CN114683292B - Sampling frequency control method of electromyographic equipment, intelligent bionic hand and storage medium - Google Patents
Sampling frequency control method of electromyographic equipment, intelligent bionic hand and storage medium Download PDFInfo
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Abstract
The invention discloses a sampling frequency control method of electromyographic equipment, an intelligent bionic hand and a storage medium, wherein the method comprises the following steps: acquiring gesture electromyographic signal data and inertia measurement unit data, and determining a motion mode according to the gesture electromyographic signal data and the inertia measurement unit data, wherein the inertia measurement unit data is used for reflecting the motion posture of a user in a three-dimensional space; according to the action mode, determining a sampling frequency, wherein the sampling frequency is used for determining the frequency for acquiring the gesture electromyographic signal data and the inertia measurement unit data; and adjusting the sampling frequency according to the data of the inertial measurement unit. The invention can determine different sampling frequencies according to different action modes, and can adjust the sampling frequency according to the data of the inertia measurement unit, thereby saving power consumption and prolonging the battery endurance time of the electromyographic equipment.
Description
Technical Field
The invention relates to the field of power consumption control, in particular to a sampling frequency control method of electromyographic equipment, an intelligent bionic hand and a storage medium.
Background
The intelligent bionic hand is an intelligent product with high integration of a brain-computer interface technology and an artificial intelligence algorithm. The intelligent bionic hand can identify the movement intention of the wearer by extracting the neuromuscular signals of the wearer and convert the movement schematic diagram into the movement of the intelligent bionic hand, so that the smart intelligence is achieved, and the hand moves with the heart.
However, the current intelligent bionic hand cannot change the power consumption strategy along with the change of the action mode of the user during the use. For example, when the intelligent bionic hand of the user is switched from the motion mode to the natural placement state, the frequency of signal acquisition is not reduced, so that the power consumption of the intelligent bionic hand is wasted, and the endurance time of the intelligent bionic hand is influenced.
Accordingly, the prior art is yet to be improved and developed.
Disclosure of Invention
The invention aims to solve the technical problems that a sampling frequency control method of the electromyographic equipment, an intelligent bionic hand and a storage medium are provided aiming at overcoming the defects in the prior art, and the problems that in the using process of the electromyographic equipment, the power consumption is wasted due to the fact that a power consumption strategy cannot be changed according to an action mode in the prior art are solved, so that the problem that the endurance time of the electromyographic equipment is insufficient is solved.
The technical scheme adopted by the invention for solving the technical problem is as follows:
in a first aspect, the present invention provides a sampling frequency control method of an electromyographic device, wherein the method comprises:
acquiring gesture electromyographic signal data and inertia measurement unit data, and determining a motion mode according to the gesture electromyographic signal data and the inertia measurement unit data, wherein the inertia measurement unit data is used for reflecting the motion posture of a user in a three-dimensional space;
determining a sampling frequency according to the action mode, wherein the sampling frequency is the frequency for acquiring the gesture electromyographic signal data and the inertia measurement unit data;
and adjusting the sampling frequency according to the data of the inertial measurement unit.
In one implementation, the acquiring gesture electromyographic signal data and inertial measurement unit data, and determining a motion mode according to the gesture electromyographic signal data and the inertial measurement unit data includes:
acquiring the electromyographic signal data, and analyzing the electromyographic signal data to obtain action potential information corresponding to the electromyographic signal data;
determining a gesture action corresponding to the action potential information according to the action potential information;
acquiring the data of the inertial measurement unit, and analyzing the data of the inertial measurement unit to obtain a motion attitude corresponding to the data of the inertial measurement unit;
determining the motion mode according to the gesture motion and the motion gesture.
In one implementation, the determining the motion pattern from the gesture motion and the motion gesture includes:
if the motion posture is a sitting posture and the gesture motion is a natural placing state of the sitting posture, determining that the motion mode is the natural state mode;
if the motion posture is a standing posture and the gesture motion is a natural placing state of the standing posture, determining that the motion mode is the natural state mode;
and if the motion posture is a walking posture and the gesture motion is a natural placing state of the walking posture, determining that the motion mode is the natural state mode.
In one implementation, the determining a sampling frequency according to the motion pattern includes:
matching the action mode with a preset sampling frequency template, wherein the sampling frequency template is provided with a corresponding relation between the sampling frequency and the action mode;
and determining the sampling frequency corresponding to the action mode according to the sampling frequency template.
In one implementation, the adjusting the sampling frequency according to the inertial measurement unit data includes:
obtaining a fluctuation numerical value of the inertial measurement unit data according to the inertial measurement unit data;
and adjusting the sampling frequency according to the fluctuation value of the data of the inertial measurement unit.
