CN113977589B - Gesture recognition threshold adjusting method and device and storage medium - Google Patents
Gesture recognition threshold adjusting method and device and storage medium Download PDFInfo
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- CN113977589B CN113977589B CN202111587617.4A CN202111587617A CN113977589B CN 113977589 B CN113977589 B CN 113977589B CN 202111587617 A CN202111587617 A CN 202111587617A CN 113977589 B CN113977589 B CN 113977589B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1602—Programme controls characterised by the control system, structure, architecture
- B25J9/161—Hardware, e.g. neural networks, fuzzy logic, interfaces, processor
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J13/00—Controls for manipulators
- B25J13/08—Controls for manipulators by means of sensing devices, e.g. viewing or touching devices
- B25J13/087—Controls for manipulators by means of sensing devices, e.g. viewing or touching devices for sensing other physical parameters, e.g. electrical or chemical properties
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
- B25J9/1661—Programme controls characterised by programming, planning systems for manipulators characterised by task planning, object-oriented languages
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
- G06F3/015—Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/017—Gesture based interaction, e.g. based on a set of recognized hand gestures
Abstract
The invention discloses a gesture recognition threshold value adjusting method, a gesture recognition threshold value adjusting device and a storage medium, wherein the method comprises the following steps: acquiring electromyographic signal data in a preset time period, and determining gesture actions based on the electromyographic signal data; determining frequency information corresponding to the gesture actions according to the gesture actions, wherein the frequency information is used for reflecting the frequency of the gesture actions occurring in the preset time period; and adjusting the recognition threshold corresponding to the gesture action according to the frequency information. According to the invention, the frequency information corresponding to the gesture action in the preset time period can be compared with the preset frequency threshold, and the recognition threshold corresponding to the gesture action is adjusted according to the comparison result of the frequency information and the preset frequency threshold, so that the convenience and the comfort of the user using the bionic hand are improved.
Description
Technical Field
The invention relates to the technical field of robots, in particular to a gesture recognition threshold adjusting method, a gesture recognition threshold adjusting device 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 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 operation habit or operation rule of the user is not considered to recognize the gesture of the existing bionic hand when recognizing the gesture, for example, the user often makes a stretching motion of the hand in a half time period from 7 o 'clock to 7 o' clock in the morning, and the user habit in the time period is not considered to be performed by the existing bionic hand when recognizing the gesture, which results in low recognition accuracy and low recognition efficiency.
Thus, there is a need for improvements and enhancements in the art.
Disclosure of Invention
The present invention provides a gesture recognition threshold adjustment method, device and storage medium, aiming at solving the above-mentioned drawbacks of the prior art, and aims to solve the problem that the prior art does not consider the operation habit or operation rule of the user to perform recognition when recognizing a gesture, which results in low recognition accuracy and low recognition efficiency, thereby affecting the use of the user.
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 gesture recognition threshold adjustment method, where the method includes:
acquiring electromyographic signal data in a preset time period, and determining gesture actions based on the electromyographic signal data;
determining frequency information corresponding to the gesture actions according to the gesture actions, wherein the frequency information is used for reflecting the frequency of the gesture actions occurring in the preset time period;
and adjusting the recognition threshold corresponding to the gesture action according to the frequency information.
In one implementation method, the acquiring electromyographic signal data within a preset time period and determining a gesture action based on the electromyographic signal data includes:
acquiring historical electromyographic signal data;
acquiring the electromyographic signal data within the preset time period based on the historical electromyographic signal data;
and analyzing the electromyographic signal data to determine the gesture action corresponding to the electromyographic signal data.
In one implementation method, the acquiring the electromyographic signal data within the preset time period based on the historical electromyographic signal data includes:
acquiring time information corresponding to the historical electromyographic signal data;
and determining the preset time period according to the time information, and acquiring the electromyographic signal data in the preset time period.
In an implementation method, the analyzing the electromyographic signal data to determine the gesture corresponding to the electromyographic signal data includes:
analyzing a plurality of collected electromyographic signal data to obtain action potential information corresponding to each electromyographic signal data;
and determining the gesture action corresponding to each piece of action potential information according to the action potential information.
