CN114452054A - Control method and device of intelligent artificial limb, intelligent artificial limb and storage medium - Google Patents
Control method and device of intelligent artificial limb, intelligent artificial limb and storage medium Download PDFInfo
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- CN114452054A CN114452054A CN202210168583.3A CN202210168583A CN114452054A CN 114452054 A CN114452054 A CN 114452054A CN 202210168583 A CN202210168583 A CN 202210168583A CN 114452054 A CN114452054 A CN 114452054A
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
- A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
- A61F2/50—Prostheses not implantable in the body
- A61F2/68—Operating or control means
- A61F2/70—Operating or control means electrical
- A61F2/72—Bioelectric control, e.g. myoelectric
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/389—Electromyography [EMG]
Abstract
The application discloses a control method of an intelligent artificial limb, which comprises the following steps: collecting a characteristic signal of an intelligent artificial limb user, wherein the characteristic signal is an electromyographic signal or an electroencephalographic signal; determining the behavior state of the user according to the characteristic signal; and controlling the intelligent artificial limb to operate in a power consumption mode corresponding to the behavior state. The application also discloses a control device of the intelligent artificial limb, the intelligent artificial limb and a computer readable storage medium. The method and the device aim to reduce the power consumption of the intelligent artificial limb while ensuring the effective control of the user on the intelligent artificial limb.
Description
Technical Field
The present application relates to the field of intelligent prosthesis technology, and in particular, to a control method for an intelligent prosthesis, a control device for an intelligent prosthesis, and a computer-readable storage medium.
Background
Compared with the traditional artificial limb with limited activity, the intelligent artificial limb can receive electromyographic signals transmitted by human brains through limb residual muscles, and the electromyographic signals are processed by a sensor and a mechanical system which are arranged in the artificial limb arm barrel to generate corresponding actions, so that a user can make more various and more precise actions through the intelligent artificial limb, and the user can flexibly control the activity of the artificial limb conveniently.
At present, in order to ensure effective control of a user on an intelligent artificial limb and maintain normal operation of the intelligent artificial limb, the intelligent artificial limb needs to continuously acquire relevant physiological data (such as myoelectric signals) and motion data of the user, so that when the user needs to control the intelligent artificial limb to make corresponding actions, the intelligent artificial limb can quickly respond. However, this inevitably causes the intelligent artificial limb to always operate in a state of high power consumption.
The above is only for the purpose of assisting understanding of the technical solutions of the present application, and does not represent an admission that the above is prior art.
Disclosure of Invention
The present application provides a control method for an intelligent prosthesis, a control device for an intelligent prosthesis, and a computer-readable storage medium, which are intended to reduce power consumption of the intelligent prosthesis while ensuring effective control of the intelligent prosthesis by a user.
In order to achieve the above object, the present application provides a control method of an intelligent prosthesis, comprising the steps of:
collecting a characteristic signal of an intelligent artificial limb user, wherein the characteristic signal is an electromyographic signal or an electroencephalographic signal;
determining the behavior state of the user according to the characteristic signal;
and controlling the intelligent artificial limb to operate in a power consumption mode corresponding to the behavior state.
Optionally, before the step of determining the behavior state of the user according to the characteristic signal, the method further includes:
acquiring target data, wherein the target data comprises gesture characteristic data and/or current time period of the user;
the step of determining the behavior state of the user according to the characteristic signal comprises:
determining the activity of the behaviors according to the characteristic signals and the target data;
and determining the behavior state of the user according to the behavior activity, wherein the lower the behavior activity is, the lower the running power consumption corresponding to the behavior state is determined to be.
Optionally, the current time period is a preset time period in which the current time point is located, the preset time period includes a plurality of time periods, and the preset time period is obtained by analyzing big data based on the historical behavior state of the user.
Optionally, the step of determining activity according to the characteristic signal and the target data includes:
determining the activity corresponding to the characteristic signal and determining the activity corresponding to the target data;
and calculating the activity degree according to the activity degree corresponding to the characteristic signal and the activity degree corresponding to the target data.
Optionally, the step of calculating a behavior activity level according to the activity level corresponding to the characteristic signal and the activity level corresponding to the target data includes:
performing weighted summation operation according to the liveness and the weight corresponding to the characteristic signals and according to the liveness and the weight corresponding to the target data to obtain the behavior liveness;
and the weight corresponding to the characteristic signal is greater than the weight corresponding to the target data.