In one implementation, the adjusting the sampling frequency according to the fluctuation value of the inertial measurement unit data includes:
if the fluctuation value of the data of the inertia measurement unit is larger than a first fluctuation threshold value, increasing the sampling frequency;
and if the fluctuation value of the data of the inertia measurement unit is smaller than a second fluctuation threshold value, reducing the sampling frequency.
In one implementation, the method further comprises:
if the action mode is a natural state mode, comparing the duration of the natural state mode with a preset duration threshold;
if the duration is greater than the preset duration threshold, reducing the sampling frequency to be a limit sampling frequency corresponding to the natural state mode, wherein the limit sampling frequency is a preset lowest sampling frequency value corresponding to the natural state mode.
In a second aspect, an embodiment of the present invention further provides a sampling frequency control apparatus for an electromyographic device, where the apparatus includes:
the motion mode acquisition module is used for acquiring gesture electromyographic signal data and inertia measurement unit data and determining a motion mode according to the gesture electromyographic signal data and the inertia measurement unit data;
the sampling frequency acquisition module is used for determining a sampling frequency according to the action mode, and the sampling frequency is used for determining the frequency for acquiring the gesture electromyographic signal data and the inertia measurement unit data;
and the sampling frequency adjusting module is used for adjusting the sampling frequency according to the data of the inertial measurement unit.
In a third aspect, an embodiment of the present invention further provides an intelligent bionic hand, which is characterized in that the intelligent bionic hand includes a memory, a processor, and a program of a sampling frequency control method of an electromyographic device stored in the memory and capable of running on the processor, and when the processor executes the program of the sampling frequency control method of the electromyographic device, the step of the sampling frequency control method of the electromyographic device according to any one of the above-mentioned schemes is implemented.
In a fourth aspect, an embodiment of the present invention further provides a computer readable storage medium, which is characterized in that the computer readable storage medium stores a program of a sampling frequency control method of an electromyographic device, and when the program of the sampling frequency control method of the electromyographic device is executed by a processor, the method realizes the steps of the sampling frequency control method of the electromyographic device according to any one of the above schemes.
Has the advantages that: compared with the prior art, the invention provides a sampling frequency control method of electromyographic equipment. Therefore, the myoelectric device can be controlled to collect gesture myoelectric signal data and inertia measurement unit data with different frequencies according to different action modes. And meanwhile, adjusting the sampling frequency according to the data of the inertial measurement unit. Therefore, the sampling frequency can be dynamically adjusted, so that the effects of controlling the power consumption of the intelligent bionic hand and prolonging the battery endurance time are achieved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a specific implementation of a sampling frequency control method of an electromyographic device according to an embodiment of the present invention.
Fig. 2 is an area diagram of a fluctuation value of inertial measurement unit data according to an embodiment of the present invention.
Fig. 3 is a schematic block diagram of a sampling frequency control apparatus of an electromyographic apparatus according to an embodiment of the present invention.
Fig. 4 is a schematic block diagram of an internal structure of an intelligent bionic hand provided by 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 intelligent bionic hand is an intelligent product with high integration of a brain-computer interface technology and an artificial intelligence algorithm. The intelligent 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 movement of the intelligent bionic hand, so that the smart intelligence is achieved, and the hand moves with the heart. However, the current intelligent bionic hand collects signal data with the same frequency during use, and the collection frequency cannot be changed according to the action mode of a user. For example, when the intelligent bionic hand of the user is in a natural placing state, only little signal data can be generated, but the frequency of signal acquisition cannot be reduced by the intelligent bionic hand, so that the power consumption of the intelligent bionic hand is wasted, and the endurance time of the intelligent bionic hand is influenced.
Therefore, in order to solve the above problems, the present embodiment provides a sampling frequency control method for an electromyographic device, which first acquires gesture electromyographic signal data and inertial measurement unit data, determines an action mode according to the gesture electromyographic signal data and the inertial measurement unit data, determines a sampling frequency according to the action mode, and finally adjusts the sampling frequency according to the inertial measurement unit data. According to the method, the electromyographic equipment can be controlled to sample at different frequencies according to different action modes, the sampling frequency is increased or decreased continuously according to the data of the inertia measurement unit, the power consumption of the electromyographic equipment is controlled, and the battery endurance time is prolonged.
For example, when acquiring gesture electromyographic signal data and inertia measurement unit data, determining that a motion posture is a sitting posture, determining that a gesture motion is a natural placing state of the sitting posture, determining that a motion mode is the natural state mode, and obtaining that the sampling frequency in the natural state mode is 1 time/second, the intelligent bionic hand collects signal data every other second, and if the fluctuation numerical value of the inertia measurement unit data is small and the sampling frequency can be reduced to 1 time/minute, the sampling frequency is reduced to once per minute, so that the power consumption is reduced. If the sampling frequency is increased to 2 times/second according to the larger fluctuation value of the data of the inertia measurement unit, the sampling frequency is further increased to two times per second, and therefore the signal data of the user is acquired in time.
