Earphone system, earphone and control method thereof

Abstract

The invention provides an earphone system, an earphone and a control method thereof, the earphone system comprises a camera module, a gesture recognition module, a control module and a communication module, the camera module is used for collecting a hand image of a user; the gesture recognition module is used for recognizing a target gesture executed by the user according to the hand image; the control module is used for generating a control instruction corresponding to the target gesture; and the communication module is used for transmitting the control instruction to electronic equipment connected with the earphone, so that the electronic equipment executes an operation corresponding to the control instruction.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of earphone technology, and more specifically, to an earphone system, an earphone and a control method thereof. Background Art

[0002] With the development of wireless communication technology and AI (Artificial Intelligence) interaction technology, people's functional demands for wireless headphones are getting higher and higher.

[0003] Wireless headphones control mobile phones mainly through Bluetooth technology, allowing users to control the phone through physical buttons or touch controls to perform audio playback, calls, volume adjustment and other operations without direct contact with the phone. Summary of the invention

[0004] An object of the embodiments of the present disclosure is to provide an earphone system, an earphone and a control method thereof.

[0005] According to a first aspect of an embodiment of the present disclosure, there is provided an earphone system, including a camera module, a gesture recognition module, a control module and a communication module.

[0006] The camera module is used to collect a hand image of the user;

[0007] The gesture recognition module is used to recognize the target gesture performed by the user according to the hand image;

[0008] The control module is used to generate a control instruction corresponding to the target gesture;

[0009] The communication module is used to transmit the control instruction to an electronic device connected to the headset system, so that the electronic device performs an operation corresponding to the control instruction.

[0010] Optionally, the earphone system further includes an image preprocessing module, which is used to perform a preprocessing operation on the hand image, wherein the preprocessing operation includes noise reduction and / or contrast enhancement.

[0011] Optionally, the earphone system further includes a hand detection module, and the hand detection module is used to detect a hand area in the hand image;

[0012] The gesture recognition module is used to recognize a target gesture performed by a user according to the human hand area.

[0013] Optionally, the hand detection module is further used to eliminate hand areas whose size is smaller than or equal to a set size.

[0014] Optionally, the hand detection module is used to detect the hand area in the hand image based on skin color, shape or deep learning.

[0015] Optionally, the gesture recognition module is also used to extract hand features in the hand image; and obtain the target gesture based on the hand features and a pre-trained machine learning model.

[0016] Optionally, the gesture recognition module is further used to iterate the machine learning model according to the hand features and the target gesture.

[0017] Optionally, the earphone system further includes an environment detection module and an alarm module, wherein the environment detection module is used to detect whether the environment of the user is dangerous based on the hand image; and the alarm module is used to issue an alarm prompt when it is detected that the environment of the user is dangerous.

[0018] Optionally, the earphone system further includes a wearing detection module, and the wearing detection module is used to detect whether the earphone is in a wearing state;

[0019] The camera module is used to capture the user's hand image when the headset is in a wearing state.

[0020] According to a second aspect of the present disclosure, an earphone is provided, comprising the earphone system according to the first aspect of the present disclosure.

[0021] According to a third aspect of the present disclosure, a method for controlling an earphone is provided, comprising:

[0022] Collecting a user's hand image;

[0023] recognizing a target gesture performed by the user according to the hand image;

[0024] generating a control instruction corresponding to the target gesture;

[0025] The control instruction is transmitted to an electronic device connected to the headset, so that the electronic device performs an operation corresponding to the control instruction.

[0026] Through the embodiments of the present disclosure, a camera module and a gesture recognition module are integrated into the headset system to realize the gesture recognition function, thereby increasing the functionality of the headset system and providing a user with an interactive experience. Moreover, the user does not need to touch the headset, but only needs to perform corresponding gestures to control the electronic device through the headset system, thereby providing a more convenient user experience.

[0027] Further features and advantages of the present invention will become apparent from the following detailed description of exemplary embodiments of the present invention with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.

[0029] Figure 1 is a block diagram of a headset system according to an embodiment of the present disclosure;

[0030] Figure 2 is a block diagram of a headset system according to another embodiment of the present disclosure;

[0031] Figure 3 is a flow chart of a method for controlling an earphone according to an embodiment of the present disclosure;

[0032] Figure 4 The flowchart is an example of a method for controlling an earphone according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0033] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless otherwise specifically stated.