In one implementation method, the determining, according to the gesture motion, frequency information corresponding to the gesture motion includes:
distinguishing the gesture actions, and counting the occurrence times corresponding to each gesture action;
and determining frequency information corresponding to each gesture action according to the occurrence times.
In an implementation method, the adjusting, according to the frequency information, a recognition threshold corresponding to the gesture motion includes:
comparing the frequency information corresponding to each gesture action with a preset frequency threshold;
if the frequency information is larger than the frequency threshold, taking the gesture action corresponding to the frequency information larger than the frequency threshold as a high-frequency action;
and adjusting the identification threshold corresponding to the high-frequency action.
In one implementation method, the adjusting the recognition threshold corresponding to the high-frequency action includes:
acquiring an identification threshold curve corresponding to the high-frequency action;
and reducing the identification threshold in the identification threshold curve.
In a second aspect, an embodiment of the present invention further provides a gesture recognition threshold adjusting apparatus, where the apparatus includes:
the gesture action determining module is used for acquiring electromyographic signal data in a preset time period and determining a gesture action based on the electromyographic signal data;
the frequency information determining module is used for determining frequency information corresponding to the gesture action according to the gesture action, wherein the frequency information is used for reflecting the frequency of the gesture action occurring in the preset time period;
and the recognition threshold adjusting module is used for adjusting the recognition threshold corresponding to the gesture action according to the frequency information.
In a third aspect, an embodiment of the present invention further provides an intelligent bionic hand, where the intelligent bionic hand includes a memory, a processor, and a program of a gesture recognition threshold adjustment method stored in the memory and executable on the processor, and when the processor executes the program of the gesture recognition threshold adjustment method, the steps of the gesture recognition threshold adjustment method according to any one of the above-mentioned schemes are implemented.
In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores thereon a program of a gesture recognition threshold adjustment method, and when the program of the gesture recognition threshold adjustment method is executed by a processor, the steps of the gesture recognition threshold adjustment method according to any one of the above-mentioned schemes are implemented.
Has the advantages that:
compared with the prior art, the invention provides a gesture recognition threshold value adjusting method, firstly, electromyographic signal data in a preset time period is obtained, and gesture actions are determined based on the electromyographic signal data; determining frequency information corresponding to the gesture actions according to the gesture actions, wherein the frequency information is used for reflecting the frequency of the gesture actions occurring in the preset time period; and adjusting the recognition threshold corresponding to the gesture action according to the frequency information. The invention analyzes the electromyographic signal data in the preset time period to determine the gesture action in the preset time period, and adjusts the recognition threshold value of the gesture action in the preset time period, so that the intelligent bionic hand can be recognized more accurately and quickly when recognizing the specific gesture action in the specific time period. In addition, the electromyographic signal data in the preset time period can be obtained from historical use data of the user on the intelligent bionic hand, so that the intelligent bionic hand of the user can be analyzed based on the operation habit or the operation rule of the user, the identification accuracy and the identification efficiency are improved, and the convenience and the comfort of the user using the bionic hand are improved.
Drawings
Fig. 1 is a flowchart of a specific implementation of a gesture recognition threshold adjustment method according to an embodiment of the present invention.
Fig. 2 is a schematic block diagram of an apparatus of a gesture recognition threshold adjustment method according to an embodiment of the present invention.
Fig. 3 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 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. In the prior art, the operation habit or the operation rule of the user is not considered for recognition when the gesture is recognized, so that the user habit in a certain time period is not considered during gesture recognition, and the recognition accuracy is not high and the recognition efficiency is low. In order to solve the technical problem, the embodiment provides a gesture recognition threshold adjusting method, and the embodiment first obtains myoelectric signal data within a preset time period, and determines a gesture action based on the myoelectric signal data; determining frequency information corresponding to the gesture actions according to the gesture actions, wherein the frequency information is used for reflecting the frequency of the gesture actions occurring in the preset time period; and adjusting the recognition threshold corresponding to the gesture action according to the frequency information. According to the embodiment, the frequency information corresponding to the gesture action in the preset time period can be compared with the preset frequency threshold, and the identification threshold corresponding to the gesture action is adjusted according to the comparison result of the frequency information and the preset frequency threshold, so that the intelligent bionic hand of the user can identify the gesture action of the user more accurately based on the adjusted identification threshold, and the convenience and the comfort of the user using the bionic hand are improved.