Optionally, the behavior state includes a sleep state, a first active state, and a second active state; wherein the content of the first and second substances,
the behavior activity degree corresponding to the sleep state is lower than the behavior activity degree corresponding to the first activity state;
and the activity degree of the behavior corresponding to the first activity state is lower than that of the behavior corresponding to the second activity state.
To achieve the above object, the present application further provides a control device for an intelligent prosthesis, including:
the intelligent artificial limb system comprises an acquisition module, a processing module and a control module, wherein the acquisition module is used for acquiring a characteristic signal of an intelligent artificial limb user, and the characteristic signal is an electromyographic signal or an electroencephalographic signal;
the processing module is used for determining the behavior state of the user according to the characteristic signal;
and the control module is used for controlling the intelligent artificial limb to operate in a power consumption mode corresponding to the behavior state.
Optionally, the acquisition module is further configured to acquire target data, where the target data includes the posture characteristic data of the user and/or a current time period;
the processing module comprises a calculation module and an analysis module;
the calculation module is used for determining the activity liveness according to the characteristic signals and the target data;
and the analysis module is used for determining the behavior state of the user according to the behavior activity, wherein the lower the behavior activity is, the lower the running power consumption corresponding to the behavior state is determined to be.
To achieve the above object, the present application also provides an intelligent prosthesis, comprising: the intelligent artificial limb control method comprises a memory, a processor and a control program of the intelligent artificial limb, wherein the control program of the intelligent artificial limb is stored on the memory and can run on the processor, and the steps of the control method of the intelligent artificial limb are realized when the control program of the intelligent artificial limb is executed by the processor.
In order to achieve the above object, the present application also provides a computer readable storage medium, on which a control program of an intelligent prosthesis is stored, which when executed by a processor implements the steps of the control method of the intelligent prosthesis as described above.
According to the control method of the intelligent artificial limb, the control device of the intelligent artificial limb, the intelligent artificial limb and the computer readable storage medium, the behavior state of the user is analyzed based on the relevant characteristic signals of the user, the use requirement of the user on the intelligent artificial limb is judged based on the relevant behavior activeness, and the intelligent artificial limb is controlled to operate in a corresponding power consumption mode according to the behavior state of the user, so that the intelligent artificial limb can operate in a corresponding low-power consumption mode in some scenes with low use requirements of the user, the operation power consumption of the intelligent artificial limb is further reduced to a certain extent, the use requirement of the user on the intelligent artificial limb can be met, and the effective control of the user on the intelligent artificial limb is ensured.
Drawings
FIG. 1 is a schematic diagram illustrating steps of a method for controlling an intelligent prosthesis according to an embodiment of the present application;
FIG. 2 is a schematic diagram illustrating steps of a method for controlling an intelligent prosthesis according to another embodiment of the present application;
FIG. 3 is a block diagram illustrating the structure of a control device of an intelligent prosthesis according to an embodiment of the present application;
fig. 4 is a schematic block diagram of the internal structure of an intelligent prosthesis according to an embodiment of the present application.
The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and intended to be illustrative of the present invention and should not be construed as limiting the present invention, and all other embodiments that can be obtained by one skilled in the art based on the embodiments of the present invention without inventive efforts shall fall within the scope of protection of the present invention.
Referring to fig. 1, in one embodiment, the control method of the intelligent prosthesis includes:
s10, collecting a characteristic signal of the intelligent prosthesis user, wherein the characteristic signal is an electromyographic signal or an electroencephalographic signal;
step S20, determining the behavior state of the user according to the characteristic signal;
and step S30, controlling the intelligent artificial limb to operate in a power consumption mode corresponding to the behavior state.
In this embodiment, the terminal in this embodiment may be an intelligent prosthesis, or may be a device, an apparatus, or the like that controls the intelligent prosthesis. The following description takes an embodiment terminal as an intelligent artificial limb as an example.
The intelligent artificial limb can be an intelligent artificial hand (such as an intelligent bionic hand, a myoelectric artificial hand and the like) and can also be an intelligent artificial leg (such as an intelligent bionic leg, a myoelectric artificial leg and the like).