[0034] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0035] Technologies, methods and equipment known to persons of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods and equipment should be considered part of the specification.

[0036] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0037] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0038] The present disclosure provides an earphone system. Figure 1 As shown, the headset system 1000 may include a camera module 1100 , a gesture recognition module 1200 , a control module 1300 and a communication module 1400 .

[0039] The camera module 1100 is used to collect images of the user's hands.

[0040] The gesture recognition module 1200 is used to recognize a target gesture performed by a user according to a hand image.

[0041] The control module 1300 is used to generate a control instruction corresponding to the target gesture.

[0042] The communication module 1400 is used to transmit the control instruction to the electronic device connected to the earphone system 1000, so that the electronic device performs the operation corresponding to the control instruction.

[0043] In this embodiment, the electronic device may be connected to the headset system wirelessly or by wire, so that the headset system can communicate with the electronic device. For example, the wireless connection method may include Bluetooth connection, Wi-Fi connection, radio frequency connection, ZigBee connection, etc., and the wired connection method may include USB data cable connection, headset cable connection, etc.

[0044] In one embodiment of the present disclosure, the camera module 1100 is arranged at a position so that the camera module 1100 can capture images in front of the user and / or images on both sides of the left and right ears of the user when the earphones are worn, so that the camera module 1100 can easily capture images of the user's hands, thereby facilitating gesture recognition and control of the electronic device.

[0045] In one embodiment of the present disclosure, the gesture recognition module 1200 is also used to extract hand features in the hand image; and obtain the target gesture based on the hand features and a pre-trained machine learning model.

[0046] In this embodiment, feature extraction is the process of converting the hand region into a feature vector, and commonly used methods include at least one of a geometric feature extraction method, a texture feature extraction method, and a key point extraction method. Among them, the geometric features may include the length, angle, and area of ​​the palm of the hand, etc.; the texture features may include the texture and details of the hand skin, etc.; the key points of the hand may include the fingertips, joints, etc.

[0047] Gesture recognition is the process of classifying gestures using extracted features. The machine learning model used for gesture recognition may include at least one of a support vector machine (SVM), a hidden Markov model (HMM), and a convolutional neural network (CNN).

[0048] Support vector machine (SVM) is a commonly used machine learning algorithm suitable for small sample and high-dimensional classification problems.

[0049] Hidden Markov Model (HMM) is suitable for processing time series data, such as dynamic gesture recognition.

[0050] Convolutional Neural Network (CNN) is a deep learning model suitable for image classification tasks with high recognition accuracy.

[0051] The gesture recognition module 1200 of this embodiment uses a machine learning model to perform gesture recognition, and can determine whether the user performs a valid gesture based on the hand image, and the classification of the valid gesture when the user performs a valid gesture.

[0052] The valid gestures may also be pre-set according to the application scenario or specific needs. For example, the valid gestures may include at least one of: waving left, waving right, waving up, waving down, sliding left, sliding right, sliding up, sliding down, circling fingers, etc.

[0053] Then, the gesture recognition module 1200 can be a recognition result of the user not performing a valid gesture when the user does not perform a valid gesture, and a recognition result of the classification of the valid gestures performed by the user when the user performs a valid gesture, that is, the target gesture, such as waving left, waving right, waving up, waving down, sliding left, sliding right, sliding up, sliding down, and circling.

[0054] Through this embodiment, the headset system can support the recognition of multiple valid gestures, meet multiple control requirements for electronic devices, and improve the accuracy and robustness of gesture recognition results. Moreover, the camera module and gesture recognition module can work stably under different lighting conditions, ensuring the reliability of gesture recognition results and reducing the occurrence of misidentification and missed recognition. In addition, the efficient machine learning model can ensure the real-time performance of gesture recognition, so that the headset system can respond instantly to the gesture operations performed by the user, improving the user experience.

