CN114217694A - Bionic hand and gesture control method thereof, server and storage medium - Google Patents
Bionic hand and gesture control method thereof, server and storage medium Download PDFInfo
- Publication number
- CN114217694A CN114217694A CN202210154830.4A CN202210154830A CN114217694A CN 114217694 A CN114217694 A CN 114217694A CN 202210154830 A CN202210154830 A CN 202210154830A CN 114217694 A CN114217694 A CN 114217694A
- Authority
- CN
- China
- Prior art keywords
- gesture
- gesture operation
- data
- template
- current
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Human Computer Interaction (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Probability & Statistics with Applications (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Dermatology (AREA)
- General Health & Medical Sciences (AREA)
- Neurology (AREA)
- Neurosurgery (AREA)
- User Interface Of Digital Computer (AREA)
Abstract
The invention discloses a gesture control method based on a bionic hand, a server and a storage medium, wherein the method comprises the following steps: acquiring historical data of each gesture operation of the bionic hand in a preset time period; performing statistical analysis on historical data of each gesture operation to obtain a related gesture template set of each gesture operation; acquiring current gesture operation and electromyographic data acquired after the current gesture operation, matching a related gesture template set of the current gesture operation based on the electromyographic data, and determining a target gesture template, wherein the target gesture template is the next operation of the current gesture operation. According to the embodiment of the invention, the relevant gesture template to be executed by each gesture is obtained through statistical analysis of the historical data of each gesture operation of the bionic hand in the preset time period, and then the myoelectric data acquired by the current gesture operation is intensively matched in the relevant gesture template with a reduced range, so that the next gesture after the current gesture operation is quickly matched, and the gesture control efficiency is improved.
Description
Technical Field
The invention relates to the technical field of information, in particular to a gesture control method based on a bionic hand, a server and a storage medium.
Background
A group of weak groups exist in the society, the weak groups can not work and live like normal people due to the fact that the weak groups accidentally lose upper limbs, the living experience of the weak groups is seriously influenced, the bionic hand can enable an upper limb amputation patient to execute basic common gestures based on the bionic hand, but the existing method obtains a final gesture template in a mode of matching myoelectric signals with myoelectric signals in a database, and the method has the disadvantages of large time delay and low efficiency.
Thus, there is still a need for improvement and development of the prior art.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a gesture control method based on a bionic hand, aiming at solving the problem that the upper limb amputation patient in the prior art cannot work and live through gestures as normal people.
The technical scheme adopted by the invention for solving the problems is as follows:
in a first aspect, an embodiment of the present invention provides a gesture control method based on a bionic hand, where the method includes:
acquiring historical data of each gesture operation of the bionic hand in a preset time period;
performing statistical analysis on historical data of each gesture operation to obtain a related gesture template set of each gesture operation; wherein the associated gesture template set is a plurality of gesture templates which have relevance to each gesture operation;
acquiring current gesture operation and electromyographic data acquired after the current gesture operation, matching a related gesture template set of the current gesture operation based on the electromyographic data, and determining a target gesture template, wherein the target gesture template is the next operation of the current gesture operation.
In one implementation, the statistically analyzing the historical data of each gesture operation to obtain an associated gesture template set of each gesture operation includes:
performing data cleaning on the historical data of each gesture operation to obtain preprocessed data;
calculating association probability values between each gesture operation and other gesture operations except the gesture operation in the preprocessed data based on the frequency of each gesture operation;
and determining an associated gesture template set of each gesture operation according to the associated probability value.
In one implementation, the determining an associated gesture template set for each gesture operation according to the associated probability value includes:
and when the association probability value is greater than or equal to a preset association strength threshold value, taking a gesture template corresponding to the association probability value as an association gesture template set of the gesture operation.
In one implementation manner, the obtaining of the current gesture operation and the electromyographic data acquired after the current gesture operation, and matching the associated gesture template set of the current gesture operation based on the electromyographic data, and determining the target gesture template includes:
acquiring current gesture operation and electromyographic data acquired after the current gesture operation;
and according to the mapping relation between the electromyographic data and the gesture template, searching a gesture template corresponding to the electromyographic data in a related gesture template set of the current gesture operation, and determining a target gesture template.