For example, after acquiring the electromyographic signal data of the bionic hand user, the embodiment acquires the electromyographic signal data of the bionic hand user in a time period a based on the electromyographic signal data, analyzes a plurality of pieces of acquired electromyographic signal data in the time period a to obtain a gesture corresponding to each piece of electromyographic signal data, and adjusts the recognition threshold corresponding to the gesture according to the frequency information corresponding to each gesture. For example, the example analyzes that the gesture actions of the bionic hand user in the time period a include shaking hands and holding a pen, obtains frequency information corresponding to the shaking hands and the holding a pen according to the times of the shaking hands and the holding a pen gesture actions in the time period a, and adjusts the corresponding recognition threshold according to the frequency information of the shaking hands and the holding a pen gesture actions. Therefore, when the bionic hand of the user does high-frequency gesture motion in the time period A, the bionic hand is more easily and accurately recognized, and therefore recognition accuracy and recognition efficiency are improved.
Exemplary method
The method for adjusting the gesture recognition threshold value can be applied to terminal equipment, and the terminal equipment can be computers, mobile phones and other intelligent terminal products. In addition, the gesture recognition threshold adjustment method in this embodiment is applied to the bionic hand, so the terminal device in this embodiment may establish a communication connection with the bionic hand, or be directly set on the bionic hand to form an intelligent bionic hand.
In specific implementation, as shown in fig. 1, the method for adjusting the gesture recognition threshold in this embodiment includes the following steps:
s100, acquiring electromyographic signal data in a preset time period, and determining gesture actions based on the electromyographic signal data.
The electromyographic signal data is superposition of action potential information of a motion unit (such as an arm) in a plurality of myofibers on time and space, and because each electromyographic signal data has different action potential information, and the action potential information reflects that a surface electromyographic signal is a comprehensive effect of electrical activity of superficial muscles and nerve trunks on the surface of skin, the gesture characteristics corresponding to the action gesture made by a 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 features.
For example, when myoelectric signal data of the bionic hand user in a preset time period of 10:00AM-11:30AM are obtained as a1, a2, A3, a4 and a5, it can be determined that the gesture actions of the bionic hand user are respectively that a1 is shaking, a2 is holding a cup, A3 is holding a pen, a4 is holding a cup and a5 is holding a mouse based on the myoelectric signals. It can be seen that each electromyographic signal data may represent the same gesture action or may represent different gesture actions.
In one implementation, the determining the gesture motion based on the myoelectric signal data within the preset time period in this embodiment includes the following steps:
s101, acquiring historical electromyographic signal data;
step S102, acquiring the electromyographic signal data in the preset time period based on the historical electromyographic signal data;
step S103, analyzing the electromyographic signal data and determining the gesture action corresponding to the electromyographic signal data.
When the intelligent bionic hand is applied specifically, the obtained historical electromyographic signal data is analyzed by the intelligent bionic hand. All electromyographic signal data in a preset time period are obtained by analyzing the time information of the historical electromyographic signal data. And analyzing the electromyographic signal data within the preset time period to further obtain the gesture action corresponding to the electromyographic signal data within the preset time period.
In one implementation, a user performing an action using an intelligent bionic hand may generate electromyographic signal data. Due to the influence of the operation habit or operation rule of the user, when the user uses the bionic hand at different time intervals of a day, the occurrence frequency of a certain gesture action is related to a certain time interval, namely the frequency of the gesture action of a certain bionic hand occurring at certain time intervals is higher, and the frequency of the gesture action occurring at certain time intervals is not high. Therefore, the embodiment analyzes the collected electromyographic signal data, and links the electromyographic signal data with a preset time period. Firstly, time information of historical electromyographic signal data is analyzed to obtain all electromyographic signal data in a preset time period, and action potential information in the preset time period is also obtained. If the similarity between the action potential information and the action potential information of a certain gesture action exceeds an identification threshold, the gesture action corresponding to the action potential information in a preset time period can be determined. In a preset time period, if a plurality of pieces of action potential information exist, the gesture action corresponding to each piece of action potential information needs to be determined.
