium, and the computer execution instructions are used for realizing the processing method for the intelligent wearable device for relieving the tremor of the upper limb in any embodiment when being executed by a processor.

The computer readable storage medium described above may be implemented by any type or combination of volatile or nonvolatile memory devices such as static random access memory, electrically erasable programmable read-only memory, magnetic memory, flash memory, magnetic or optical disk. A readable storage medium can be any available medium that can be accessed by a general purpose or special purpose computer.

In the alternative, a readable storage medium is coupled to the processor such that the processor can read information from, and write information to, the readable storage medium. In the alternative, the readable storage medium may be integral to the processor. The processor and the readable storage medium may reside in an Application SPECIFIC INTEGRATED Circuits (ASIC). The processor and the readable storage medium may reside as discrete components in a device.

The embodiment of the application also provides a computer program product, which comprises a computer program, the computer program is stored in a computer readable storage medium, at least one processor can read the computer program from the computer readable storage medium, and the technical scheme provided by any one of the method embodiments can be realized when the at least one processor executes the computer program.

Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.

It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims (5)

1. The tremor suppression system is characterized by comprising intelligent wearable equipment, a first server, a second server, monitoring equipment and terminal equipment of a user, wherein the intelligent wearable equipment, the first server, the second server and the monitoring equipment are in communication connection, the intelligent wearable equipment, the monitoring equipment and the first server are in communication connection with the terminal equipment, and the wearable equipment is worn on the upper limb of the user of the patient;

the intelligent wearable device collects tremor characteristics of the user and sends the tremor characteristics to the first server, wherein the tremor characteristics comprise tremor frequency and tremor amplitude, and the tremor characteristics are determined based on upper limb movement data collected from the upper limbs;

the first server determines a target method for reducing tremor of the user according to the tremor amplitude and a preset amplitude threshold value, and the method comprises the following steps:

If the tremor amplitude is smaller than the amplitude threshold, the first server determines a voice prompt method as the target method;

if the tremor amplitude is greater than the amplitude threshold, the first server determines a neural stimulation method as the target method;

If the target method is a neural stimulation method, the first server analyzes and predicts the tremor characteristics through a pre-trained tremor suppression model according to the tremor characteristics, so as to obtain suppression parameters, the suppression parameters are sent to the intelligent wearable device, the suppression parameters are used for carrying out neural stimulation on the user to relieve limb tremors, the tremor suppression model is obtained based on random forest model training, and the tremor suppression model is obtained in the second server in a training mode and is distributed in the first server;

the intelligent wearable device outputs a nerve stimulation signal according to the suppression parameter;

the monitoring equipment collects tremor change information of the user in real time and sends the tremor change information to the first server;

The first server adjusts the inhibition parameters according to the tremor change information through a pre-designed closed-loop control model and a pre-set neuron model, obtains the adjusted inhibition parameters and sends the adjusted inhibition parameters to the intelligent wearable equipment;

wherein the neuron model is derived based on clinical data and neurological feature adjustment parameters for modeling a state of neuron activity of a user;

correspondingly, the adjusting the suppression parameter by combining a pre-designed closed-loop control model with a pre-set neuron model to obtain an adjusted suppression parameter comprises:

Simulating the neuron activity of the user based on the neuron model to obtain a simulation result;

adjusting and optimizing parameters of the closed-loop control model based on the simulation result to obtain an adjusted closed-loop control model;

adjusting the suppression parameters based on the adjusted closed-loop control model to obtain the adjusted suppression parameters;

correspondingly, the intelligent wearable device outputs a neural stimulation signal according to the suppression parameter, including:

the intelligent wearable device outputs a nerve stimulation signal according to the adjusted suppression parameter;

the intelligent wearable device collects tremor characteristics of the user, including:

the intelligent wearable equipment acquires sensor data in real time, wherein the sensor data is upper limb movement data of the user, which are acquired based on a preset fusion sensor;

the intelligent wearable device preprocesses the sensor data to obtain processed sensor data;

the intelligent wearable device performs feature extraction on the processed sensor data through a wavelet transformation algorithm to obtain the tremor feature.

2. The system of claim 1, wherein the first server adjusts the suppression parameters according to the tremor variation information via a pre-designed closed-loop control model to obtain adjusted suppression parameters, comprising:

The first server carries out fuzzification processing on the tremor change information to obtain fuzzification information;

the first server determines an adjustment variable according to a preset fuzzy rule and the fuzzy information, wherein the adjustment variable is used for adjusting the suppression parameter;

the first server performs defuzzification processing on the adjustment variable to obtain a defuzzified adjustment variable;

And the first server adjusts the suppression parameters according to the defuzzified adjustment variables to obtain the adjusted suppression parameters.

3. The system of claim 1, wherein the system further comprises a controller configured to control the controller,

The second server obtains a dataset comprising tremor data and neural stimulation data for at least one user;

the second server preprocesses the data set and divides the data set into a training set and a testing set according to a preset proportion;

And training the forest model by the second server through a preset optimization algorithm according to the training set to obtain an initial tremor suppression model.

4. The system of claim 3, wherein the system further comprises a controller configured to control the controller,

And the second server tests and adjusts the initial tremor suppression model according to the test set until the mean square deviation of the output of the adjusted initial tremor suppression model is smaller than a preset mean square deviation threshold, and then the adjusted initial tremor suppression model is determined to be the tremor suppression model.

5. The system of claim 1, wherein the system further comprises a controller configured to control the controller,

If the target method is a voice prompt method, a first server generates voice prompt information according to the tremor characteristics and sends the voice prompt information to the intelligent wearable equipment, wherein the voice prompt information is used for prompting the user to perform gesture adjustment and motion correction;

and the intelligent wearable device carries out voice prompt on the user according to the voice prompt information.