Exemplary method
The embodiment provides a sampling frequency control method of an electromyographic device, which can be applied to a terminal device, and the terminal device can be a computer, a mobile phone and other intelligent terminal products. In addition, the sampling frequency control method of the electromyographic device in this embodiment is applied to the bionic hand, so the terminal device in this embodiment can establish communication connection with the bionic hand, or be directly arranged on the bionic hand to form an intelligent bionic hand.
In specific implementation, as shown in fig. 1, the sampling frequency control method of the electromyographic device in this embodiment includes the following steps:
s100, acquiring gesture electromyographic signal data and inertia measurement unit data, and determining a motion mode according to the gesture electromyographic signal data and the inertia measurement unit data, wherein the inertia measurement unit data is used for reflecting the motion posture of a user in a three-dimensional space.
The electromyographic signal data is the superposition of action potential information of a motor unit (such as an arm) in a plurality of muscle fibers on time and space, and can reflect the activity of neuromuscular to a certain extent. The intelligent bionic hand is provided with an inertia measurement unit for collecting data of the inertia measurement unit. The inertial measurement unit comprising three single axesAccelerometerAnd three single axesGyroscopeThe accelerometer detects the acceleration signals of the user in the three independent axes of the carrier coordinate system, and the gyroscope detects the acceleration signals of the carrier relative to the navigation coordinate systemAngular velocitySignal, measure the user atThree-dimensional spaceAnd the angular velocity and the acceleration are calculated, and the motion posture of the user is calculated according to the angular velocity and the acceleration. Three axis acceleration signal data andangular velocityThe signal data is the inertial measurement unit data.
The action pattern is a result of classifying actions. The actions are divided into different categories, so that the intelligent bionic hand can determine which signal acquisition frequency is more suitable for which action, because the gesture electromyographic signal data can reflect the activity of neuromuscular, the inertia measurement unit data can reflect the motion posture of a user, and the user can determine what type of action the user does at present by combining the gesture electromyographic signal data and the inertia measurement unit data, and the action mode can be determined.
For example, when the electromyographic signal data of the intelligent bionic hand user reflects that the user is patting an object with a hand, and the data of the inertia measurement unit reflects that the user is jumping, rotating and squatting, the action mode of the user is determined to be basketball playing.
In one implementation, the step S100 includes the following steps:
step S101, acquiring the electromyographic signal data, and analyzing the electromyographic signal data to obtain action potential information corresponding to the electromyographic signal data;
and S102, determining a gesture action corresponding to the action potential information according to the action potential information.
Specifically, each electromyographic signal data has different action potential information, and the action potential information reflects that the surface electromyographic signal is a comprehensive effect of the electrical activity of superficial muscles and nerve trunks on the skin surface, so that the gesture characteristics corresponding to the action gesture made by the user can be determined based on the action potential information. The embodiment can acquire corresponding action potential information from the effective electromyographic signal data, and then analyze corresponding gesture characteristics, thereby determining gesture actions.
For example, after myoelectric signal data with the numbers of a1 and a2 … a10 are collected by the intelligent bionic hand, action potential information corresponding to the myoelectric signal data is a1 and a2 … a 10. And determining the gesture action corresponding to each action potential information according to the action potential information. For example, it is determined that the gesture motions corresponding to the motion potential information a1, a2, A3 are handshake motions, the gesture motions corresponding to the motion potential information a4, A5, A6, a7, a8, a9 are pen holding motions, the motion potential information a10 is key holding motions, the gesture motions corresponding to the electromyographic signal data a1, a2, A3 are handshake motions, the gesture motions corresponding to the electromyographic signal data a4, A5, A6, a7, a8, a9 are pen holding motions, and the gesture motion corresponding to a10 is key holding motions.
Step S103, acquiring the data of the inertial measurement unit, and analyzing the data of the inertial measurement unit to obtain a motion attitude corresponding to the data of the inertial measurement unit;
and step S104, determining the motion mode according to the gesture motion and the motion posture.
Specifically, the movement posture of the whole body of the user in the three-dimensional space is obtained through the data of the inertial measurement unit, and the data of the inertial measurement unit can be obtained at any time in the use process by wearing the intelligent bionic hand with the inertial measurement unit. The inertial measurement unit data are three-axis attitude angular velocity and acceleration, and reflect the whole body movement attitude of the intelligent bionic hand user, such as jumping attitude, rotating attitude, walking attitude, sitting attitude, standing attitude and the like. The gesture motion reflects a partial motion of the user's hand, such as a punch, pen, tap, five-finger bend, five-finger stretch, etc. Considering that the gesture action of the human body can be adjusted under different motion postures, the intelligent bionic hand can combine the motion posture with the gesture action when determining the action mode. For example, the motion posture is a massage motion mode in which a combination of sitting posture and flick gesture is a massage, but the motion posture is a basketball motion mode in which a combination of standing posture and flick gesture is a flick gesture.