For example, after electromyographic signal data with the numbers of A1 and A2 … A100 are collected by an intelligent bionic hand in a historical day, the 100 electromyographic signal data are analyzed to obtain time information corresponding to the electromyographic signal data A1-A100. If the preset time period is determined to be 9:00AM-10:00AM, and the electromyographic signal data in the preset time period is A10-A19, the action potential information corresponding to the electromyographic signal data A10 is a10, the action potential information corresponding to the electromyographic signal data A11 is a11, and the action potential information corresponding to the electromyographic signal data A19 is a19 in sequence. And determining the gesture action corresponding to each action potential information according to the action potential information. For example, if the similarity of the action potential information a10, a11, a12 and the gesture action exceeds the recognition threshold, the gesture action corresponding to the action potential information a10, a11 and a12 is determined to be a handshake, and if the similarity of the action potential information a13 and the action potential information of any gesture action does not exceed the recognition threshold, the action potential information a13 is not determined to be any gesture action. If the similarity of the action potential information a14, a15, a16, a17, a18, a19 and the gesture action of holding the pen exceeds the recognition threshold, the gesture action corresponding to the action potential information a14, a15, a16, a17, a18 and a19 is determined to be holding the pen.
Step S200, according to the gesture action, determining frequency information corresponding to the gesture action, wherein the frequency information is used for reflecting the frequency of the gesture action occurring in the preset time period.
After the gesture action corresponding to each electromyographic signal data in the preset time period is determined, the embodiment can continuously determine the frequency information corresponding to the gesture action. The gesture actions obtained in the embodiment only reflect the occurrence frequency of the gesture actions in the preset time period, so that the operation habit and the operation rule of the user can be better approached.
In one implementation, the determining the frequency information corresponding to the gesture motion includes the following steps:
s201, distinguishing the gesture actions, and counting the occurrence frequency corresponding to each gesture action;
s202, determining frequency information corresponding to each gesture motion according to the occurrence times.
In a specific application, the same gesture may occur multiple times, for example, the handshake gesture in the above example occurs 3 times, the pen holding gesture occurs 6 times, and the undetermined gesture occurs 1 time.
For example, if the bionic hand user generates No. A1 and A2 … … A1000 electromyographic signal data in 24 hours a day, the preset time period is determined to be 4:30PM-5:55 PM. Analyzing the 1000 electromyographic signal data to obtain electromyographic signal data A501-A730 in a preset time period, and further determining action potential information corresponding to the electromyographic signal data A501-A730 as a501-a 730. And determining the gesture action corresponding to each action potential information according to the action potential information, wherein in the embodiment, the determined gesture actions are assumed to be shaking hands, holding a pen, holding a cup, typing and holding a spoon. Distinguishing the gesture actions, and counting the occurrence frequency of each gesture action, assuming that the counted result is: hold 10 times, hold pen 80 times, hold cup 5 times, type 110, hold spoon 10, and gesture action 15 times without confirmation. According to the occurrence times, the frequency information corresponding to each gesture action is determined to be 4.35% of shaking hands, 34.78% of holding pens, 2.17% of holding cups, 47.83% of typing and 4.35% of holding spoons respectively. Likewise, undetermined gesture actions do not count for the moment the frequency of occurrence.
In one implementation manner, the determining of the frequency information corresponding to the gesture motion in this embodiment includes the following steps:
and S300, adjusting the recognition threshold corresponding to the gesture action according to the frequency information.
The collected frequency information is based on the preset time period, so that the recognition threshold corresponding to the gesture action is adjusted according to the frequency information of the gesture action, the intelligent bionic hand of the user can more accurately recognize the gesture action of the user based on the adjusted recognition threshold, and the convenience and the comfort of the user using the bionic hand are improved.
In an implementation manner, when the recognition threshold corresponding to the gesture action is adjusted, the method includes the following steps:
step S301, comparing the frequency information corresponding to each gesture action with a preset frequency threshold;
step S302, if the frequency information is larger than the frequency threshold, taking the gesture action corresponding to the frequency information larger than the frequency threshold as a high-frequency action;
step S303, adjusting the recognition threshold corresponding to the high-frequency operation.