As shown in step S10, the characteristic signal (or called physiological signal) may be a myoelectric signal or an electroencephalogram signal. The joint part of the intelligent artificial limb and the residual limb of the user can be called as an accepting cavity, and the intelligent artificial limb is conducted through the skin of the user on the basis of a detection electrode arranged in the accepting cavity, so that the electromyographic signals of the user can be collected; or the intelligent artificial limb is also connected with an electroencephalogram signal detection device, and when the user wears the electroencephalogram signal detection device, the intelligent artificial limb can acquire the electroencephalogram signal of the user through the electroencephalogram signal detection device.
Optionally, when the intelligent prosthesis is running, the characteristic signal of the user can be collected in a timing or real-time manner. The intelligent artificial limb can acquire characteristic signals in real time in some high-power-consumption operation modes; in some low-power-consumption operation modes, the intelligent artificial limb can acquire the characteristic signals at regular time, and in different low-power-consumption modes, the timing duration can be different (and the lower the power consumption corresponding to the operation mode is, the longer the timing duration is).
As shown in step S20, after the intelligent prosthesis collects the current characteristic signal of the user, the current behavior state of the user can be analyzed according to the activity level corresponding to the characteristic signal. Wherein the activity degree of the behavior is characterized by the activity degree of the current behavior of the user; when determining the activity level on the basis of the characteristic signal, the respective activity level can be determined as a function of the signal frequency or the potential of the characteristic signal. If the signal frequency of the set characteristic signal is higher, the behavior activity corresponding to the characteristic signal is higher.
Optionally, because the frequency or potential of the normal characteristic signal (such as an electroencephalogram signal) is not always stable and unchangeable, when the frequency or potential of the characteristic signal is determined, the mean value of the frequency or potential of the signal within a certain time duration may be calculated, and then the behavior liveness is determined by using the calculated mean value.
Optionally, the behavior states have a plurality of types, and each behavior state is associated with a corresponding behavior activity.
Optionally, the behavior state includes a sleep state, a first active state, and a second active state; each behavior state is associated with corresponding behavior activity, and the behavior activity corresponding to the sleep state is lower than the behavior activity corresponding to the first activity state; and the activity degree of the behavior corresponding to the first activity state is lower than that of the behavior corresponding to the second activity state.
It should be noted that the first active state is characterized in that the user is awake, but has infrequent activities, such as sitting still, standing, rest, etc.; the second active state is characterized as a state in which the user is awake and has more frequent activity (more frequent activity than the first active state).
Optionally, the second active state may be further divided into an action state and a motion state; wherein the action state is a more frequently active state than the first active state, such as walking, working, etc.; the exercise state is a more frequent state than the action state, such as running and high jump. Therefore, the activity level corresponding to the action state is lower than the activity level corresponding to the motion state.
Optionally, a corresponding activity interval is pre-divided for each behavior state. It should be understood that the maximum value of the activity interval corresponding to the sleep state is smaller than the minimum value of the activity interval corresponding to the first activity state; and the maximum value of the behavior activity interval corresponding to the first activity state is smaller than the minimum value of the behavior activity interval corresponding to the second activity state. The activity interval corresponding to the second activity state can be divided into an activity interval corresponding to the action state and an activity interval corresponding to the motion state, and the maximum value of the activity interval corresponding to the action state is smaller than the minimum value of the activity interval corresponding to the motion state.
Optionally, after determining the behavior activity corresponding to the current characteristic signal of the user, the intelligent prosthesis may determine a behavior activity interval in which the behavior activity corresponding to the characteristic signal is located, and use a behavior state associated with the determined behavior activity interval as the current behavior state of the user. If the behavior activity corresponding to the current characteristic signal is in the behavior activity interval associated with the sleep state, it can be determined that the user is currently in the sleep state.
Optionally, each behavior state is associated with a power consumption mode of a corresponding intelligent prosthesis, and the lower the behavior activity degree corresponding to the behavior state is, the lower the operation power consumption of the power consumption mode corresponding to the behavior state is.
Optionally, the operating power consumption of the power consumption mode corresponding to the sleep state is lower than the operating power consumption of the power consumption mode corresponding to the first active state; the operation power consumption of the power consumption mode corresponding to the first active state is lower than that of the power consumption mode corresponding to the second active state. When the second active state is further divided into an action state and a motion state, the operation power consumption of the power consumption mode corresponding to the action state is lower than that of the power consumption mode corresponding to the motion state.