For example, if the gesture is used as holding a pen and the movement posture is sitting, the action mode is writing. If the gesture motion is left-right swing and the motion posture is standing, the motion mode is calling. Therefore, local hand actions are determined according to the gesture actions, the motion postures of the whole bodies of the intelligent bionic hand users are obtained according to the data of the inertial measurement units, and then what types of actions the intelligent bionic hand users do can be judged, and the actions are classified to obtain action modes.
In one implementation, the step S104 includes the following steps:
step S1041, if the movement posture is a sitting posture and the gesture movement is a natural placing state of the sitting posture, determining that the movement mode is the natural state mode;
step S1042, if the motion posture is a standing posture and the gesture motion is a natural placing state of the standing posture, determining that the motion mode is the natural state mode;
step S1043, if the motion posture is a walking posture and the gesture motion is a natural placement state of the walking posture, determining that the motion mode is the natural state mode.
Specifically, the natural state mode is a state in which the change frequency of the gesture motion of the bionic hand user is low and the whole body motion is relatively static, and the sending frequencies of the gesture electromyographic signal data and the inertia measurement unit data are both low in the natural state mode. Considering that the natural placing states of the hands of the human body under different motion postures are different, in this embodiment, there are two factors respectively set up the natural placing states of the gesture actions under the sitting posture, the standing posture and the walking posture, and whether the gesture action is judged to be the natural placing state of the motion posture: a specified gesture action and a specified gesture action hold time. Namely, the gesture motion is used as a designated gesture motion in a motion posture, and when the designated gesture motion holding time is higher than a preset natural state threshold value, the gesture motion can be determined as a natural placement state. When judging whether the gesture is the natural state mode, firstly determining the motion posture of the intelligent bionic hand user, then determining whether the gesture action of the intelligent bionic hand user is the natural placing state corresponding to the motion posture, if the gesture action is the natural placing state corresponding to the motion posture, determining that the action mode is the natural state mode under the current motion posture, and if the gesture action is not the natural placing state corresponding to the motion posture, matching the motion mode under the unnatural state for the motion posture and the gesture action. It should be noted that the designated gesture actions may be multiple, and the holding times of the multiple designated gesture actions cannot be overlapped.
For example, in one implementation, the designated gesture motion for setting the natural state mode for the standing position is five-finger micro-flexion or five-finger extension, and the natural state threshold for the five-finger micro-flexion holding time is set to 40 seconds and the natural state threshold for the five-finger extension holding time is set to 40 seconds. When the motion posture of the user of the intelligent bionic hand is a standing posture, the gesture motion is five-finger micro-bending, the holding time is 50 seconds, and the holding time is 40 seconds higher than a preset natural state threshold value, the motion mode in the current standing posture can be determined to be a natural state mode. If the motion posture of the intelligent bionic hand user is a standing posture, the gesture motion is not five-finger micro-bending, the motion mode is not a natural state mode, and if the gesture motion is five-finger micro-bending but the gesture motion holding time is less than 40 seconds, the motion mode is not a natural state mode. If the gesture motion holding time for the five-finger micro-flexion gesture motion is 20 seconds, and then the gesture motion holding time for the five-finger extension gesture motion is 30 seconds, although the holding time of the two designated gesture motions is added to exceed 40 seconds, the gesture motion is not a natural state mode.
And S200, determining a sampling frequency according to the action mode, wherein the sampling frequency is the frequency for acquiring the gesture electromyographic signal data and the inertia measurement unit data.
Specifically, sampling frequency is the frequency of gathering gesture flesh electrical signal data and inertia measurement unit data, and the electric quantity of intelligent bionic hand need be consumed in the sampling, and sampling frequency is higher, and the electric quantity consumption is more, so if want to practice thrift the electric quantity of intelligent bionic hand just need reduce sampling frequency. But simultaneously, if the signal data of the intelligent bionic hand user is required to be collected in time, a certain sampling frequency is required to be ensured. That is to say, the sampling frequency is too high, the electric quantity is wasted, the sampling frequency is too low, and the signal data of the user cannot be acquired in time. In order to determine a reasonable sampling frequency, the present embodiment determines the sampling frequency according to the motion pattern, considering that there is a certain relationship between the occurrence density of the signal data and the motion pattern. After the action mode is determined, the frequency of acquiring the gesture electromyographic signal data and the inertia measurement unit data can be determined according to different action modes, so that the sampling frequency can be dynamically adjusted.
For example, when the action mode is basketball shooting, the frequency of gesture actions is high, the whole body movement is active, when the natural state mode is basketball shooting, the gesture actions are few, the whole body movement is relatively static, and different sampling frequencies can be determined for the basketball shooting and the natural state mode, for example, the sampling frequency when the action mode is basketball shooting is increased to 10 times/second, and when the action mode is the natural state mode, the sampling frequency is decreased to 1 time/second, so that the waste of electric quantity or untimely signal acquisition can be avoided through the dynamic adjustment of the sampling frequency.