In a second aspect, an embodiment of the present invention further provides a gesture control apparatus based on a bionic hand, where the apparatus includes:
the historical data acquisition module of each gesture operation is used for acquiring the historical data of each gesture operation of the bionic hand in a preset time period;
the associated gesture template set acquisition module of each gesture operation is used for carrying out statistical analysis on historical data of each gesture operation to obtain an associated gesture template set of each gesture operation; wherein the associated gesture template set is a plurality of gesture templates which have relevance to each gesture operation;
the target gesture template acquisition module is used for acquiring a current gesture operation and electromyographic data acquired after the current gesture operation, matching a related gesture template set of the current gesture operation based on the electromyographic data and determining a target gesture template, wherein the target gesture template is the next operation of the current gesture operation.
In one implementation manner, the historical data is specifically position information of the bionic hand, the on-off time of the bionic hand, the use frequency of the bionic hand, the frequency of each gesture operation, and historical myoelectric data corresponding to each gesture operation.
In one implementation, the associated gesture template set obtaining module for each gesture operation includes:
the preprocessing data acquisition unit is used for carrying out data cleaning on the historical data of each gesture operation to obtain preprocessing data;
the association probability value calculation unit is used for calculating association probability values between each gesture operation and other gesture operations except the gesture operation in the preprocessed data based on the frequency of each gesture operation;
and the associated gesture template set determining subunit is used for determining an associated gesture template set of each gesture operation according to the associated probability value.
In one implementation, the associated gesture template set determination subunit includes:
and the associated gesture template set calculating unit is used for taking the gesture template corresponding to the associated probability value as the associated gesture template set of the gesture operation when the associated probability value is greater than or equal to a preset associated intensity threshold value.
In one implementation, the target gesture template acquisition module includes:
the myoelectric data acquisition unit is used for acquiring current gesture operation and myoelectric data acquired after the current gesture operation;
and the mapping unit is used for searching a gesture template corresponding to the electromyographic data in a relevant gesture template set of the current gesture operation according to the mapping relation between the electromyographic data and the gesture template, and determining a target gesture template.
In a third aspect, an embodiment of the present invention further provides a server, including a memory, and one or more programs, where the one or more programs are stored in the memory, and configured to be executed by one or more processors includes a function for executing the method for controlling a gesture based on a simulated hand according to any one of the above items.
In a fourth aspect, embodiments of the present invention further provide a non-transitory computer-readable storage medium, where instructions of the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the method for controlling gestures based on a bionic hand as described in any one of the above.
The invention has the beneficial effects that: the method comprises the steps of obtaining historical data of each gesture operation of a bionic hand in a preset time period; then, performing statistical analysis on historical data of each gesture operation to obtain a related gesture template set of each gesture operation; wherein the associated gesture template set is a plurality of gesture templates which have relevance to each gesture operation; finally, acquiring current gesture operation and electromyographic data acquired after the current gesture operation, matching a related gesture template set of the current gesture operation based on the electromyographic data, and determining a target gesture template, wherein the target gesture template is the next operation of the current gesture operation; therefore, the embodiment of the invention obtains the associated gesture template to be executed by each gesture through statistical analysis of the historical data of each gesture operation of the bionic hand in the preset time period, and then intensively matches the myoelectric data acquired by the current gesture operation in the associated gesture template with a reduced range, so that the next gesture after the current gesture operation is quickly matched, and the gesture control efficiency is improved.
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 schematic flowchart of a gesture control method based on a bionic hand according to an embodiment of the present invention.
Fig. 2 is a schematic block diagram of a bionic hand-based gesture control apparatus according to an embodiment of the present invention.
Fig. 3 is a schematic block diagram of an internal structure of a server according to an embodiment of the present invention.