In a specific application, a plurality of determined gesture actions are possible, and the occurrence frequency of each gesture action is high or low. In this embodiment, the gesture is further divided based on the occurrence frequency of the gesture, and if the frequency information is greater than the frequency threshold, the gesture motion corresponding to the frequency information greater than the frequency threshold is taken as the high-frequency motion. Specifically, as an example, in the preset time period of 4:30PM-5:55PM, the frequency information corresponding to each gesture action is respectively 4.35% of the handshake, 34.78% of the holding pen, 2.17% of the holding cup, 47.83% of the typing, and 4.35% of the holding spoon, if the frequency threshold is 30%, that means that in the preset time period of 4:30PM-5:55PM, the occurrence frequency of the gesture actions of the handshake, holding cup, and holding spoon is less than the frequency threshold, and the occurrence frequency of the gesture actions of the holding pen and typing is greater than the frequency threshold. The hand gestures of holding the hand, the cup and the spoon are not used as high-frequency gestures, and the hand gestures of holding the pen and typing are used as high-frequency gestures. Then, in this embodiment, the recognition threshold values corresponding to the pen-holding and typing gestures in the preset time period of 4:30PM-5:55PM may be adjusted, including obtaining recognition threshold value curves corresponding to the pen-holding and typing gestures, respectively, and reducing the recognition threshold values in the respective recognition threshold value curves. Therefore, in a preset time period, the intelligent bionic hand of the user can identify the gesture action of the user more accurately based on the adjusted identification threshold, the identification of the gesture action is closer to the operation habit or the operation rule of the user, the use comfort of the user using the bionic hand is improved, and the use requirement of the user can be met.
In summary, in the embodiment, firstly, electromyographic signal data within a preset time period is acquired, and a gesture action is determined based on the electromyographic signal data; determining frequency information corresponding to the gesture actions according to the gesture actions, wherein the frequency information is used for reflecting the frequency of the gesture actions occurring in the preset time period; and adjusting the recognition threshold corresponding to the gesture action according to the frequency information. According to the embodiment, the frequency information corresponding to the gesture action in the preset time period can be compared with the preset frequency threshold, and the identification threshold corresponding to the gesture action is adjusted according to the comparison result of the frequency information and the preset frequency threshold, so that the intelligent bionic hand of the user can identify the gesture action of the user more accurately based on the adjusted identification threshold, and the convenience and the comfort of the user using the bionic hand are improved.
Exemplary devices
Based on the above embodiment, the present invention also discloses a device of a gesture recognition threshold adjustment method, as shown in fig. 2, the device includes: a signal data acquisition module 10, a gesture feature acquisition module 20, and a recognition threshold adjustment module 30. Specifically, the signal data acquiring module 10 in this embodiment is configured to acquire electromyographic signal data, and screen the electromyographic signal data to obtain effective electromyographic signal data. The gesture feature obtaining module 20 is configured to obtain, according to the effective electromyographic signal data, a gesture feature corresponding to the effective electromyographic signal data. The recognition threshold adjusting module 30 is configured to adjust the recognition threshold corresponding to the gesture according to the frequency information.
In one implementation, the gesture motion determination module 10 in this embodiment includes:
the electromyographic signal data acquisition unit is used for acquiring historical electromyographic signal data;
and the gesture action determining unit is used for determining the gesture action corresponding to the electromyographic signal data according to a preset time period.
In one implementation manner, the electromyographic signal data acquiring unit in the present embodiment includes:
the time information acquisition subunit is used for acquiring time information corresponding to the historical electromyographic signal data;
the action potential information acquisition subunit is used for acquiring a plurality of electromyographic signal data and determining action potential information corresponding to each electromyographic signal data;
in one implementation manner, the frequency information determining module 20 in this embodiment includes:
the frequency information distinguishing unit is used for distinguishing different gesture actions;
and the frequency information determining unit is used for determining the frequency information corresponding to each gesture motion.
In one implementation, the recognition threshold adjusting module 30 in this embodiment includes:
the appearance frequency acquiring unit is used for acquiring frequency information corresponding to the gesture characteristics;
the high-frequency action recognition unit is used for recognizing the gesture action with the frequency information higher than the frequency threshold;
and an identification threshold value adjusting unit for adjusting an identification threshold value of the high-frequency motion.