In one implementation, the step S200 includes the following steps:
step S201, matching the action mode with a preset sampling frequency template, wherein the sampling frequency template is provided with a corresponding relation between the sampling frequency and the action mode;
and S202, determining the sampling frequency corresponding to the action mode according to the sampling frequency template.
The sampling frequency template is obtained by mapping the motion pattern and the sampling frequency. That is, in the sampling frequency template, one sampling frequency is set for each motion pattern, wherein the setting of the sampling frequency depends on the characteristics of the motion pattern. For example, a higher sampling frequency is set for active motion patterns and a lower sampling frequency is set for more static motion patterns. The sampling frequency template is favorable for helping a user to customize the sampling frequency under different action modes, and by setting the sampling frequency with different heights and matching with the action modes, an intelligent bionic hand of the user can obtain the effect of saving power consumption by controlling the sampling frequency on the premise of ensuring timely signal data acquisition.
For example, as described in the above example, when the action mode is basketball shooting, the frequency of the gesture action is high, and the whole body movement is active, then a higher sampling frequency is matched for basketball shooting in the sampling frequency template to meet the sampling requirement. And in the natural state mode, the gesture motion is less, the whole body motion is relatively static, and the natural state mode can be matched with a lower sampling frequency so as to save power consumption.
And S300, adjusting the sampling frequency according to the data of the inertial measurement unit.
Specifically, after the data of the inertial measurement unit is obtained, the sampling frequency may be adjusted according to the data of the inertial measurement unit. The inertial measurement unit data can reflect the motion posture of the intelligent bionic hand user, and the inertial measurement unit data comprises data content and data generation frequency. The data content is angular velocity and acceleration data of the three-axis attitude, and the data generation frequency is the number of times that the intelligent bionic hand acquires the data of the inertial measurement unit in unit time. Therefore, the data content reflects the motion posture of the user, and the data occurrence frequency reflects whether the motion posture is changed frequently or not. The data generation frequency is high, so that higher signal acquisition frequency is required to be ensured, and when the data generation frequency is low, the intelligent bionic hand can acquire the data of the inertia measurement unit in time by adopting lower signal acquisition frequency, so that the sampling frequency can be adjusted according to the data of the inertia measurement unit. For example, when the intelligent bionic hand acquires the current data of the inertial measurement unit of the user frequently, the data of the inertial measurement unit can be acquired in time by using a higher sampling frequency, and when the data occurrence frequency of the inertial measurement unit of the user is detected to be reduced, the data of the inertial measurement unit can be acquired in time by using a lower sampling frequency.
In one implementation, the step S300 includes the following steps:
s301, obtaining a fluctuation numerical value of the inertial measurement unit data according to the inertial measurement unit data;
and S302, adjusting the sampling frequency according to the fluctuation value of the data of the inertia measurement unit.
Specifically, the present embodiment employs an area map to reflect the fluctuation value of the inertial measurement unit data. The area graph is also called a region graph and is formed on the basis of a line graph, the region between a broken line and an independent variable coordinate axis in the line graph is filled with color or texture, so that a filled region is called an area, the filling of the color can better highlight trend information, and the area graph is used for emphasizing the degree of change of the quantity along with time and is used for representing trends and relations instead of conveying specific values. In this embodiment, the area map of the fluctuation value of the data of the inertial measurement unit can reflect the fluctuation condition of the data of the inertial measurement unit, and the larger the painted area in each time unit is, the larger the fluctuation value of the data of the inertial measurement unit in unit time is, that is, the higher the frequency of the data of the inertial measurement unit occurs. The smaller the painted area per time unit, the smaller the value of fluctuation of the data representing the inertial measurement unit per unit time, i.e., the lower the frequency of occurrence of the data representing the inertial measurement unit. Therefore, the frequency of the data generation of the inertia measurement unit can be reflected visually through the painted area in the area diagram.
For example, as shown in fig. 2, the horizontal axis of the area graph is a time axis, the vertical axis represents the number of times of occurrence of the data of the inertial measurement unit, and the painted area in the area graph represents the fluctuation value of the data of the inertial measurement unit. Therefore, after the area graph is drawn according to the data of the inertia measurement unit, the sampling frequency can be adjusted according to the size of the painted area in the area graph.
In one implementation, the step S302 includes the following steps:
step S3021, if the fluctuation value of the data of the inertial measurement unit is greater than a first fluctuation threshold, increasing the sampling frequency;
step S3022, if the fluctuation value of the data of the inertial measurement unit is smaller than a second fluctuation threshold, reducing the sampling frequency.