After determining the current behavior state of the user, as shown in step S30, further determining a power consumption mode (which may be denoted as a target power consumption mode) corresponding to the current behavior state, and then controlling the intelligent prosthesis to operate in the determined target power consumption mode. If the current power consumption mode of the intelligent artificial limb is the target power consumption mode, the intelligent artificial limb maintains the current power consumption mode to continuously run; and if the current power consumption mode of the intelligent artificial limb is not the target power consumption mode, switching the intelligent artificial limb from the current power consumption mode to the target power consumption mode.
Optionally, the active component for driving the intelligent artificial limb to move is removed, and the main power consumption of the intelligent artificial limb is generated in the processes of sampling the user and calculating the internal components, so that the intelligent artificial limb can adjust the power consumption by adjusting the frequency, the sampling interval duration and/or the output power of the sampling component (such as a detection electrode) when the user is sampled, thereby realizing the switching between different power consumption modes. The lower the frequency during sampling and/or the output power of the sampling component is adjusted, the lower the power consumption of the intelligent artificial limb is; the longer the adjustment sampling interval duration (which may be the timing duration described above), the lower the power consumption of the intelligent prosthesis.
And/or the intelligent artificial limb can adjust the power consumption by adjusting the operation frequency, the power and the like of internal components, so as to realize the switching between different power consumption modes. The lower the operation frequency and the power for adjusting the internal components are, the lower the power consumption of the intelligent artificial limb is.
It should be noted that, when the intelligent artificial limb samples the user, besides collecting the characteristic signal, some other physiological data (such as body temperature, blood pressure, etc.) of the user may also be collected. Optionally, the intelligent artificial limb is provided with an attitude sensor, and the intelligent artificial limb can also acquire motion data of the user through the attitude sensor.
For example, the sampling frequency of the power consumption mode corresponding to the sleep state is set to be lower than the sampling frequency of the power consumption mode corresponding to the first active state; setting the operation frequency of the power consumption mode corresponding to the sleep state to be lower than the operation frequency of the power consumption mode corresponding to the first active state; setting the sampling interval duration of the power consumption mode corresponding to the first active state to be longer than the sampling interval duration of the power consumption mode corresponding to the second active state (for the motion state, the intelligent artificial limb can perform real-time sampling on the user), and the like.
It should be understood that, the lower the activity of the characteristic signal corresponding to the user behavior state is, the lower the activity degree of the behavior state can be reflected, which indicates that the user has lower use demand for the intelligent artificial limb in the corresponding behavior state, and at this time, the intelligent artificial limb can be controlled to operate in a mode with correspondingly lower power consumption (for example, the frequency of related work of the intelligent artificial limb is reduced, and a part of components and parts which are not frequently used are shut down), so as to reduce the power consumption of the intelligent artificial limb; on the contrary, if the activity liveness of the characteristic signal corresponding to the user behavior state is higher, the activity degree of the behavior state can be reflected to be higher, which indicates that the user has a higher use demand for the intelligent artificial limb in the corresponding behavior state, and then the intelligent artificial limb can be controlled to operate in a mode with correspondingly higher power consumption (such as increasing the frequency of the related work of the intelligent artificial limb, starting a part of commonly used components and parts and the like), so that the function of the intelligent artificial limb is more complete, and the intelligent artificial limb can further meet the use demand of the current behavior state of the user for the intelligent artificial limb.
Therefore, the behavior state of the user can be analyzed based on the related characteristic signals of the user, the use requirement of the user on the intelligent artificial limb is judged based on the behavior activity corresponding to the related behavior state, the intelligent artificial limb is controlled to operate in a corresponding power consumption mode according to the behavior state of the user, so that the intelligent artificial limb can operate in a corresponding high-power consumption mode in some scenes with high use requirement of the user, the effective control of the user on the intelligent artificial limb is realized, the use requirement of the user on the intelligent artificial limb is met, the intelligent artificial limb is controlled to operate in a corresponding low-power consumption mode in some scenes with low use requirement of the user on the intelligent artificial limb, the operation power consumption of the intelligent artificial limb is reduced to a certain extent, the use requirement of the user on the intelligent artificial limb can be met, and the effective control of the user on the intelligent artificial limb is ensured.
In an embodiment, as shown in fig. 2, on the basis of the embodiment shown in fig. 1, before the step of determining the behavior state of the user according to the characteristic signal, the method further includes:
step S40, acquiring target data, wherein the target data comprises the gesture characteristic data and/or the current time period of the user;
the step of determining the behavior state of the user according to the characteristic signal comprises:
step S21, determining activity according to the characteristic signal and the target data;
and step S22, determining the behavior state of the user according to the behavior activity, wherein the lower the behavior activity is, the lower the running power consumption corresponding to the behavior state is determined to be.
