CN114668564B - Method for dynamically adjusting sampling frequency based on electromyographic signal data - Google Patents
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Abstract
The invention discloses a method for dynamically adjusting sampling frequency based on electromyographic signal data, which comprises the following steps: acquiring posture data and electromyographic signal data, wherein the posture data is used for reflecting the movement posture change of the bionic hand; analyzing the posture data, determining the fluctuation amplitude of the posture data, and analyzing the electromyographic signal data when the fluctuation amplitude meets a preset condition to determine a gesture action corresponding to the electromyographic signal data; and if the gesture action meets the preset action requirement, dynamically adjusting the sampling frequency based on the gesture data. The invention can determine whether the gesture action meets the action requirement by analyzing the gesture data and the electromyographic signal data, and adjust the sampling frequency if the gesture action meets the action requirement so as to control the sampling frequency in time and be beneficial to reducing the energy consumption of the electromyographic equipment.
Description
Technical Field
The invention relates to the technical field of electromyographic signal control, in particular to a method for dynamically adjusting sampling frequency based on electromyographic signal data.
Background
With the development of artificial intelligence technology and bioelectricity collection technology, people increasingly strongly demand intelligent auxiliary equipment. In the life of disabled people, the requirement of the artificial limb is not only limited to beauty and some simple aids, but also the desire of intelligent artificial limb, so that the appearance of intelligent bionic hands is promoted. The intelligent bionic hand is an intelligent product with high integration of a brain-computer interface technology and an artificial intelligence algorithm. The bionic hand can identify the movement intention of the wearer by extracting the arm neuromuscular signals of the wearer and convert the movement schematic diagram into the actions of the bionic hand, so that the dexterity and intelligence are achieved, and the hand moves with the heart.
At present, the bionic hand basically collects the myoelectric signals uninterruptedly, so that the timeliness and the accuracy of the myoelectric signal collection can be ensured. However, the continuous collection of the electromyographic signals causes high energy consumption of the bionic hand and influences the use of the user. Therefore, the energy consumption of the bionic hand cannot be controlled in the prior art.
Thus, there is a need for improvements and enhancements in the art.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a method, an apparatus and a storage medium for dynamically adjusting a sampling frequency based on electromyographic signal data, aiming at solving the problems in the prior art that the energy consumption of a bionic hand is high and cannot be controlled due to the continuous acquisition of the electromyographic signal.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
in a first aspect, the present invention provides a method for dynamically adjusting a sampling frequency based on electromyographic signal data, wherein the method includes:
acquiring posture data and myoelectric signal data, wherein the posture data is used for reflecting the motion posture change of the bionic hand;
analyzing the attitude data to determine the fluctuation amplitude of the attitude data, and analyzing the electromyographic signal data to determine the gesture action corresponding to the electromyographic signal data when the fluctuation amplitude meets a preset condition;
and if the gesture action meets the preset action requirement, dynamically adjusting the sampling frequency based on the gesture data.
In one implementation, the analyzing the attitude data to determine a fluctuation amplitude of the attitude data includes:
analyzing the attitude data in a preset time period, and drawing fluctuation curve data corresponding to the attitude data;
and determining the fluctuation amplitude of the attitude data in the preset time period according to the fluctuation curve data.
In one implementation, the determining, according to the fluctuation curve data, the fluctuation amplitude of the attitude data within the preset time period includes:
acquiring a starting time and a terminating time corresponding to the fluctuation curve data based on the fluctuation curve data, and determining a peak value and a valley value between the starting time and the terminating time;
determining the fluctuation amplitude based on the peak value and the valley value, wherein the fluctuation amplitude is the difference between the peak value and the valley value.
In one implementation manner, when the fluctuation amplitude satisfies a preset condition, analyzing the electromyographic signal data to determine a gesture action corresponding to the electromyographic signal data includes:
comparing the fluctuation amplitude with a preset fluctuation range;
if the fluctuation amplitude is smaller than the fluctuation range, determining that the fluctuation amplitude meets a preset condition;
and acquiring action potential information corresponding to the electromyographic signal data, and determining the gesture action according to the action potential information.