In one implementation manner, the high-frequency motion recognition unit in this embodiment includes:
the frequency comparison subunit is used for comparing the frequency information with a preset frequency threshold;
the high-frequency action screening subunit is used for taking the gesture action with the occurrence frequency larger than the frequency threshold value as a high-frequency action if the occurrence frequency is larger than the frequency threshold value;
in an implementation manner, the identification threshold adjusting unit in this embodiment includes:
the identification threshold curve acquisition subunit is used for acquiring an identification threshold curve corresponding to the high-frequency action;
and the identification threshold curve adjusting subunit is used for reducing the identification threshold in the identification threshold curve.
The working principle of the gesture recognition threshold adjusting device in this embodiment is the same as that described in the above method embodiment, and is not described herein again.
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. 3. 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 gesture recognition threshold adjustment method.
It will be understood by those skilled in the art that the block diagram shown in fig. 3 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, a smart bionic hand is provided, which includes a memory, a processor and a program of a gesture recognition threshold adjustment method stored in the memory and executable on the processor, and when the processor executes the program of the gesture recognition threshold adjustment method, the following operation instructions are implemented:
acquiring electromyographic signal data in a preset time period, and determining gesture actions based on the electromyographic signal data;
determining frequency information corresponding to the gesture actions according to the gesture actions, wherein the frequency information is used for reflecting the frequency of the gesture actions occurring in the preset time period;
and adjusting the recognition threshold corresponding to the gesture action according to the frequency information.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, 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 gesture recognition threshold adjusting method, a gesture recognition threshold adjusting device, an intelligent bionic hand and a storage medium, wherein the method comprises the following steps: acquiring electromyographic signal data in a preset time period, and determining gesture actions based on the electromyographic signal data; determining frequency information corresponding to the gesture actions according to the gesture actions, wherein the frequency information is used for reflecting the frequency of the gesture actions occurring in the preset time period; and adjusting the recognition threshold corresponding to the gesture action according to the frequency information. According to the invention, the frequency information corresponding to the gesture action in the preset time period can be compared with the preset frequency threshold, and the recognition threshold corresponding to the gesture action is adjusted according to the comparison result of the frequency information and the preset frequency threshold, so that the intelligent bionic hand of the user can more accurately recognize the gesture action of the user based on the adjusted recognition threshold, and the convenience and the comfort of the user using the bionic hand are improved.
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 (10)
1. A gesture recognition threshold adjustment method, the method comprising:
acquiring electromyographic signal data in a preset time period, and determining gesture actions based on the electromyographic signal data;
determining frequency information corresponding to the gesture actions according to the gesture actions, wherein the frequency information is used for reflecting the frequency of the gesture actions occurring in the preset time period;
and adjusting the recognition threshold corresponding to the gesture action according to the frequency information.
2. The gesture recognition threshold adjusting method according to claim 1, wherein the acquiring myoelectric signal data within a preset time period and determining a gesture action based on the myoelectric signal data comprises:
acquiring historical electromyographic signal data;
acquiring the electromyographic signal data within the preset time period based on the historical electromyographic signal data;
and analyzing the electromyographic signal data to determine the gesture action corresponding to the electromyographic signal data.
3. The gesture recognition threshold adjusting method according to claim 2, wherein the acquiring the electromyographic signal data within the preset time period based on the historical electromyographic signal data comprises:
acquiring time information corresponding to the historical electromyographic signal data;
and determining the preset time period according to the time information, and acquiring the electromyographic signal data in the preset time period.
4. The gesture recognition threshold adjustment method according to claim 2, wherein the analyzing the electromyographic signal data to determine the gesture action corresponding to the electromyographic signal data includes:
analyzing a plurality of collected electromyographic signal data to obtain action potential information corresponding to each electromyographic signal data;
and determining the gesture action corresponding to each piece of action potential information according to the action potential information.
5. The gesture recognition threshold adjustment method according to claim 1, wherein the determining, according to the gesture motion, frequency information corresponding to the gesture motion includes:
distinguishing the gesture actions, and counting the occurrence times corresponding to each gesture action;
and determining frequency information corresponding to each gesture action according to the occurrence times.