Detailed Description
The invention discloses a gesture control method based on a bionic hand, a server and a storage medium, and in order to make the purpose, technical scheme and effect of the invention clearer and clearer, the invention is further described in detail by referring to the attached drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. As used herein, the term "and/or" includes all or any element and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
In the prior art, the upper limb amputation patients can not work and live through gestures like normal people, so that the quality of life of the patients is greatly reduced.
In order to solve the problems in the prior art, the embodiment of the invention obtains the associated gesture template to be executed by each gesture through statistical analysis of the historical data of each gesture operation of the bionic hand in the preset time period, and then intensively matches the myoelectric data acquired by the current gesture operation in the associated gesture template with a reduced range, so that the next gesture after the current gesture operation is quickly matched, and the gesture control efficiency is improved. During specific implementation, acquiring historical data of each gesture operation of a bionic hand in a preset time period; then, performing statistical analysis on historical data of each gesture operation to obtain a related gesture template set of each gesture operation; and finally, acquiring the current gesture operation and electromyographic data acquired after the current gesture operation, matching a related gesture template set of the current gesture operation based on the electromyographic data, and determining a target gesture template, wherein the target gesture template is the next operation of the current gesture operation.
Exemplary method
The embodiment provides a gesture control method based on a bionic hand, and the method can be applied to a server of communication technology. As shown in fig. 1 in detail, the method includes:
s100, acquiring historical data of each gesture operation of the bionic hand in a preset time period;
in this embodiment, the bionic hand comprises a bionic palm and fingers, a friction wrist, an artificial intelligence algorithm processing unit, a myoelectricity induction sensor and a battery, has 6 degrees of freedom and 10 movable joints, can realize the independent motion of 5 fingers and the cooperative operation among the fingers, meets the 23 common gestures required by upper limb amputees, and achieves the free-hand conversion among the gestures. The predetermined time periods are different periods of the day, may be every hour of the day, may be the morning, noon and afternoon of the day. The historical data is specifically position information of the bionic hand, the on-off time of the bionic hand, the use frequency of the bionic hand, the frequency of each gesture operation and historical electromyographic data corresponding to each gesture operation. The positioning information displayed by the position information of the bionic hand is the kitchen or bedroom in the home; the startup and shutdown time of the bionic hand is specifically the time when the bionic hand is turned on and the time when the bionic hand is turned off; the use frequency of the bionic hand refers to the times of executing each gesture template by the bionic hand; the frequency of each gesture operation is the number of times each gesture operation is executed; the historical electromyographic data corresponding to each gesture operation corresponds to one historical electromechanical data for each gesture operation, so that the mapping relation between the gesture operations and the electromechanical data can be obtained. Historical data of each gesture operation in a preset time period are collected and stored through a myoelectric induction sensor of the bionic hand.
After obtaining the history data of each gesture operation, the following steps as shown in fig. 1 can be executed: s200, performing statistical analysis on historical data of each gesture operation to obtain a related gesture template set of each gesture operation; wherein the associated gesture template set is a plurality of gesture templates which have relevance to each gesture operation;
in practice, the series of gesture operations may be repeated in a specific period of time, for example, wearing clothes, buttons, trousers in the morning; for example, in the getting-up period in the morning, the user can have a series of gesture operations corresponding to boiling water tap, brushing teeth and washing face, and if the gesture operation of wearing clothes is finished, the user is aware that the user may perform the operation of buckling buttons or wearing trousers. Eating at a restaurant at a fixed time, turning on the computer at the fixed time, and performing work on the computer if historical data is obtained during use of the bionic hand by the user: statistical analysis is carried out on the historical data by using the frequency of the bionic hand to be high or low and the frequency of the gesture used in the period at the specific position of the restaurant, the time of opening the bionic hand and the time of closing the bionic hand, so that an associated gesture template set of each gesture operation can be obtained, namely, a gesture template which is possibly executed subsequently by each gesture operation is obtained, and the matching range can be narrowed.