In this embodiment, the first and second fluctuation thresholds are painted area thresholds of the data area map of the inertia measurement unit in unit time, and the first fluctuation threshold is greater than the second fluctuation threshold, where a fluctuation value of the data of the inertia measurement unit greater than the first fluctuation threshold represents a high occurrence frequency of the data of the inertia measurement unit, and the sampling frequency needs to be increased to obtain the sampling signal in time, and a fluctuation value of the data of the inertia measurement unit less than the second fluctuation threshold represents a low occurrence frequency of the data of the inertia measurement unit, so that the sampling frequency can be reduced to save electric quantity. In this embodiment, the color-coated area of the data area diagram of the inertial measurement unit represents the fluctuation value of the data of the inertial measurement unit, that is, the sampling frequency is adjusted according to the comparison result between the color-coated area of the data area diagram of the inertial measurement unit and the first and second fluctuation thresholds.
Specifically, as described in the above example, if the first fluctuation threshold is 15, the second fluctuation threshold is 10, and the unit time is 1 in one time unit on the abscissa axis, and in fig. 2, the painted area of the area map is smaller than the second fluctuation threshold 10 in the time period t2-t1, the sampling frequency is decreased. And in the time period from t3 to t2, if the color painting area is larger than the second fluctuation threshold 10 and smaller than the first fluctuation threshold 15, the sampling frequency is not changed, and in the time period from t5 to t4, if the color painting area is larger than the first fluctuation threshold 15, the sampling frequency is increased.
In one implementation, the method further comprises the steps of:
step S303, if the action mode is a natural state mode, comparing the duration of the natural state mode with a preset duration threshold;
step S304, if the duration is greater than the preset duration threshold, reducing the sampling frequency to be a limit sampling frequency corresponding to the natural state mode, where the limit sampling frequency is a preset lowest sampling frequency value corresponding to the natural state mode.
Specifically, when the action mode enters the natural state mode, the sampling frequency corresponding to the natural state mode is determined according to the sampling frequency template, and sampling is performed according to the sampling frequency. And meanwhile, recording the time of entering the natural state mode, comparing the time of the user for keeping the natural state mode with a duration threshold, and adjusting the sampling frequency according to the comparison result. If the duration time of the natural state mode exceeds a preset duration time threshold, it can be considered that the gesture actions of the user are few in a certain time period, the whole body movement is in a relatively static state, the generated signal data are few, and the sampling frequency can be reduced to the minimum, namely, the limit sampling frequency, so that the power consumption is further saved. And when the action mode of the user is not the natural state mode any more, re-determining the sampling frequency corresponding to the action mode according to the sampling frequency template.
For example, the preset duration threshold is 10 minutes, when the action mode of the user is detected to be the sitting posture natural state mode, the sampling frequency is determined to be 10 times/minute according to the sampling frequency template, and when the time that the user is in the sitting posture natural state mode reaches 10 minutes, the sampling frequency is set to be the limit sampling frequency of 1 time/minute, so that the power consumption is saved and the battery endurance time is prolonged on the premise that the minimum sampling requirement is fully met by reducing the sampling frequency. When the situation that the user exits the sitting posture natural state mode and enters the basketball playing action mode is detected, the sampling frequency is determined to be 600 times/minute according to the sampling frequency template, and at the moment, the intelligent bionic hand performs sampling according to the sampling frequency of 600 times/minute.
Exemplary devices
Based on the above embodiment, as shown in fig. 3, the present invention also discloses a sampling frequency control device for electromyographic equipment, wherein the device includes:
the motion mode acquisition module 10 is configured to acquire gesture electromyogram signal data and inertial measurement unit data, and determine a motion mode according to the gesture electromyogram signal data and the inertial measurement unit data;
the sampling frequency acquisition module 20 is configured to determine a sampling frequency according to the motion mode, where the sampling frequency is used to determine a frequency for acquiring the gesture electromyographic signal data and the inertial measurement unit data;
and the sampling frequency adjusting module 30 is configured to adjust the sampling frequency according to the data of the inertial measurement unit.
In one implementation, the motion pattern obtaining module 10 includes:
the action potential information acquisition unit is used for acquiring the electromyographic signal data and analyzing the electromyographic signal data to obtain action potential information corresponding to the electromyographic signal data;
the gesture action determining unit is used for determining a gesture action corresponding to the action potential information according to the action potential information;
the motion attitude acquisition unit is used for acquiring the data of the inertial measurement unit and analyzing the data of the inertial measurement unit to obtain a motion attitude corresponding to the data of the inertial measurement unit;
and the motion mode determining unit is used for determining the motion mode according to the gesture motion and the motion posture.
In one implementation, the motion mode determining unit includes:
a sitting posture natural state mode determining subunit, configured to determine that the action mode is the natural state mode if the motion posture is a sitting posture and the gesture action is a natural placement state of the sitting posture;
a standing posture natural state mode determining subunit, configured to determine that the motion mode is the natural state mode if the motion posture is a standing posture and the gesture motion is a natural placement state of the standing posture;
and the walking posture natural state mode determining subunit is used for determining that the action mode is the natural state mode if the motion posture is the walking posture and the gesture motion is the natural placing state of the walking posture.