In one implementation, the dynamically adjusting the sampling frequency based on the gesture data if the gesture motion meets a preset motion requirement includes:
determining an action mode corresponding to the gesture action according to the gesture action;
if the action mode is a static action mode or an intermittent action mode, acquiring the duration time of the fluctuation range of the attitude data meeting the preset condition;
and dynamically adjusting the sampling frequency according to the duration.
In one implementation, the dynamically adjusting the sampling frequency according to the duration includes:
comparing the duration with a preset time value;
and if the duration is greater than or equal to the preset time value, reducing the sampling frequency.
In one implementation, the method further includes:
re-acquiring the fluctuation amplitude of the attitude data;
if the fluctuation amplitude of the attitude data obtained again is larger than a preset fluctuation threshold value, the sampling frequency is increased;
reacquiring the electromyographic signal data;
and if the action mode corresponding to the reacquired electromyographic signal data is an active action mode, continuously increasing the sampling frequency until the sampling frequency is restored to the initial state.
In a second aspect, an embodiment of the present invention further provides an apparatus for dynamically adjusting a sampling frequency based on electromyographic signal data, where the apparatus includes:
the data acquisition module is used for acquiring posture data and myoelectric signal data, and the posture data is used for reflecting the movement posture change of the bionic hand;
the data analysis module is used for analyzing the attitude data, determining the fluctuation amplitude of the attitude data, analyzing the electromyographic signal data when the fluctuation amplitude meets a preset condition, and determining the gesture action corresponding to the electromyographic signal data;
and the frequency adjusting module is used for dynamically adjusting the sampling frequency based on the attitude data if the gesture action meets the preset action requirement.
In one implementation, the data analysis module includes:
the fluctuation analysis unit is used for analyzing the attitude data in a preset time period and drawing fluctuation curve data corresponding to the attitude data;
and the amplitude determining unit is used for determining the fluctuation amplitude of the attitude data in the preset time period according to the fluctuation curve data.
In one implementation, the amplitude determining unit includes:
the numerical value determining subunit is configured to obtain a start time and an end time corresponding to the fluctuation curve data based on the fluctuation curve data, and determine a peak value and a valley value between the start time and the end time;
a difference determining subunit, configured to determine the fluctuation amplitude based on the peak value and the valley value, where the fluctuation amplitude is a difference between the peak value and the valley value.
In one implementation, the data analysis module includes:
the fluctuation comparison unit is used for comparing the fluctuation amplitude with a preset fluctuation range;
the condition judgment unit is used for determining that the fluctuation amplitude meets a preset condition if the fluctuation amplitude is smaller than the fluctuation range;
and the action determining unit is used for acquiring action potential information corresponding to the electromyographic signal data and determining the gesture action according to the action potential information.
In one implementation, the frequency adjustment module includes:
the action mode determining unit is used for determining an action mode corresponding to the gesture action according to the gesture action;
a duration determining unit, configured to obtain, if the motion mode is a static motion mode or an intermittent motion mode, a duration that a fluctuation amplitude of the posture data satisfies a preset condition;
and the frequency dynamic adjusting unit is used for dynamically adjusting the sampling frequency according to the duration.
In one implementation, the dynamic frequency adjustment unit includes:
the time comparison subunit is used for comparing the duration with a preset time value;
and the frequency reduction subunit is used for reducing the sampling frequency if the duration is greater than or equal to the preset time value.
In one implementation, the apparatus further includes:
a fluctuation amplitude re-determination unit for re-acquiring the fluctuation amplitude of the attitude data;
the first sampling frequency recovery unit is used for increasing the sampling frequency if the fluctuation amplitude of the attitude data obtained again is larger than a preset fluctuation threshold value;
an electromyographic signal reacquisition unit for reacquiring the electromyographic signal data;
and the second sampling frequency recovery unit is used for continuously increasing the sampling frequency if the action mode corresponding to the reacquired electromyographic signal data is an active action mode until the sampling frequency is recovered to the initial state.