In order to obtain an associated gesture template set of each gesture operation, the statistical analysis is performed on the historical data of each gesture operation, and the obtaining of the associated gesture template set of each gesture operation includes the following steps: performing data cleaning on the historical data of each gesture operation to obtain preprocessed data; calculating association probability values between each gesture operation and other gesture operations except the gesture operation in the preprocessed data based on the frequency of each gesture operation; and determining an associated gesture template set of each gesture operation according to the associated probability value.
Specifically, some abnormal data may exist in the historical data of each gesture operation, for example, a gesture operation related to dining is recorded in a toilet, or a bionic hand is recorded to be opened at night, the user uses the bionic hand in the daytime, the frequency of data recording is 0, the data are all abnormal data, and data cleaning is required to obtain preprocessed data. The frequency of each gesture operation is different, and when the frequency of one gesture operation and the frequency of the next gesture operation which are connected together are larger, it is indicated that the possibility that the next gesture operation is executed after the previous gesture operation is executed in a certain time period is larger, for example, the total probability is 100, and when the association frequency of one gesture operation and the other gesture operation is 80 times, the association probability value of the gesture and the other gesture operation is 0.8. Therefore, by calculating the association probability value between each gesture operation and other gesture operations except the gesture operation in the preprocessed data, the associated gesture template set of each gesture operation can be determined. In this embodiment, the gesture templates of the bionic hand are classified according to 6 actions, namely five-finger grip, two-finger grip, three-finger grip, single-finger grip, and side-finger grip. If the gesture template is divided into more detailed, the number of the gesture templates is 23: the three fingers are used for single-point pinching, three-finger pinching, pen clamping 1 (the index finger and the little finger are on, the middle finger and the ring finger are on the lower side for pen clamping), two-finger clamping, holder clamping, pen clamping 2 (the index finger and the little finger are on the lower side, the middle finger and the ring finger are on the upper side for pen clamping), two-finger clamping, food finger flicks, five-finger grabbing, two-finger pinching, middle finger flicks, gesture 6, mouse gesture, four-finger grabbing, hook bending 9 (hand hook represents number 9), rabbit head gesture, five-finger opening and two-finger side pinching.
In order to obtain an associated gesture template set of each gesture operation, the determining the associated gesture template set of each gesture operation according to the associated probability value includes the following steps: and when the association probability value is greater than or equal to a preset association strength threshold value, taking a gesture template corresponding to the association probability value as an association gesture template set of the gesture operation.
Specifically, assuming that the preset association strength threshold is 0.5, when the frequency of one gesture operation and other gesture operations is 70, dividing 70 by 100 to obtain an association probability value of the gesture operation and other gesture operations, where 0.7 is greater than 0.5, and then using other gesture templates associated with the gesture operation and corresponding to the association probability value of 0.7 as an associated gesture template set of the gesture operation.
Having obtained a set of associated gesture templates for each gesture, the following steps may be performed as shown in fig. 1: s300, acquiring the current gesture operation and electromyographic data acquired after the current gesture operation, matching the relevant gesture template set of the current gesture operation based on the electromyographic data, and determining a target gesture template.
Specifically, for fast matching, after one gesture operation is performed, if gesture control is performed only according to the collected electromyogram data, if there are many gesture templates, it means that it takes much time for matching. Therefore, after the current gesture operation is obtained, in the process of executing the current gesture operation, the relevant gesture template set of the current gesture can be obtained through the background, then the myoelectric data is collected after the current gesture operation, matching is carried out on the relevant gesture template set of the current gesture operation based on the myoelectric data, namely matching is carried out in a small range, and the target gesture template can be quickly determined.
In order to determine a target gesture template, the acquiring of the current gesture operation and the electromyographic data acquired after the current gesture operation, and the matching of the associated gesture template set of the current gesture operation based on the electromyographic data include the following steps: acquiring current gesture operation and electromyographic data acquired after the current gesture operation; and according to the mapping relation between the electromyographic data and the gesture template, searching a gesture template corresponding to the electromyographic data in a related gesture template set of the current gesture operation, and determining a target gesture template.