In this embodiment, the target data is at least one of the posture characteristic data of the user and the current time period. The intelligent artificial limb is provided with an attitude sensor, and the intelligent artificial limb can acquire attitude characteristic data of a user through the attitude sensor. The posture characteristic data can be the maintaining time length of the same posture of the user, the activity amplitude of the body in a certain time length, the switching frequency of the posture in a certain time length and the like.
Alternatively, the intelligent prosthesis may perform step S40 while performing step S10.
Optionally, when the target data includes the posture characteristic data, after the intelligent prosthesis acquires the current characteristic signal and the posture characteristic data of the user, the current activity of the user may be determined according to the characteristic signal and the posture signal.
Optionally, the signal frequency or the signal potential of each preset characteristic signal is associated with a corresponding first activity, and the higher the signal frequency or the signal potential of the preset characteristic signal is, the higher the corresponding first activity is. After the intelligent artificial limb obtains the current characteristic signal, the corresponding first activity can be determined according to the signal frequency or the potential of the characteristic signal.
It should be noted that, because the frequency or the potential of the normal characteristic signal (such as the electroencephalogram signal) is not always stable and unchanged, when determining the frequency or the potential of the characteristic signal, the average value of the frequency or the potential of the signal within a certain time period may be calculated, and then the calculated average value is used to determine the first activity.
And after the intelligent artificial limb obtains the current posture characteristic data of the user, determining a second activity according to the posture characteristic data. For example, it may be set that the longer the duration of the same gesture of the user is maintained, the lower the corresponding second activity level is; setting that the smaller the activity amplitude of the body of the user in a certain time length is, the lower the corresponding second activity is; and setting the higher the switching frequency of the user posture in a certain time length, the higher the corresponding second activity.
If the plurality of second liveness degrees are obtained through calculation according to the plurality of posture characteristic data, the average value of the plurality of second liveness degrees can be calculated, and the calculated average value is used as the finally determined second liveness degree.
Optionally, after the first activity and the second activity are obtained, the sum of the first activity and the second activity (or the average of the first activity and the second activity) is calculated to obtain the activity. Therefore, after the intelligent artificial limb obtains the activity degree, the activity degree interval where the activity degree is located can be determined, and the behavior state associated with the determined activity degree interval is used as the current behavior state of the user.
Therefore, the characteristic signal and the attitude characteristic data are adopted to jointly determine the activity liveness, so that the current behavior state of the user is judged in an auxiliary manner by combining the attitude characteristic data on the basis of utilizing the characteristic signal, the accuracy of analyzing the current behavior state of the user is improved, the intelligent artificial limb can run in a power consumption mode more suitable for the current behavior state of the user, and the running power consumption of the intelligent artificial limb is reduced as much as possible while the effective control of the user on the intelligent artificial limb is ensured.
In some embodiments, when the target data includes the posture feature data and the current period, the terminal may divide the day into a plurality of preset periods in advance, and each preset period is associated with a corresponding third activity. Wherein, the user generally has corresponding behavior states in different preset time periods.
For example, a user is generally mostly in a sleep state during a late-night period; during the evening period, the user may be in a sleep state, a first active state, or an active state; during the daytime period, the user may be in a first active state, an action state, or an active state. Thus, the third activity level for the late-night period may be set lower than the third activity level for the evening period; setting a third activity level for the evening hours lower than a third activity level for the daytime hours, and so on.
Optionally, the first activity is determined according to the current characteristic signal of the user, the second activity is determined according to the current posture characteristic data of the user, and meanwhile, the third activity of the preset time interval corresponding to the current time interval is obtained according to the current time interval in which the current time point is located.
Optionally, after the first activity, the second activity and the third activity are obtained, the sum of the first activity, the second activity and the third activity (or the average of the first activity, the second activity and the third activity) is calculated to obtain the behavior activity. Therefore, after the intelligent artificial limb obtains the activity degree, the activity degree interval where the activity degree is located can be determined, and the behavior state associated with the determined activity degree interval is used as the current behavior state of the user.