In a third aspect, an embodiment of the present invention further provides an electromyographic device, where the electromyographic device includes a memory, a processor, and a program stored in the memory and running on the processor and used for dynamically adjusting a sampling frequency based on electromyographic signal data, and when the processor executes the program used for dynamically adjusting the sampling frequency based on electromyographic signal data, the method for dynamically adjusting the sampling frequency based on electromyographic signal data according to any one of the above schemes is implemented.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, where a program for dynamically adjusting a sampling frequency based on electromyographic signal data is stored on the computer-readable storage medium, and when the program for dynamically adjusting a sampling frequency based on electromyographic signal data is executed by a processor, the steps of the method for dynamically adjusting a sampling frequency based on electromyographic signal data according to any one of the above schemes are implemented.
Has the advantages that: compared with the prior art, the invention provides a method for dynamically adjusting sampling frequency based on electromyographic signal data. And then analyzing the attitude data to determine the fluctuation amplitude of the attitude data, and analyzing the electromyographic signal data to determine the gesture action corresponding to the electromyographic signal data when the fluctuation amplitude meets a preset condition. And if the gesture action meets the preset action requirement, dynamically adjusting the sampling frequency based on the gesture data. The invention can determine whether the gesture action meets the action requirement by analyzing the gesture data and the electromyographic signal data, and adjust the sampling frequency if the gesture action meets the action requirement so as to control the sampling frequency in time and be beneficial to reducing the energy consumption of the electromyographic equipment.
Drawings
Fig. 1 is a flowchart of a method for dynamically adjusting a sampling frequency based on electromyographic signal data according to an embodiment of the present invention.
Fig. 2 is a schematic block diagram of an apparatus for dynamically adjusting a sampling frequency based on electromyographic signal data according to an embodiment of the present invention.
Fig. 3 is a schematic block diagram of an electromyographic apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and effects of the present invention clearer and clearer, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment provides a method for dynamically adjusting sampling frequency based on electromyographic signal data, and the method based on the embodiment can control the sampling frequency of the electromyographic signal, so that the control of energy consumption of electromyographic equipment is realized. In specific implementation, the present embodiment first obtains posture data and myoelectric signal data, where the posture data is used to reflect the movement posture change of the bionic hand. And then analyzing the attitude data to determine the fluctuation amplitude of the attitude data, and analyzing the electromyographic signal data to determine the gesture action corresponding to the electromyographic signal data when the fluctuation amplitude meets a preset condition. And if the gesture action meets the preset action requirement, dynamically adjusting the sampling frequency based on the gesture data. The embodiment can analyze the gesture data and the electromyographic signal data to determine whether the gesture action meets the action requirement, and if the gesture action meets the action requirement, the sampling frequency is adjusted to control the sampling frequency in time, so that the energy consumption of the electromyographic equipment is reduced.
For example, the electromyographic device first obtains posture data and electromyographic signal data, where the posture data is used to reflect a motion posture change of a bionic hand, so that the electromyographic device may analyze the posture data to obtain a fluctuation amplitude, for example, the fluctuation amplitude is a, if the fluctuation amplitude a meets a preset condition, the electromyographic signal data may be analyzed to determine a gesture motion corresponding to the electromyographic signal data, and if the determined gesture motion is a static motion, it is indicated that the motion posture corresponding to the gesture motion is small, and at this time, a sampling frequency of the electromyographic signal data may be reduced to reduce energy consumption of the electromyographic device.
Exemplary method
The method for dynamically adjusting the sampling frequency based on the electromyographic signal data of the embodiment can be applied to electromyographic equipment, which can be an intelligent bionic hand, and specifically, as shown in fig. 1, the method for dynamically adjusting the sampling frequency based on the electromyographic signal data comprises the following steps:
s100, acquiring posture data and electromyographic signal data, wherein the posture data is used for reflecting the movement posture change of the bionic hand.