[0055] In this embodiment, control instructions corresponding to multiple valid gestures can be set in advance according to application scenarios or specific needs. For example, the control instruction corresponding to the valid gesture of waving to the left can be set as the control instruction for answering the call, the control instruction corresponding to the valid gesture of waving to the right can be set as the control instruction for hanging up the call, the control instruction corresponding to the valid gesture of waving upward can be set as the control instruction for playing music, the control instruction corresponding to the valid gesture of waving downward can be set as the control instruction for pausing music, the control instruction corresponding to the valid gesture of sliding to the left can be set as the control instruction for reducing the playback volume, the control instruction corresponding to the valid gesture of sliding to the right can be set as the control instruction for increasing the playback volume, the control instruction corresponding to the valid gesture of sliding upward can be set as the control instruction for switching to the previous song, the control instruction corresponding to the valid gesture of sliding downward can be set as the control instruction for switching to the next song, and the control instruction corresponding to the valid gesture of circling fingers can be set as the control instruction for activating the voice assistant.

[0056] Furthermore, when the electronic device receives a control instruction sent by the headphone system, it performs an operation corresponding to the control instruction, so that the headphone system can control the electronic device according to the user's gestures.

[0057] Through the embodiments of the present disclosure, a camera module and a gesture recognition module are integrated into the headset system to realize the gesture recognition function, thereby increasing the functionality of the headset system and providing a user with an interactive experience. Moreover, the user does not need to touch the headset, but only needs to perform corresponding gestures to control the electronic device through the headset system, thereby providing a more convenient user experience.

[0058] In one embodiment of the present disclosure, Figure 2 As shown, the headset system 1000 further includes an image preprocessing module 1500, which is used to perform preprocessing operations on the hand image, and the preprocessing operations include noise reduction and / or contrast enhancement.

[0059] In this embodiment, the hand image is preprocessed before gesture recognition, which can improve the quality of the hand image and improve the accuracy and stability of the gesture recognition results obtained subsequently based on the preprocessed hand image.

[0060] In one embodiment of the present disclosure, Figure 2 As shown, the headset system 1000 further includes a hand detection module 1600, which is used to detect a hand region in a hand image. The gesture recognition module 1200 is used to recognize a target gesture performed by a user according to the hand region.

[0061] In this embodiment, the hand detection module 1600 detects and extracts the hand region in the hand image, and the gesture recognition module 1200 recognizes the target gesture performed by the user based on the hand region.

[0062] In one embodiment of the present disclosure, the hand detection module 1600 is used to detect the hand area in the hand image based on skin color, shape or deep learning.

[0063] Detecting the hand area based on skin color utilizes the skin color model to detect the hand area in the hand image, but it is easily affected by lighting changes and background interference.

[0064] Shape-based detection of the hand area is to detect the hand area in the hand image using the shape features of the hand, such as contours, edges, etc.

[0065] Hand region based on deep learning: It uses deep learning models such as convolutional neural network (CNN) to detect the hand region in the hand image with high accuracy and robustness.

[0066] In one embodiment of the present disclosure, the hand detection module 1600 is used to remove hand regions whose size is less than or equal to a set size.

[0067] In this embodiment, the set size may be set in advance according to the farthest distance between the hand of the user wearing the headset and the camera module during the wearing process of the headset.

[0068] The hand detection module 1600 of this embodiment eliminates the hand area whose size is less than or equal to the set size, which can ensure that the hands in the hand area used for gesture recognition are the hands of the user wearing the headset, rather than the hands of other users, so as to avoid other users performing valid gestures and causing the electronic device to produce erroneous operations.

[0069] In one embodiment of the present disclosure, the gesture recognition module 1200 is also used to iterate the machine learning model according to the hand features and the target gesture.

[0070] In this embodiment, when it is recognized that the user performs any valid gesture, the machine learning model is iterated according to the hand features extracted from the hand image of the gesture recognition module 1200 and the recognized target gesture.

[0071] By iterating the machine learning model, this embodiment can enable the machine learning model to learn the personalized gesture operations of the user wearing the headset, thereby improving the recognition efficiency of the machine learning model. It can perform gesture recognition more quickly and send the control instructions corresponding to the recognized target gesture to the electronic device, so that the electronic device can respond in time, reduce gesture control delay, reduce the power consumption of the headset system, and extend the battery life of the headset system.