Specifically, a current gesture operation is obtained, a gesture template set associated with the current gesture operation can be obtained through calculation in a system background, and then myoelectric data collected after the current gesture operation is obtained.
In one implementation, the target gesture template is associated with a gesture template in a gesture template set database to obtain a combined gesture. In a special case, when the left hand and the right hand of the user are amputated and both use the bionic hand, when the gesture template of the right bionic hand of the user is used for pinching chopsticks, the fact that the user is eating is very likely to mean that the left bionic hand of the user is a gesture template for holding a bowl, which can be obtained through historical data or the use habits of the user, and thus the gesture template of the left bionic hand of the user and the gesture template of the right bionic hand of the user form a combined gesture.
Exemplary device
As shown in fig. 2, an embodiment of the present invention provides a gesture control apparatus based on a bionic hand, the apparatus includes a historical data acquisition module 401 for each gesture operation, an associated gesture template set acquisition module 402 for each gesture operation, and a target gesture template acquisition module 403, where:
a historical data acquisition module 401 for each gesture operation, configured to acquire historical data of each gesture operation of the bionic hand in a preset time period;
an associated gesture template set obtaining module 402 for each gesture operation, configured to perform statistical analysis on historical data of each gesture operation to obtain an associated gesture template set for each gesture operation; wherein the associated gesture template set is a plurality of gesture templates which have relevance to each gesture operation;
a target gesture template obtaining module 403, configured to obtain a current gesture operation and myoelectric data acquired after the current gesture operation, match a gesture template set associated with the current gesture operation based on the myoelectric data, and determine a target gesture template, where the target gesture template is a next operation of the current gesture operation.
In one implementation manner, the historical data is specifically position information of the bionic hand, the on-off time of the bionic hand, the use frequency of the bionic hand, the frequency of each gesture operation, and historical myoelectric data corresponding to each gesture operation.
In one implementation, the associated gesture template set obtaining module 402 for each gesture operation includes:
the preprocessing data acquisition unit is used for carrying out data cleaning on the historical data of each gesture operation to obtain preprocessing data;
the association probability value calculation unit is used for calculating association probability values between each gesture operation and other gesture operations except the gesture operation in the preprocessed data based on the frequency of each gesture operation;
and the associated gesture template set determining subunit is used for determining an associated gesture template set of each gesture operation according to the associated probability value.
In one implementation, the associated gesture template set determination subunit includes:
and the associated gesture template set calculating unit is used for taking the gesture template corresponding to the associated probability value as the associated gesture template set of the gesture operation when the associated probability value is greater than or equal to a preset associated intensity threshold value.
In one implementation, the target gesture template obtaining module 403 includes:
the myoelectric data acquisition unit is used for acquiring current gesture operation and myoelectric data acquired after the current gesture operation;
and the mapping unit is used for searching a gesture template corresponding to the electromyographic data in a relevant gesture template set of the current gesture operation according to the mapping relation between the electromyographic data and the gesture template, and determining a target gesture template.
Based on the above embodiments, the present invention further provides a server, and a schematic block diagram thereof may be as shown in fig. 3. The server comprises a processor, a memory, a network interface, a display screen and a temperature sensor which are connected through a system bus. Wherein the processor of the server is configured to provide computing and control capabilities. The memory of the server 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 server is used for communicating with an external terminal through network connection. The computer program is executed by a processor to implement a bionic hand-based gesture control method. The display screen of the server can be a liquid crystal display screen or an electronic ink display screen, and the temperature sensor of the server is arranged in the server in advance and used for detecting the operating temperature of internal equipment.
It will be appreciated by those skilled in the art that the schematic diagram of fig. 3 is merely a block diagram of a portion of the structure associated with the inventive arrangements and is not intended to limit the servers to which the inventive arrangements may be applied, and that a particular server may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a server is provided that includes a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by one or more processors the one or more programs include instructions for: acquiring an electromyographic signal acquired by an electromyographic induction sensor of the bionic hand;
obtaining an action classification probability set of the movement intention according to the electromyographic signals;
and determining a target gesture template according to the action classification probability set of the movement intention.