The electromyographic device of the embodiment firstly acquires posture data and electromyographic signal data, wherein the posture data reflects the motion posture change of a bionic hand (namely the electromyographic device), and specifically comprises data such as coordinate change of each finger joint of the bionic hand. In specific implementation, the embodiment may detect and obtain IMU (inertial measurement unit) data of the finger joint through the inertial measurement unit, and the IMU data may reflect the posture data. The electromyographic signal data is data which is collected by connecting the electromyographic equipment with neurons on arms of a human body and can reflect action potential information on the neurons.
Step S200, analyzing the attitude data, determining the fluctuation amplitude of the attitude data, and analyzing the electromyographic signal data when the fluctuation amplitude meets a preset condition to determine the gesture action corresponding to the electromyographic signal data.
After obtaining the posture data and the electromyographic signal data, the present embodiment may first analyze the posture data to determine the fluctuation amplitude of the posture data. The fluctuation amplitude reflects the change degree of the signal corresponding to the attitude data. After the fluctuation amplitude is obtained, the fluctuation amplitude is analyzed, and if the fluctuation amplitude meets a preset condition, the electromyographic signal data can be analyzed to determine the gesture action corresponding to the electromyographic signal data.
In one implementation, the present embodiment, when determining the fluctuation amplitude, includes the following steps:
step S201, analyzing the attitude data in a preset time period, and drawing fluctuation curve data corresponding to the attitude data;
step S202, determining the fluctuation amplitude of the attitude data in the preset time period according to the fluctuation curve data.
In specific implementation, the present embodiment may collect posture data within a preset time period, analyze the posture data, draw corresponding fluctuation curve data according to the posture data, and specifically, automatically generate the fluctuation curve data according to the posture data based on preset software. The fluctuation curve data reflects posture data at different times and can reflect the change condition of the posture data within a preset time period, so that the embodiment can acquire the starting time and the ending time corresponding to the fluctuation curve data according to the fluctuation curve data and determine the peak value and the valley value between the starting time and the ending time. Determining the fluctuation amplitude based on the peak value and the valley value, wherein the fluctuation amplitude is the difference between the peak value and the valley value. For example, the fluctuation range is a-B because the peak value and the trough value between the start time and the end time obtained from the fluctuation curve data are a and B, respectively.
Next, when determining the gesture, the embodiment includes the following steps:
step S203, comparing the fluctuation amplitude with a preset fluctuation range;
step S204, if the fluctuation amplitude is smaller than the fluctuation range, determining that the fluctuation amplitude meets a preset condition;
and S205, acquiring action potential information corresponding to the electromyographic signal data, and determining the gesture action according to the action potential information.
Specifically, after obtaining the fluctuation range, the present embodiment compares the fluctuation range with a preset fluctuation range; and if the fluctuation amplitude is smaller than the fluctuation range, determining that the fluctuation amplitude meets a preset condition. In this embodiment, the preset fluctuation range may be set to be relatively small, and when the fluctuation range is smaller than the fluctuation range, it may be said that the fluctuation of the attitude data is small. The electromyographic signal data may then be further analyzed. The embodiment acquires action potential information corresponding to the electromyographic signal data, and determines a gesture action corresponding to the electromyographic signal data at the moment based on the action potential information. Since the action potential information corresponding to different gesture actions is different, the gesture actions can be determined based on a preset gesture template, and the action potential information corresponding to different gesture actions is set in the gesture template, so that after the action potential information corresponding to the electromyographic signal data is obtained, the action potential information is input into the gesture template for matching, and the corresponding gesture actions can be obtained.
And step S300, if the gesture motion meets the preset motion requirement, dynamically adjusting the sampling frequency based on the attitude data.
After the gesture action is determined, the gesture action is analyzed, if the gesture action meets a preset action requirement, for example, the gesture action is a static action, it indicates that the user does not perform too complicated or too many actions, and the electromyographic device does not need to acquire electromyographic signal data in real time or continuously, so that the sampling frequency can be dynamically adjusted.