Therefore, the characteristic signal, the attitude characteristic data and the current time interval are adopted to jointly determine the activity, so that the current behavior state of the user is judged in an auxiliary manner by combining the attitude characteristic data and the current time interval on the basis of utilizing the characteristic signal, the accuracy of analyzing the current behavior state of the user is improved, the intelligent artificial limb can run in a power consumption mode more suitable for the current behavior state of the user, and the running power consumption of the intelligent artificial limb is reduced as much as possible while the effective control of the user on the intelligent artificial limb is ensured.
In some embodiments, when the target data includes a current time interval, the first activity is determined according to the current characteristic signal of the user, and meanwhile, according to the current time interval in which the current time point is located, a third activity of a preset time interval corresponding to the current time interval is obtained.
Optionally, after the first activity and the third activity are obtained, the sum of the first activity and the third activity (or the average of the first activity and the third activity) is calculated to obtain the activity. Therefore, after the intelligent artificial limb obtains the activity degree, the activity degree interval where the activity degree is located can be determined, and the behavior state associated with the determined activity degree interval is used as the current behavior state of the user.
Therefore, the characteristic signal and the current time interval are adopted to jointly determine the activity, so that the current behavior state of the user is judged in an auxiliary manner by combining the current time interval on the basis of utilizing the characteristic signal, the accuracy of analyzing the current behavior state of the user is improved, the intelligent artificial limb can run in a power consumption mode more suitable for the current behavior state of the user, and the running power consumption of the intelligent artificial limb is reduced as much as possible while the effective control of the user on the intelligent artificial limb is ensured.
In an embodiment, on the basis of the above embodiment, the current time period is a preset time period in which a current time point is located, the preset time period includes a plurality of preset time periods, and the preset time period is obtained by performing big data analysis based on the historical behavior state of the user.
In this embodiment, a big data analysis technology is used in advance to analyze a plurality of historical behavior states of a user and a time period during which the historical behavior state occurs, so as to obtain a behavior state associated with each preset time period in a plurality of preset time periods (for example, one day) of the user within a certain time duration. In this case, the behavior state that the user generates the most times or maintains the longest time in any preset time period may be used as the behavior state associated with the preset time period.
For example, if the user is generally in a sleep state in a period of 23:00 to 7:00 based on big data analysis, determining that the associated behavior state is in the sleep state in a preset period of 23:00 to 7: 00; if the user is generally in the action state in the period of 8:00-8:30 based on the big data analysis, the associated action state in the preset period of 8:00-8:30 is determined to be the action state.
It should be noted that the total duration corresponding to different preset time periods may be the same or different.
Optionally, a corresponding third activity level may be set according to the behavior state associated with each preset time period. Setting a third activity degree associated with a preset time period corresponding to the sleep state, wherein the third activity degree is lower than the third activity degree associated with the preset time period corresponding to the first activity state; and setting a third activity degree associated with a preset time period corresponding to the first active state to be lower than a third activity degree associated with a preset time period corresponding to the second active state. And the third activity degree associated with the preset time interval corresponding to the behavior state can be further set to be lower than the third activity degree associated with the preset time interval corresponding to the motion state.
Optionally, when determining the current third activity of the user, a preset time period corresponding to the current time period may be determined according to the current time period of the current time point, and the third activity associated with the determined preset time period may be obtained.
Therefore, the corresponding relation between the historical behavior state of the user and the corresponding behavior generation time interval is analyzed by utilizing the big data, the accuracy of judging the current behavior state of the user can be improved based on the corresponding relation, the intelligent artificial limb can further operate in a power consumption mode more suitable for the current behavior state of the user, and the operation power consumption of the intelligent artificial limb is reduced as far as possible while the effective control of the user on the intelligent artificial limb is ensured.
In an embodiment, on the basis of the foregoing embodiment, when the terminal calculates the activity level according to the activity level corresponding to the characteristic signal and the activity level corresponding to the target data, the terminal may perform a weighted summation operation according to the activity level and the weight corresponding to the characteristic signal and according to the activity level and the weight corresponding to the target data, so as to obtain the activity level. And the weight corresponding to the characteristic signal is greater than the weight corresponding to the target data.
Optionally, when the target data includes the posture feature data, setting a first weight corresponding to the feature signal to be greater than a second weight corresponding to the posture feature data (if the first weight is set to be 0.6 and the second weight is set to be 0.4), and after calculating to obtain a first activity corresponding to the feature signal and a second activity corresponding to the posture feature data, summing products of the first activity and the first weight and products of the second activity and the second weight to obtain the activity.