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, 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 Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
In summary, the present invention discloses a gesture control method, a server and a storage medium based on a bionic hand, wherein the method comprises: acquiring historical data of each gesture operation of the bionic hand in a preset time period; performing statistical analysis on historical data of each gesture operation to obtain a related gesture template set of each gesture operation; acquiring current gesture operation and electromyographic data acquired after the current gesture operation, matching a related gesture template set of the current gesture operation based on the electromyographic data, and determining a target gesture template, wherein the target gesture template is the next operation of the current gesture operation. According to the embodiment of the invention, the relevant gesture template to be executed by each gesture is obtained through statistical analysis of the historical data of each gesture operation of the bionic hand in the preset time period, and then the myoelectric data acquired by the current gesture operation is intensively matched in the relevant gesture template with a reduced range, so that the next gesture after the current gesture operation is quickly matched, and the gesture control efficiency is improved.
Based on the above embodiments, the present invention discloses a gesture control method based on a bionic hand, and it should be understood that the application of the present invention is not limited to the above examples, and it is obvious to those skilled in the art that the above descriptions can be modified or changed, and all such modifications and changes are intended to fall within the scope of the appended claims.
Claims (12)
1. A method for gesture control based on a bionic hand, the method comprising:
acquiring historical data of each gesture operation of the bionic hand in a preset time period;
performing statistical analysis on historical data of each gesture operation to obtain a related gesture template set of each gesture operation; wherein the associated gesture template set is a plurality of gesture templates which have relevance to each gesture operation;
acquiring current gesture operation and electromyographic data acquired after the current gesture operation, matching a related gesture template set of the current gesture operation based on the electromyographic data, and determining a target gesture template, wherein the target gesture template is the next operation of the current gesture operation.
2. The method as claimed in claim 1, wherein the historical data is position information of the bionic hand, on-off time of the bionic hand, use frequency of the bionic hand, frequency of each gesture operation, and historical electromyographic data corresponding to each gesture operation.
3. The bionic hand-based gesture control method according to claim 2, wherein the statistical analysis of the historical data of each gesture operation to obtain the associated gesture template set of each gesture operation comprises:
performing data cleaning on the historical data of each gesture operation to obtain preprocessed data;
calculating association probability values between each gesture operation and other gesture operations except the gesture operation in the preprocessed data based on the frequency of each gesture operation;
and determining an associated gesture template set of each gesture operation according to the associated probability value.
4. The bionic hand-based gesture control method according to claim 3, wherein the determining of the associated gesture template set of each gesture operation according to the associated probability value comprises:
and when the association probability value is greater than or equal to a preset association strength threshold value, taking a gesture template corresponding to the association probability value as an association gesture template set of the gesture operation.
5. The bionic hand-based gesture control method according to claim 1, wherein the acquiring of the current gesture operation and the electromyographic data acquired after the current gesture operation, and matching the associated gesture template set of the current gesture operation based on the electromyographic data, and the determining of the target gesture template comprises:
acquiring current gesture operation and electromyographic data acquired after the current gesture operation;
and according to the mapping relation between the electromyographic data and the gesture template, searching a gesture template corresponding to the electromyographic data in a related gesture template set of the current gesture operation, and determining a target gesture template.
6. A bionic hand-based gesture control apparatus, the apparatus comprising:
the historical data acquisition module of each gesture operation is used for acquiring the historical data of each gesture operation of the bionic hand in a preset time period;
the associated gesture template set acquisition module of each gesture operation is used for carrying out statistical analysis on historical data of each gesture operation to obtain an associated gesture template set of each gesture operation; wherein the associated gesture template set is a plurality of gesture templates which have relevance to each gesture operation;
the target gesture template obtaining module is used for obtaining a current gesture operation and electromyographic data collected after the current gesture operation, matching a related gesture template of the current gesture operation based on the electromyographic data, and determining a target gesture template, wherein the target gesture template is the next operation of the current gesture operation.