In an implementation manner, when the sampling frequency is dynamically adjusted, the embodiment includes the following steps:
step S301, determining an action mode corresponding to the gesture action according to the gesture action;
step S302, if the action mode is a static action mode or an intermittent action mode, acquiring the duration time of the fluctuation range of the attitude data meeting a preset condition;
and step S303, dynamically adjusting the sampling frequency according to the duration.
Specifically, the embodiment determines the motion mode corresponding to the gesture motion according to the gesture motion. Specifically, the embodiment may analyze the occurrence frequency and the occurrence time of the gesture motion and the motion amplitude of the gesture motion, and if the occurrence frequency of the gesture motion is less than a preset value and the occurrence time is not continuous, it may be determined that the motion mode of the gesture motion is an intermittent motion mode, that is, the gesture motion is not continuous. If the motion amplitude of the gesture motion is smaller than the preset range, the static motion mode of the gesture motion can be determined. When the gesture motion of the present embodiment is determined to be the static motion mode and the intermittent motion mode, the present embodiment may obtain the fluctuation range of the gesture data, and then obtain the duration that the fluctuation range meets the preset condition, that is, obtain the duration that the fluctuation range of the gesture data is smaller. Then comparing the duration with a preset time value; if the duration is greater than or equal to the preset time value, it is indicated that the time for maintaining the small fluctuation amplitude of the gesture data is long, and the motion mode of the gesture motion at this time is a static motion mode or an intermittent motion mode, and for the electromyographic device, frequent sampling of electromyographic signal data is not needed, so that the sampling frequency can be reduced, the sampling result and control of the electromyographic device (such as bionic) are not affected, and the energy consumption of the electromyographic device is also reduced.
In one implementation, after the sampling frequency is reduced, the embodiment may reacquire the fluctuation amplitude of the attitude data; if the fluctuation range of the re-acquired attitude data is greater than the preset fluctuation threshold, the fluctuation of the attitude data at this time is relatively large, so that the sampling frequency can be increased, and specifically, the sampling frequency can be restored to half of the sampling frequency before the reduction. Then, the electromyographic device reacquires the electromyographic signal data, and if the action mode corresponding to the reacquired electromyographic signal data is an active action mode (that is, the action amplitude of the gesture action corresponding to the reacquired electromyographic signal data is relatively large), in order to capture the electromyographic signal data in time so as to miss the gesture action that the user wants to execute, the electromyographic device may continue to increase the sampling frequency until the sampling frequency returns to the initial state, that is, returns to the sampling frequency before the decrease. Therefore, the embodiment can dynamically adjust the sampling frequency of the electromyographic equipment, realize the control of the energy consumption of the electromyographic equipment, and reduce the energy consumption of the electromyographic equipment on the premise of not influencing the use of a user.
In summary, the present embodiment first obtains posture data and myoelectric signal data, where the posture data is used to reflect the movement posture change of the bionic hand. And then analyzing the attitude data to determine the fluctuation amplitude of the attitude data, and analyzing the electromyographic signal data to determine the gesture action corresponding to the electromyographic signal data when the fluctuation amplitude meets a preset condition. And if the gesture action meets the preset action requirement, dynamically adjusting the sampling frequency based on the gesture data. The embodiment can analyze the gesture data and the electromyographic signal data to determine whether the gesture action meets the action requirement, and if the gesture action meets the action requirement, the sampling frequency is adjusted to control the sampling frequency in time, so that the energy consumption of the electromyographic equipment is reduced.
Exemplary devices
Based on the above embodiment, the present invention further provides an apparatus for dynamically adjusting a sampling frequency based on electromyographic signal data, as shown in fig. 2, the apparatus including: a data acquisition module 10, a data analysis module 20, and a frequency adjustment module 30. Specifically, the data acquiring module 10 is configured to acquire posture data and myoelectric signal data, where the posture data is used to reflect a movement posture change of the bionic hand. The data analysis module 20 is configured to analyze the posture data, determine a fluctuation amplitude of the posture data, and analyze the electromyographic signal data when the fluctuation amplitude satisfies a preset condition, to determine a gesture action corresponding to the electromyographic signal data. The frequency adjustment module 30 is configured to dynamically adjust the sampling frequency based on the gesture data if the gesture motion meets a preset motion requirement.