[0072] In one embodiment, through the highly integrated SiP (system-in-package) technology, multiple functional modules such as the image preprocessing module 1500, the hand detection module 1600, the gesture recognition module 1200 are integrated into one chip, which can reduce the size and power consumption of the headset system.

[0073] In one embodiment of the present disclosure, Figure 2 As shown, the earphone system 1000 also includes an environment detection module 1700 and an alarm module 1800. The environment detection module 1700 is used to detect whether the environment where the user is located is dangerous based on the hand image; the alarm module 1800 is used to issue an alarm prompt when it is detected that the environment where the user is located is dangerous.

[0074] In one embodiment, the environment detection module 1700 can detect whether there are high-speed objects (such as vehicles, etc.) approaching the user around the user based on the hand image captured by the camera module 1100. If so, it is determined that the environment in which the user is located is dangerous.

[0075] In another embodiment, the environment detection module 1700 may also determine that the environment in which the user is located is dangerous when it detects that the distance between the user and the dangerous object is less than or equal to a set distance based on the hand image captured by the camera module 1100.

[0076] In this embodiment, the alarm module 1800 may issue an alarm voice or a vibration reminder when the environment detection module 1700 detects that the environment in which the user is located is dangerous, so as to remind the user to stay away from the danger and ensure the user's safety.

[0077] In one embodiment of the present disclosure, Figure 2 As shown, the earphone system 1000 also includes a wearing detection module 1900, which is used to detect whether the earphone is in a wearing state; the camera module 1100 is used to capture the user's hand image when the earphone is in a wearing state.

[0078] In this embodiment, when the user is not wearing the earphone, that is, the earphone is not in a wearing state, the user will not perform gestures to control the electronic device through the earphone, and therefore, the camera module 1900 may not capture images.

[0079] Through this embodiment, the power consumption of the earphone system can be reduced.

[0080] In one embodiment of the present disclosure, the headset system may further include a radar module, the radar module is used to transmit a radar signal to the user's hand, receive a radar echo signal reflected by the user's hand, and detect the user's hand movement according to the radar echo signal. The control module generates a control instruction corresponding to the target gesture when at least one of the radar module and the gesture recognition module detects that the user performs a target gesture.

[0081] In this embodiment, the radar module may be at least one of an ultrasonic radar module, a millimeter wave radar module, a microwave radar module, and a laser radar module.

[0082] This embodiment uses multi-sensor fusion technology to improve the adaptability of the headset system in different environments and ensure that gesture recognition can work stably under various lighting, temperature and noise conditions.

[0083] The present disclosure further provides an earphone, which may include the earphone system 1000 described in any of the aforementioned embodiments.

[0084] The present disclosure also provides a method for controlling an earphone, which can be implemented by an earphone, and the earphone can be the earphone including the earphone system 1000 in the aforementioned embodiment, such as Figure 3 As shown, the method includes:

[0085] Step S3100, collecting the user's hand image.

[0086] In one embodiment of the present disclosure, before executing step S3100 to collect the user's hand image, the method may further include: detecting whether the headset is in a wearing state; and executing step S3100 to collect the user's hand image when the headset is in a wearing state.

[0087] Step S3200: identifying a target gesture performed by the user based on the hand image.

[0088] In one embodiment of the present disclosure, before executing step S3200 and identifying the target gesture performed by the user based on the hand image, the method may further include: performing a preprocessing operation on the hand image, the preprocessing operation including noise reduction and / or contrast enhancement.

[0089] In one embodiment of the present disclosure, before executing step S3200, identifying the target gesture performed by the user based on the hand image, the method may further include: detecting a human hand area in the hand image; then, identifying the target gesture performed by the user based on the hand image may include: identifying the target gesture performed by the user based on the human hand area.

[0090] Based on this embodiment, before identifying the target gesture performed by the user based on the human hand region, the method may further include: eliminating the human hand region whose size is less than or equal to the set size.

[0091] In one embodiment of the present disclosure, identifying the target gesture performed by the user based on the hand image may include: extracting hand features in the hand image; and obtaining the target gesture based on the hand features and a pre-trained machine learning model.