7. The gesture control device based on the bionic hand as claimed in claim 6, wherein the historical data is specifically position information of the bionic hand, on-off time of the bionic hand, use frequency of the bionic hand, frequency of each gesture operation and historical electromyographic data corresponding to each gesture operation.
8. The bionic hand-based gesture control device according to claim 7, wherein the associated gesture template set acquisition module of each gesture operation comprises:
the preprocessing data acquisition unit is used for carrying out data cleaning on the historical data of each gesture operation to obtain preprocessing data;
the association probability value calculation unit is used for calculating association probability values between each gesture operation and other gesture operations except the gesture operation in the preprocessed data based on the frequency of each gesture operation;
and the associated gesture template set determining subunit is used for determining an associated gesture template set of each gesture operation according to the associated probability value.
9. The bionic hand-based gesture control device according to claim 8, wherein the associated gesture template set determination subunit comprises:
and the associated gesture template set calculating unit is used for taking the gesture template corresponding to the associated probability value as the associated gesture template set of the gesture operation when the associated probability value is greater than or equal to a preset associated intensity threshold value.
10. The bionic hand-based gesture control device according to claim 6, wherein the target gesture template acquisition module comprises:
the myoelectric data acquisition unit is used for acquiring current gesture operation and myoelectric data acquired after the current gesture operation;
and the mapping unit is used for searching a gesture template corresponding to the electromyographic data in a relevant gesture template set of the current gesture operation according to the mapping relation between the electromyographic data and the gesture template, and determining a target gesture template.
11. A server comprising a memory, and one or more programs, wherein the one or more programs are stored in the memory, and wherein the one or more programs being configured to be executed by the one or more processors comprises instructions for performing the method of any of claims 1-5.
12. A non-transitory computer-readable storage medium, wherein instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the method of any of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210154830.4A CN114217694A (en) | 2022-02-21 | 2022-02-21 | Bionic hand and gesture control method thereof, server and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210154830.4A CN114217694A (en) | 2022-02-21 | 2022-02-21 | Bionic hand and gesture control method thereof, server and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114217694A true CN114217694A (en) | 2022-03-22 |
Family
ID=80708989
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210154830.4A Pending CN114217694A (en) | 2022-02-21 | 2022-02-21 | Bionic hand and gesture control method thereof, server and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114217694A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114625257A (en) * | 2022-05-16 | 2022-06-14 | 浙江强脑科技有限公司 | Action recognition method and device based on electromyographic signals |
| CN114676737A (en) * | 2022-05-26 | 2022-06-28 | 深圳市心流科技有限公司 | Dynamic regulation method for sampling frequency of electromyographic signal |
| CN117827010A (en) * | 2024-03-01 | 2024-04-05 | 浙江强脑科技有限公司 | Control method and control device for bionic hand, bionic hand and storage medium |
| CN118161133A (en) * | 2024-05-13 | 2024-06-11 | 浙江强脑科技有限公司 | Method and device for evaluating gestures of bionic hand, storage medium and bionic hand |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017086533A1 (en) * | 2015-11-20 | 2017-05-26 | 동국대학교 산학협력단 | Method and system for simulating nui/nux-based vr environment game control |
| CN113199496A (en) * | 2021-04-09 | 2021-08-03 | 杭州胖力科技有限公司 | Bionic hand control device and method and electronic equipment |
| CN113946224A (en) * | 2021-12-20 | 2022-01-18 | 深圳市心流科技有限公司 | Control method and device for myoelectric gesture recognition of intelligent bionic hand and storage medium |