Optionally, when the target data includes the current time period, the first weight corresponding to the feature signal may be set to be greater than the third weight corresponding to the current time period (for example, the first weight is set to be 0.6, and the third weight is set to be 0.4), and after the first activity degree corresponding to the feature signal and the third activity degree corresponding to the current time period are obtained through calculation, the product of the first activity degree and the first weight, and the product of the third activity degree and the third weight are summed to obtain the activity degree.
Optionally, when the target data includes the posture characteristic data and the current period, the first weight corresponding to the characteristic signal may be set to be greater than the second weight corresponding to the posture characteristic data, and the first weight corresponding to the characteristic signal may be set to be greater than the third weight corresponding to the current period (for example, the first weight is set to be 0.6, the second weight is set to be 0.2, and the third weight is set to be 0.2, or the first weight is set to be 0.4, the second weight is set to be 0.3, and the third weight is set to be 0.3), and after the first activity degree corresponding to the characteristic signal, the second activity degree corresponding to the posture characteristic signal, and the third activity degree corresponding to the current period are obtained through calculation, the product of the first activity degree and the first weight, the product of the second activity degree and the second weight, and the product of the third activity degree and the third weight are summed to obtain the activity degree.
Therefore, when the characteristic signals and the target data are adopted to jointly determine the activity degree, the characteristic signals are assigned with weights larger than the target data, so that the current behavior state of the user can be judged in an auxiliary mode by combining the target data, the characteristic of high accuracy rate when the behavior state is judged by utilizing the characteristic signals to the maximum extent can be utilized, the accuracy rate of analyzing the current behavior state of the user is further improved, the intelligent artificial limb can operate in a power consumption mode more suitable for the current behavior state of the user, and the operation power consumption of the intelligent artificial limb is reduced as far as possible while the effective control of the user on the intelligent artificial limb is ensured.
In addition, the embodiment of the present application also provides a control device 10 for an intelligent prosthesis, including:
the acquisition module 11 is used for acquiring a characteristic signal of an intelligent prosthesis user, wherein the characteristic signal is an electromyographic signal or an electroencephalographic signal;
the processing module 12 is configured to determine a behavior state of the user according to the characteristic signal;
and the control module 13 is used for controlling the intelligent artificial limb to operate in a power consumption mode corresponding to the behavior state.
Optionally, on the basis of the above embodiment, the acquisition module is further configured to acquire target data, where the target data includes the posture characteristic data of the user and/or a current time period;
the processing module comprises a calculation module and an analysis module;
the computing module is used for determining the activity degree of the behaviors according to the characteristic signals and the target data;
and the analysis module is used for determining the behavior state of the user according to the behavior activity, wherein the lower the behavior activity is, the lower the running power consumption corresponding to the behavior state is determined to be.
Referring to fig. 4, an intelligent prosthesis is also provided in the embodiments of the present application, and the internal structure of the intelligent prosthesis can be as shown in fig. 4. The intelligent artificial limb comprises a processor, a memory, a network interface and a database which are connected through a system bus. Wherein the computer designed processor is used to provide computational and control capabilities. The memory of the intelligent artificial limb comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the intelligent artificial limb is used for a control program of the intelligent artificial limb. The network interface of the intelligent artificial limb is used for communicating with an external terminal through network connection. The input device of the intelligent artificial limb is used for receiving signals input by external equipment. The computer program is executed by a processor to implement a control method of an intelligent prosthesis as described in the above embodiments.
It will be understood by those skilled in the art that the structure shown in fig. 4 is a block diagram of only a portion of the structure associated with the present application and does not constitute a limitation on the intelligent prosthesis to which the present application is applied.
Furthermore, the present application also proposes a computer-readable storage medium, which includes a control program of an intelligent prosthesis, and the control program of the intelligent prosthesis, when executed by a processor, implements the steps of the control method of the intelligent prosthesis according to the above embodiments. It is to be understood that the computer-readable storage medium in the present embodiment may be a volatile-readable storage medium or a non-volatile-readable storage medium.