In one implementation, the data analysis module 20 includes:
the fluctuation analysis unit is used for analyzing the attitude data in a preset time period and drawing fluctuation curve data corresponding to the attitude data;
and the amplitude determining unit is used for determining the fluctuation amplitude of the attitude data in the preset time period according to the fluctuation curve data.
In one implementation, the amplitude determining unit includes:
the numerical value determining subunit is configured to obtain a start time and an end time corresponding to the fluctuation curve data based on the fluctuation curve data, and determine a peak value and a valley value between the start time and the end time;
a difference determining subunit, configured to determine the fluctuation amplitude based on the peak value and the valley value, where the fluctuation amplitude is a difference between the peak value and the valley value.
In one implementation, the data analysis module includes:
the fluctuation comparison unit is used for comparing the fluctuation amplitude with a preset fluctuation range;
the condition judgment unit is used for determining that the fluctuation amplitude meets a preset condition if the fluctuation amplitude is smaller than the fluctuation range;
and the action determining unit is used for acquiring action potential information corresponding to the electromyographic signal data and determining the gesture action according to the action potential information.
In one implementation, the frequency adjustment module 30 includes:
the action mode determining unit is used for determining an action mode corresponding to the gesture action according to the gesture action;
a duration determining unit, configured to obtain, if the motion mode is a static motion mode or an intermittent motion mode, a duration that a fluctuation amplitude of the posture data satisfies a preset condition;
and the frequency dynamic adjusting unit is used for dynamically adjusting the sampling frequency according to the duration.
In one implementation, the dynamic frequency adjustment unit includes:
the time comparison subunit is used for comparing the duration with a preset time value;
and the frequency reduction subunit is used for reducing the sampling frequency if the duration is greater than or equal to the preset time value.
In one implementation, the apparatus further includes:
a fluctuation-amplitude re-determination unit configured to re-acquire a fluctuation amplitude of the attitude data;
the first sampling frequency recovery unit is used for increasing the sampling frequency if the fluctuation amplitude of the attitude data obtained again is larger than a preset fluctuation threshold value;
an electromyographic signal reacquisition unit for reacquiring the electromyographic signal data;
and the second sampling frequency recovery unit is used for continuously increasing the sampling frequency if the action mode corresponding to the reacquired electromyographic signal data is an active action mode until the sampling frequency is recovered to the initial state.
The working principle of each module in the device for dynamically adjusting the sampling frequency based on the electromyographic signal data of this embodiment is the same as the principle of each step in the above method embodiment, and details are not described here.
Based on the above embodiments, the present invention also provides an electromyographic device, and a schematic block diagram of the electromyographic device may be as shown in fig. 3. The electromyographic device comprises a processor and a memory which are connected through a system bus, wherein the processor and the memory are arranged in a host. Wherein, the processor of the electromyographic device is used for providing calculation and control capability. The memory of the electromyographic device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the electromyographic equipment is used for being connected and communicated with an external terminal through network communication. The computer program is executed by a processor to implement a method of dynamically adjusting a sampling frequency based on electromyographic signal data.
It will be understood by those skilled in the art that the schematic block diagram shown in fig. 3 is only a block diagram of a partial structure related to the scheme of the present invention, and does not constitute a limitation on the electromyographic device to which the scheme of the present invention is applied, and a specific electromyographic device may include more or less components than those shown in the figure, or may combine some components, or have a different arrangement of components.