6. The method for adjusting the gesture recognition threshold according to claim 1, wherein the adjusting the recognition threshold corresponding to the gesture motion according to the frequency information includes:
comparing the frequency information corresponding to each gesture action with a preset frequency threshold;
if the frequency information is larger than the frequency threshold, taking the gesture action corresponding to the frequency information larger than the frequency threshold as a high-frequency action;
and adjusting the identification threshold corresponding to the high-frequency action.
7. The gesture recognition threshold adjustment method according to claim 6, wherein the adjusting the recognition threshold corresponding to the high-frequency motion includes:
acquiring an identification threshold curve corresponding to the high-frequency action;
and reducing the identification threshold in the identification threshold curve.
8. A gesture recognition threshold adjustment apparatus, the apparatus comprising:
the gesture action determining module is used for acquiring electromyographic signal data in a preset time period and determining a gesture action based on the electromyographic signal data;
the frequency information determining module is used for determining frequency information corresponding to the gesture action according to the gesture action, wherein the frequency information is used for reflecting the frequency of the gesture action occurring in the preset time period;
and the recognition threshold adjusting module is used for adjusting the recognition threshold corresponding to the gesture action according to the frequency information.
9. An intelligent bionic hand, characterized in that the intelligent bionic hand comprises a memory, a processor and a program of a gesture recognition threshold adjusting method stored in the memory and executable on the processor, and the processor implements the steps of the gesture recognition threshold adjusting method according to any one of claims 1 to 7 when executing the program of the gesture recognition threshold adjusting method.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a program of a gesture recognition threshold adjustment method, which when executed by a processor, implements the steps of the gesture recognition threshold adjustment method according to any one of claims 1-7.
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| CN114625257B (en) * | 2022-05-16 | 2022-08-16 | 浙江强脑科技有限公司 | Action recognition method and device based on electromyographic signals |
| CN114652493B (en) * | 2022-05-25 | 2022-08-30 | 深圳市心流科技有限公司 | Electromyographic signal control method and device, electromyographic equipment and storage medium |
| CN114676737B (en) * | 2022-05-26 | 2022-09-27 | 深圳市心流科技有限公司 | Dynamic regulation method for sampling frequency of electromyographic signal |
| CN114668564B (en) * | 2022-05-26 | 2022-09-20 | 深圳市心流科技有限公司 | Method for dynamically adjusting sampling frequency based on electromyographic signal data |
| CN114683292B (en) * | 2022-06-01 | 2022-08-30 | 深圳市心流科技有限公司 | Sampling frequency control method of electromyographic equipment, intelligent bionic hand and storage medium |
| CN115204242B (en) * | 2022-09-09 | 2022-12-09 | 深圳市心流科技有限公司 | Method and device for adjusting action template comparison threshold and storage medium |
| CN117149046B (en) * | 2023-10-25 | 2024-03-15 | 荣耀终端有限公司 | Interactive gesture intensity threshold adjustment method and electronic equipment |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102298442A (en) * | 2010-06-24 | 2011-12-28 | 索尼公司 | Gesture recognition apparatus, gesture recognition method and program |
| CN109189218A (en) * | 2018-08-20 | 2019-01-11 | 广州市三川田文化科技股份有限公司 | A kind of method, apparatus of gesture identification, equipment and computer readable storage medium |
| CN109283999A (en) * | 2018-07-26 | 2019-01-29 | 杭州懒陈鑫网络科技有限公司 | A kind of gesture interaction method and interactive system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8433138B2 (en) * | 2008-10-29 | 2013-04-30 | Nokia Corporation | Interaction using touch and non-touch gestures |
-
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102298442A (en) * | 2010-06-24 | 2011-12-28 | 索尼公司 | Gesture recognition apparatus, gesture recognition method and program |
| CN109283999A (en) * | 2018-07-26 | 2019-01-29 | 杭州懒陈鑫网络科技有限公司 | A kind of gesture interaction method and interactive system |
| CN109189218A (en) * | 2018-08-20 | 2019-01-11 | 广州市三川田文化科技股份有限公司 | A kind of method, apparatus of gesture identification, equipment and computer readable storage medium |
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