In summary, in the control method of the intelligent artificial limb, the control device of the intelligent artificial limb, the intelligent artificial limb and the computer readable storage medium provided in the embodiment of the present application, the behavior state of the user is analyzed based on the relevant characteristic signal of the user, and the usage requirement of the user on the intelligent artificial limb is determined based on the relevant behavior liveness, so as to control the intelligent artificial limb to operate in the corresponding power consumption mode according to the behavior state of the user, so that the intelligent artificial limb can operate in the corresponding low power consumption mode in some scenes where the usage requirement of the user is low, thereby reducing the operation power consumption of the intelligent artificial limb to a certain extent, and satisfying the usage requirement of the user on the intelligent artificial limb, so as to ensure the effective control of the user on the intelligent artificial limb.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium provided herein and used in the examples may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), double-rate SDRAM (SSRSDRAM), Enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, apparatus, article, or method that includes the element.
The above description is only for the preferred embodiment of the present application and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application, or which are directly or indirectly applied to other related technical fields, are intended to be included within the scope of the present application.
Claims (10)
1. A control method of an intelligent artificial limb is characterized by comprising the following steps:
collecting a characteristic signal of an intelligent artificial limb user, wherein the characteristic signal is an electroencephalogram signal or an electromyogram signal;
determining the behavior state of the user according to the characteristic signal;
and controlling the intelligent artificial limb to operate in a power consumption mode corresponding to the behavior state.
2. A control method for an intelligent prosthesis according to claim 1, wherein the step of determining the behavioural state of the user from the characteristic signal is preceded by the further step of:
acquiring target data, wherein the target data comprises gesture characteristic data and/or current time period of the user;
the step of determining the behavior state of the user according to the characteristic signal comprises:
determining a behavior activity level according to the characteristic signals and the target data;
and determining the behavior state of the user according to the behavior activity, wherein the lower the behavior activity is, the lower the running power consumption corresponding to the behavior state is determined to be.
3. A control method for an intelligent prosthetic limb according to claim 2, wherein the current time period is a preset time period at the current time point, the preset time period comprises a plurality of time periods, and the preset time period is obtained by performing big data analysis based on the historical behavior state of the user.
4. A control method for an intelligent prosthesis according to claim 2 or 3, in which the step of determining activity level from the signature signal and the target data comprises:
determining the activity corresponding to the characteristic signal and determining the activity corresponding to the target data;
and calculating the activity degree according to the activity degree corresponding to the characteristic signal and the activity degree corresponding to the target data.
5. A control method for an intelligent prosthesis according to claim 4, wherein the step of calculating activity based on activity corresponding to the signature signal and activity corresponding to the target data includes:
performing weighted summation operation according to the liveness and the weight corresponding to the characteristic signals and according to the liveness and the weight corresponding to the target data to obtain the behavior liveness;
and the weight corresponding to the characteristic signal is greater than the weight corresponding to the target data.
6. A control method for an intelligent prosthesis according to claim 1, wherein the behavioral states include a sleep state, a first active state and a second active state; wherein the content of the first and second substances,
the behavior activity degree corresponding to the sleep state is lower than the behavior activity degree corresponding to the first activity state;
and the activity degree of the behavior corresponding to the first activity state is lower than that of the behavior corresponding to the second activity state.
7. A control device for an intelligent artificial limb, comprising:
the intelligent artificial limb system comprises an acquisition module, a processing module and a control module, wherein the acquisition module is used for acquiring a characteristic signal of an intelligent artificial limb user, and the characteristic signal is an electromyographic signal or an electroencephalographic signal;
the processing module is used for determining the behavior state of the user according to the characteristic signal;
and the control module is used for controlling the intelligent artificial limb to operate in a power consumption mode corresponding to the behavior state.
8. A control device for an intelligent prosthesis according to claim 7, wherein the acquisition module is further configured to obtain target data, the target data including pose characteristic data of the user and/or a current time period;
the processing module comprises a calculation module and an analysis module;
the computing module is used for determining the activity degree of the behaviors according to the characteristic signals and the target data;
and the analysis module is used for determining the behavior state of the user according to the behavior activity, wherein the lower the behavior activity is, the lower the running power consumption corresponding to the behavior state is determined to be.
9. An intelligent prosthesis, comprising a memory, a processor and a control program of the intelligent prosthesis stored on the memory and executable on the processor, wherein the control program of the intelligent prosthesis, when executed by the processor, implements the steps of the control method of the intelligent prosthesis according to any one of claims 1 to 6.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a control program of an intelligent prosthesis, which when executed by a processor, implements the steps of the control method of the intelligent prosthesis according to any one of claims 1 to 6.
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