In one implementation, the sampling frequency acquisition module 20 includes:
the sampling frequency matching unit is used for matching the action mode with a preset sampling frequency template, and the sampling frequency template is provided with a corresponding relation between the sampling frequency and the action mode;
and the sampling frequency acquisition unit is used for determining the sampling frequency corresponding to the action mode according to the sampling frequency template.
In one implementation, the sampling frequency adjustment module 30 includes:
the fluctuation numerical value acquisition unit is used for acquiring the fluctuation numerical value of the data of the inertial measurement unit according to the data of the inertial measurement unit;
and the sampling frequency adjusting unit is used for adjusting the sampling frequency according to the fluctuation numerical value of the data of the inertia measuring unit.
In one implementation, the sampling frequency adjustment unit includes:
the sampling frequency increasing subunit is used for increasing the sampling frequency if the fluctuation numerical value of the data of the inertia measurement unit is greater than a first fluctuation threshold value;
and the sampling frequency reduction subunit is used for reducing the sampling frequency if the fluctuation numerical value of the data of the inertia measurement unit is smaller than a second fluctuation threshold value.
In one implementation, the apparatus further includes:
a duration comparison module 40, configured to compare, if the motion mode is a natural state mode, a duration of the natural state mode with a preset duration threshold;
and the limit sampling frequency setting module 50 is configured to reduce the sampling frequency to be the limit sampling frequency corresponding to the natural state mode if the duration is greater than the preset duration threshold, where the limit sampling frequency is a preset lowest sampling frequency value corresponding to the natural state mode.
Based on the above embodiments, the present invention further provides an intelligent bionic hand, and a schematic block diagram thereof can be shown in fig. 4. The intelligent bionic hand comprises a processor and a memory which are connected through a system bus. Wherein the processor of the intelligent bionic hand is used for providing computing and control capability. The memory of the intelligent bionic hand 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 intelligent bionic hand is used for being connected and communicated with an external terminal through a network. The computer program is executed by a processor to implement a sampling frequency control method of an electromyographic device.
It will be understood by those skilled in the art that the block diagram shown in fig. 4 is a block diagram of only a portion of the structure associated with the inventive arrangements and is not intended to limit the intelligent biomimetic hand to which the inventive arrangements may be applied, as the particular intelligent biomimetic hand may include more or less components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, an intelligent bionic hand is provided, which includes a memory, a processor and a sampling frequency control program of an electromyographic device stored in the memory and capable of running on the processor, and when the processor executes the sampling frequency control program of the electromyographic device, the following operation instructions are implemented:
acquiring gesture electromyographic signal data and inertia measurement unit data, and determining an action mode according to the gesture electromyographic signal data and the inertia measurement unit data, wherein the inertia measurement unit data is used for reflecting a motion posture of a user in a three-dimensional space;
determining a sampling frequency according to the action mode, wherein the sampling frequency is the frequency for acquiring the gesture electromyographic signal data and the inertia measurement unit data;
and adjusting the sampling frequency according to the data of the inertial measurement unit.
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 may be implemented by hardware instructions of a computer program, which may be stored in a non-volatile computer-readable storage medium, and when executed, may 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 invention discloses a sampling frequency control method of a myoelectric device, an intelligent bionic hand and a storage medium, wherein the method comprises the following steps: acquiring gesture electromyographic signal data and inertial measurement unit data, and determining an action mode according to the gesture electromyographic signal data and the inertial measurement unit data; according to the action mode, determining a sampling frequency, wherein the sampling frequency is used for determining the frequency for acquiring the gesture electromyographic signal data and the inertia measurement unit data; and adjusting the sampling frequency according to the data of the inertial measurement unit. The invention can determine different sampling frequencies according to different action modes and adjust the sampling frequencies according to the data of the inertia measurement unit, thereby saving power consumption and prolonging the battery endurance time 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 (7)
1. A sampling frequency control method of an electromyographic device, the method comprising:
acquiring gesture electromyographic signal data and inertia measurement unit data, and determining a motion mode according to the gesture electromyographic signal data and the inertia measurement unit data, wherein the inertia measurement unit data is used for reflecting the motion posture of a user in a three-dimensional space;
determining a sampling frequency according to the action mode, wherein the sampling frequency is the frequency for acquiring the gesture electromyographic signal data and the inertia measurement unit data;
adjusting the sampling frequency according to the inertial measurement unit data;
the acquiring gesture electromyographic signal data and inertial measurement unit data, and determining an action mode according to the gesture electromyographic signal data and the inertial measurement unit data comprise:
acquiring the electromyographic signal data, and analyzing the electromyographic signal data to obtain action potential information corresponding to the electromyographic signal data;
determining a gesture action corresponding to the action potential information according to the action potential information;
acquiring the data of the inertial measurement unit, and analyzing the data of the inertial measurement unit to obtain a motion attitude corresponding to the data of the inertial measurement unit;
determining the motion mode according to the gesture motion and the motion posture;
the determining the motion pattern from the gesture motion and the motion gesture comprises:
if the motion posture is a sitting posture and the gesture motion is a natural placing state of the sitting posture, determining that the motion mode is a natural state mode;
if the motion posture is a standing posture and the gesture motion is a natural placing state of the standing posture, determining that the motion mode is the natural state mode;
if the motion posture is a walking posture and the gesture motion is a natural placing state of the walking posture, determining that the motion mode is the natural state mode;
the method further comprises the following steps:
if the action mode is a natural state mode, comparing the duration of the natural state mode with a preset duration threshold;
if the duration is greater than the preset duration threshold, reducing the sampling frequency to be a limit sampling frequency corresponding to the natural state mode, wherein the limit sampling frequency is a preset lowest sampling frequency value corresponding to the natural state mode.