[0092] In one embodiment of the present disclosure, the method may further include: iterating the machine learning model according to the hand features and the target gesture.

[0093] Step S3300: Generate a control instruction corresponding to the target gesture.

[0094] Step S3400: transmitting the control instruction to an electronic device connected to the headset, so that the electronic device performs an operation corresponding to the control instruction.

[0095] In one embodiment of the present disclosure, the method may further include: detecting whether the environment where the user is located is dangerous based on the hand image; and issuing an alarm prompt when it is detected that the environment where the user is located is dangerous.

[0096] Figure 4 FIG. 1 is a flow chart of an example of a method for controlling a headset according to an embodiment of the present disclosure. Figure 4 As shown, the control method of the headset may include:

[0097] Step S4001, collecting the user's hand image.

[0098] Step S4002: pre-processing the hand image.

[0099] Step S4003, detecting the human hand region in the hand image, and eliminating the human hand region whose size is less than or equal to the set size.

[0100] Step S4004, extracting hand features in the hand area.

[0101] Step S4005, obtaining the target gesture based on the hand features and the pre-trained machine learning model.

[0102] Step S4006: Generate a control instruction corresponding to the target gesture.

[0103] Step S4007: transmitting the control instruction to an electronic device connected to the headset, so that the electronic device performs an operation corresponding to the control instruction.

[0104] The present invention may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present invention.

[0105] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0106] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0107] The computer program instructions for performing the operation of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present invention.

[0108] Various aspects of the present invention are described herein with reference to the flow charts and / or block diagrams of the methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each box of the flow chart and / or block diagram and the combination of each box in the flow chart and / or block diagram can be implemented by computer-readable program instructions.

[0109] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0110] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0111] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a part of a module, a program segment or an instruction, and a part of the module, a program segment or an instruction contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that it is equivalent to implement it by hardware, implement it by software, and implement it by combining software and hardware.

[0112] Embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the marketplace, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the present invention is defined by the appended claims.

Claims

1. A headphone system, characterized in that: Including camera module, gesture recognition module, control module and communication module, The camera module is used to collect a hand image of the user; The gesture recognition module is used to recognize the target gesture performed by the user according to the hand image; The control module is used to generate a control instruction corresponding to the target gesture; The communication module is used to transmit the control instruction to an electronic device connected to the headset system, so that the electronic device performs an operation corresponding to the control instruction.

2. The earphone system according to claim 1, characterized in that The headset system further includes an image preprocessing module, which is used to perform a preprocessing operation on the hand image, wherein the preprocessing operation includes noise reduction and / or contrast enhancement.

3. The earphone system according to claim 1, characterized in that The headset system further includes a human hand detection module, and the human hand detection module is used to detect a human hand area in the hand image; The gesture recognition module is used to recognize a target gesture performed by a user according to the human hand area.

4. The earphone system according to claim 3, characterized in that The hand detection module is also used to remove hand areas whose size is smaller than or equal to a set size.

5. The earphone system according to claim 1, characterized in that The gesture recognition module is also used to extract hand features in the hand image; and obtain the target gesture based on the hand features and a pre-trained machine learning model.

6. The earphone system according to claim 5, characterized in that The gesture recognition module is also used to iterate the machine learning model according to the hand features and the target gesture.

7. The earphone system according to claim 1, characterized in that The headset system also includes an environment detection module and an alarm module. The environment detection module is used to detect whether the environment of the user is dangerous based on the hand image; the alarm module is used to issue an alarm prompt when it is detected that the environment of the user is dangerous.

8. The earphone system according to claim 1, characterized in that The earphone system further comprises a wearing detection module, wherein the wearing detection module is used to detect whether the earphone is in a wearing state; The camera module is used to capture the user's hand image when the headset is in a wearing state.

9. A headset, characterized in that: Comprising an earphone system according to any one of claims 1 to 8.

10. A method for controlling an earphone, characterized in that: include: Collecting a user's hand image; recognizing a target gesture performed by the user according to the hand image; generating a control instruction corresponding to the target gesture; The control instruction is transmitted to an electronic device connected to the headset, so that the electronic device performs an operation corresponding to the control instruction.

Images