In one embodiment, an electromyographic device is provided, where the electromyographic device includes a memory, a processor, and a method program stored in the memory and executable on the processor for dynamically adjusting a sampling frequency based on electromyographic signal data, and when the processor executes the method program for dynamically adjusting the sampling frequency based on electromyographic signal data, the following operation instructions are implemented:
acquiring posture data and electromyographic signal data, wherein the posture data is used for reflecting the movement posture change of the bionic hand;
analyzing the attitude data to determine the fluctuation amplitude of the attitude data, and analyzing the electromyographic signal data to determine the gesture action corresponding to the electromyographic signal data when the fluctuation amplitude meets a preset condition;
and if the gesture action meets the preset action requirement, dynamically adjusting the sampling frequency based on the gesture data.
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 related to 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, operational databases, or other media used in embodiments provided herein 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 (DDRSDRAM), Enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
In summary, the present invention discloses a method for dynamically adjusting sampling frequency based on electromyographic signal data, the method comprising: acquiring posture data and electromyographic signal data, wherein the posture data is used for reflecting the movement posture change of the bionic hand; analyzing the attitude data to determine the fluctuation amplitude of the attitude data, and analyzing the electromyographic signal data to determine the gesture action corresponding to the electromyographic signal data when the fluctuation amplitude meets a preset condition; and if the gesture action meets the preset action requirement, dynamically adjusting the sampling frequency based on the gesture data. The invention can determine whether the gesture action meets the action requirement by analyzing the gesture data and the electromyographic signal data, and adjust the sampling frequency if the gesture action meets the action requirement so as to control the sampling frequency in time and be beneficial to reducing the energy consumption of the electromyographic equipment.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (4)
1. A method for dynamically adjusting a sampling frequency based on electromyographic signal data, the method comprising:
acquiring posture data and electromyographic signal data, wherein the posture data is used for reflecting the movement posture change of the bionic hand;
analyzing the attitude data to determine the fluctuation amplitude of the attitude data, and analyzing the electromyographic signal data to determine the gesture action corresponding to the electromyographic signal data when the fluctuation amplitude meets a preset condition;
if the gesture action meets the preset action requirement, dynamically adjusting the sampling frequency based on the gesture data;
the analyzing the attitude data to determine the fluctuation amplitude of the attitude data includes:
analyzing the attitude data in a preset time period, and drawing fluctuation curve data corresponding to the attitude data;
determining the fluctuation amplitude of the attitude data in the preset time period according to the fluctuation curve data;
determining the fluctuation amplitude of the attitude data within the preset time period according to the fluctuation curve data, wherein the determining comprises the following steps:
acquiring a starting time and a terminating time corresponding to the fluctuation curve data based on the fluctuation curve data, and determining a peak value and a valley value between the starting time and the terminating time;
determining the fluctuation amplitude based on the peak value and the valley value, wherein the fluctuation amplitude is the difference between the peak value and the valley value;
when the fluctuation amplitude meets a preset condition, the myoelectric signal data are analyzed, and a gesture action corresponding to the myoelectric signal data is determined, wherein the gesture action comprises the following steps:
comparing the fluctuation amplitude with a preset fluctuation range;
if the fluctuation amplitude is smaller than the fluctuation range, determining that the fluctuation amplitude meets a preset condition;
acquiring action potential information corresponding to the electromyographic signal data, and determining the gesture action according to the action potential information;
if the gesture motion meets the preset motion requirement, dynamically adjusting the sampling frequency based on the gesture data, wherein the dynamic adjustment comprises the following steps:
determining an action mode corresponding to the gesture action according to the gesture action, if the occurrence frequency of the gesture action is smaller than a preset value and the occurrence time is discontinuous, determining that the action mode of the gesture action is an intermittent action mode, and if the action amplitude of the gesture action is smaller than a preset range, determining that the action mode of the gesture action is a static action mode;
if the action mode is a static action mode or an intermittent action mode, acquiring the duration time of the fluctuation range of the attitude data meeting the preset condition;
according to the duration, the sampling frequency is dynamically adjusted;
the dynamically adjusting the sampling frequency according to the duration includes:
comparing the duration with a preset time value;
if the duration is greater than or equal to the preset time value, reducing the sampling frequency;
the method further comprises the following steps:
re-acquiring the fluctuation amplitude of the attitude data;
if the fluctuation amplitude of the attitude data obtained again is larger than a preset fluctuation threshold value, the sampling frequency is increased;
reacquiring the electromyographic signal data;
and if the action mode corresponding to the reacquired electromyographic signal data is an active action mode, continuously increasing the sampling frequency until the sampling frequency is restored to the initial state.