| CN113970968A (en) * | 2021-12-22 | 2022-01-25 | 深圳市心流科技有限公司 | Intelligent bionic hand action pre-judging method |
| CN113986017A (en) * | 2021-12-27 | 2022-01-28 | 深圳市心流科技有限公司 | Myoelectric gesture template generation method and device and storage medium |
-
2022
- 2022-02-21 CN CN202210154830.4A patent/CN114217694A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017086533A1 (en) * | 2015-11-20 | 2017-05-26 | 동국대학교 산학협력단 | Method and system for simulating nui/nux-based vr environment game control |
| CN113199496A (en) * | 2021-04-09 | 2021-08-03 | 杭州胖力科技有限公司 | Bionic hand control device and method and electronic equipment |
| CN113946224A (en) * | 2021-12-20 | 2022-01-18 | 深圳市心流科技有限公司 | Control method and device for myoelectric gesture recognition of intelligent bionic hand and storage medium |
| CN113970968A (en) * | 2021-12-22 | 2022-01-25 | 深圳市心流科技有限公司 | Intelligent bionic hand action pre-judging method |
| CN113986017A (en) * | 2021-12-27 | 2022-01-28 | 深圳市心流科技有限公司 | Myoelectric gesture template generation method and device and storage medium |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114625257A (en) * | 2022-05-16 | 2022-06-14 | 浙江强脑科技有限公司 | Action recognition method and device based on electromyographic signals |
| CN114676737A (en) * | 2022-05-26 | 2022-06-28 | 深圳市心流科技有限公司 | Dynamic regulation method for sampling frequency of electromyographic signal |
| CN117827010A (en) * | 2024-03-01 | 2024-04-05 | 浙江强脑科技有限公司 | Control method and control device for bionic hand, bionic hand and storage medium |
| CN118161133A (en) * | 2024-05-13 | 2024-06-11 | 浙江强脑科技有限公司 | Method and device for evaluating gestures of bionic hand, storage medium and bionic hand |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114217694A (en) | Bionic hand and gesture control method thereof, server and storage medium | |
| WO2021143353A1 (en) | Gesture information processing method and apparatus, electronic device, and storage medium | |
| Geethanjali | Myoelectric control of prosthetic hands: state-of-the-art review | |
| CN113970968B (en) | Intelligent bionic hand action pre-judging method | |
| Tam et al. | Intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning | |
| Liu et al. | Reduced daily recalibration of myoelectric prosthesis classifiers based on domain adaptation | |
| Orabona et al. | Model adaptation with least-squares SVM for adaptive hand prosthetics | |
| CN114707562B (en) | Electromyographic signal sampling frequency control method and device and storage medium | |
| CN107145236B (en) | Gesture recognition method and system based on wrist tendon pressure related characteristics | |
| WO2018233435A1 (en) | Multi-dimensional surface electromyographic signal based artificial hand control method based on principal component analysis method | |
| CN113977589B (en) | Gesture recognition threshold adjusting method and device and storage medium | |
| WO2021052045A1 (en) | Body movement recognition method and apparatus, computer device and storage medium | |
| CN114167995B (en) | Gesture locking method and device for bionic hand, terminal and storage medium | |
| CN114625257B (en) | Action recognition method and device based on electromyographic signals | |
| CN113946225A (en) | Gesture locking method, intelligent bionic hand, terminal and storage medium | |
| Cognolato et al. | Hand gesture classification in transradial amputees using the Myo armband classifier | |
| Srinivasan et al. | Activity recognition using actigraph sensor | |
| CN114668563B (en) | Multi-level regulation method for sampling frequency of electromyographic signals | |
| CN114652493B (en) | Electromyographic signal control method and device, electromyographic equipment and storage medium | |
| Cao et al. | A Novel Adaptive Mutation PSO Optimized SVM Algorithm for sEMG‐Based Gesture Recognition | |
| CN110991640A (en) | Method and device for determining seasoning usage amount and method and device for optimizing model | |
| Tseng et al. | Human identification with electrocardiogram | |
| CN114676737B (en) | Dynamic regulation method for sampling frequency of electromyographic signal | |
| CN114668564B (en) | Method for dynamically adjusting sampling frequency based on electromyographic signal data | |
| CN114167996B (en) | Sensor-based action pre-judging method and device and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20220322 |