2. The method for controlling a sampling frequency of an electromyographic device according to claim 1, wherein the determining a sampling frequency according to the motion pattern comprises:
matching the action mode with a preset sampling frequency template, wherein the sampling frequency template is provided with a corresponding relation between the sampling frequency and the action mode;
and determining the sampling frequency corresponding to the action mode according to the sampling frequency template.
3. The method for controlling sampling frequency of electromyographic equipment according to claim 1, wherein the adjusting the sampling frequency according to the inertial measurement unit data comprises:
obtaining a fluctuation numerical value of the inertial measurement unit data according to the inertial measurement unit data;
and adjusting the sampling frequency according to the fluctuation value of the data of the inertial measurement unit.
4. The myoelectric device sampling frequency control method according to claim 3, wherein the adjusting the sampling frequency according to the fluctuation value of the inertial measurement unit data includes:
if the fluctuation value of the data of the inertia measurement unit is larger than a first fluctuation threshold value, increasing the sampling frequency;
and if the fluctuation value of the data of the inertia measurement unit is smaller than a second fluctuation threshold value, reducing the sampling frequency.
5. A sampling frequency control apparatus of an electromyographic device, the apparatus comprising:
the motion mode acquisition module is used for acquiring gesture electromyographic signal data and inertia measurement unit data and determining a motion mode according to the gesture electromyographic signal data and the inertia measurement unit data;
the sampling frequency acquisition module is used for determining a sampling frequency according to the action mode, and the sampling frequency is used for determining the frequency for acquiring the gesture electromyographic signal data and the inertia measurement unit data;
the sampling frequency adjusting module is used for adjusting the sampling frequency according to the data of the inertial measurement unit;
the action mode acquisition module comprises:
the action potential information acquisition unit is used for acquiring the electromyographic signal data and analyzing the electromyographic signal data to obtain action potential information corresponding to the electromyographic signal data;
the gesture action determining unit is used for determining a gesture action corresponding to the action potential information according to the action potential information;
the motion attitude acquisition unit is used for acquiring the data of the inertial measurement unit and analyzing the data of the inertial measurement unit to obtain a motion attitude corresponding to the data of the inertial measurement unit;
a motion mode determination unit for determining the motion mode according to the gesture motion and the motion posture;
the motion mode determination unit includes:
a sitting posture natural state mode determining subunit, configured to determine that the action mode is the natural state mode if the motion posture is a sitting posture and the gesture action is a natural placement state of the sitting posture;
a standing posture natural state mode determining subunit, configured to determine that the motion mode is the natural state mode if the motion posture is a standing posture and the gesture motion is a natural placement state of the standing posture;
a walking posture natural state mode determining subunit, configured to determine that the action mode is the natural state mode if the motion posture is a walking posture and the gesture motion is a natural placement state of the walking posture;
the device further comprises:
the duration comparison module is used for comparing the duration of the natural state mode with a preset duration threshold if the action mode is the natural state mode;
and the limit sampling frequency setting module is used for reducing the sampling frequency to be the limit sampling frequency corresponding to the natural state mode if the duration is greater than the preset duration threshold, and the limit sampling frequency is the preset lowest sampling frequency value corresponding to the natural state mode.
6. An intelligent bionic hand, which is characterized by comprising a memory, a processor and a program of a sampling frequency control method of an electromyographic device stored in the memory and operable on the processor, wherein the processor, when executing the program of the sampling frequency control method of the electromyographic device, implements the steps of the sampling frequency control method of the electromyographic device according to any one of claims 1 to 4.
7. A computer-readable storage medium, characterized in that it has stored thereon a program of a sampling frequency control method of an electromyographic device, which when executed by a processor, carries out the steps of the sampling frequency control method of an electromyographic device according to any one of claims 1 to 4.
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