2. An apparatus for dynamically adjusting a sampling frequency based on electromyographic signal data, the apparatus comprising:
the data acquisition module is used for acquiring posture data and myoelectric signal data, and the posture data is used for reflecting the movement posture change of the bionic hand;
the data analysis module is used for analyzing the attitude data, determining the fluctuation amplitude of the attitude data, analyzing the electromyographic signal data when the fluctuation amplitude meets a preset condition, and determining the gesture action corresponding to the electromyographic signal data;
the frequency adjusting module is used for dynamically adjusting the sampling frequency based on the gesture data if the gesture action meets the preset action requirement;
the data analysis module comprises:
the fluctuation analysis unit is used for analyzing the attitude data in a preset time period and drawing fluctuation curve data corresponding to the attitude data;
the amplitude determining unit is used for determining the fluctuation amplitude of the attitude data in the preset time period according to the fluctuation curve data;
the amplitude determination unit includes:
the numerical value determining subunit is configured to obtain a start time and an end time corresponding to the fluctuation curve data based on the fluctuation curve data, and determine a peak value and a valley value between the start time and the end time;
a difference determining subunit, configured to determine the fluctuation amplitude based on the peak value and the valley value, where the fluctuation amplitude is a difference between the peak value and the valley value;
the data analysis module comprises:
the fluctuation comparison unit is used for comparing the fluctuation amplitude with a preset fluctuation range;
the condition judgment unit is used for determining that the fluctuation amplitude meets a preset condition if the fluctuation amplitude is smaller than the fluctuation range;
the action determining unit is used for acquiring action potential information corresponding to the electromyographic signal data and determining the gesture action according to the action potential information;
the frequency adjustment module includes:
the action mode determining unit is used for determining an action mode corresponding to the gesture action according to the gesture action, if the occurrence frequency of the gesture action is smaller than a preset value and the occurrence time is discontinuous, determining that the action mode of the gesture action is an intermittent action mode, and if the action amplitude of the gesture action is smaller than a preset range, determining that the action mode of the gesture action is a static action mode;
a duration determining unit, configured to obtain, if the motion mode is a static motion mode or an intermittent motion mode, a duration that a fluctuation amplitude of the posture data satisfies a preset condition;
the frequency dynamic adjustment unit is used for dynamically adjusting the sampling frequency according to the duration;
the dynamic frequency adjustment unit comprises:
the time comparison subunit is used for comparing the duration with a preset time value;
a frequency reduction subunit, configured to reduce the sampling frequency if the duration is greater than or equal to the preset time value;
the device, still include:
a fluctuation amplitude re-determination unit for re-acquiring the fluctuation amplitude of the attitude data;
the first sampling frequency recovery unit is used for increasing the sampling frequency if the fluctuation amplitude of the attitude data obtained again is larger than a preset fluctuation threshold value;
an electromyographic signal reacquisition unit for reacquiring the electromyographic signal data;
and the second sampling frequency recovery unit is used for continuously increasing the sampling frequency if the action mode corresponding to the reacquired electromyographic signal data is an active action mode until the sampling frequency is recovered to the initial state.
3. An electromyographic device comprising a memory, a processor, and a program stored in the memory and operable on the processor to dynamically adjust a sampling frequency based on electromyographic signal data, the processor implementing the steps of the method of dynamically adjusting a sampling frequency based on electromyographic signal data of claim 1 when executing the program to dynamically adjust a sampling frequency based on electromyographic signal data.
4. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a program for dynamically adjusting the sampling frequency based on electromyographic signal data, the program for dynamically adjusting the sampling frequency based on electromyographic signal data implementing the steps of the method for dynamically adjusting the sampling frequency based on electromyographic signal data according to claim 1, when executed by a processor.
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