Human-computer interaction method and device, computer equipment and storage medium

Abstract

The invention relates to the field of man-machine interaction, and discloses a man-machine interaction method and device, computer equipment and a storage medium. First positioning information acquired by hand wearing equipment and hand image information acquired by eye wearing equipment are acquired, and second positioning information in the hand image information is detected; according to the pre-acquired parameter information of the camera device and the hand image information, the position information of the hand in the space can be accurately determined; therefore, the first positioning information can be supplemented according to the second positioning information and the spatial position information to obtain accurate hand input information, the accuracy of three-dimensional input information is improved, and the power consumption of the wearable device is reduced.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a human-computer interaction method, device, computer equipment and storage medium. Background Art

[0002] With the development of computer technology, in extended reality (XR) applications such as virtual reality (VR) and augmented reality (AR), unlike traditional mouse devices that only need to locate the plane position, called 2 degrees of freedom, namely the x and y directions in the plane, XR input devices need to have spatial positioning capabilities, involving 6 degrees of freedom input, that is, the spatial position (XYZ) and spatial angle (pitch, roll, heading) of the input device need to be located. The current mainstream solution uses handle devices for input, which has the problems of large size, heavy weight and high power consumption. Naked hand visual tracking can also be used for positioning (identifying the position and angle of the hand), but there are problems such as low reliability and limited observation range. The above input devices are not practical enough in the field of AR applications, and cannot take into account the size, weight, power consumption and functional completeness. Summary of the invention

[0003] In view of this, the present invention provides a human-computer interaction method, apparatus, computer device and storage medium to solve the problems of high power consumption and low reliability of tracking devices.

[0004] In a first aspect, the present invention provides a human-computer interaction method, which is applied to a wearable device, wherein the wearable device includes a hand-wearable device and an eye-wearable device, and the method includes:

[0005] Collecting first positioning information of the hand-worn device, where the first positioning information includes first pitch information, first roll information, and first yaw information;

[0006] Use eye-wearing devices to collect hand image information;

[0007] Detecting second positioning information in the hand image information, where the second positioning information includes second yaw information and spatial position information of the hand, where the spatial position information is determined based on pre-acquired parameter information of the camera device and the hand image information;

[0008] The first positioning information is supplemented according to the second yaw information and the spatial position information to obtain hand input information.

[0009] Beneficial effect: the first positioning information obtained by the hand-worn device and the hand image information collected by the eye-worn device are collected, and the second positioning information in the hand image information is detected. According to the pre-acquired parameter information of the camera device and the hand image information, the position information of the hand in space can be accurately determined; the first positioning information can be supplemented according to the second yaw information and the spatial position information to obtain accurate hand input information, thereby improving the accuracy of the three-dimensional input information and reducing the power consumption of the wearable device.

[0010] In an optional embodiment, the method further includes:

[0011] Obtain the first positioning information set of the hand-worn device within a preset historical time period and the measured information at the current moment, where the last moment of the preset historical time period is the moment before the current moment;

[0012] Determine an average speed within a preset historical time period according to each first positioning information;

[0013] Determine the prediction information at the current moment according to the first positioning information corresponding to the last moment in the preset historical time period and the average speed;

[0014] Determine the current positioning information based on measured information, predicted information and preset thresholds.

[0015] In an optional implementation, the hand-worn device is provided with a button, and the button is used to provide feedback on the operation result.

[0016] In an optional implementation, the hand-worn device is provided with an indicator light.

[0017] In an optional embodiment, the hand-worn device is a ring.

[0018] In an optional embodiment, the indicator light is arranged at the lower right corner of the ring face.

[0019] In a second aspect, the present invention provides a human-computer interaction device, which is applied to a wearable device, wherein the wearable device includes a hand wear device and an eye wear device, and the device includes:

[0020] An information collection module, used to collect first positioning information of the hand-worn device, where the first positioning information includes first pitch information, first roll information, and first yaw information;

[0021] An image acquisition module, used to acquire hand image information using the eyewear device;

[0022] an information detection module, configured to detect second positioning information in the hand image information, wherein the second positioning information includes second yaw information and spatial position information of the hand, wherein the spatial position information is determined according to pre-acquired parameter information of a camera device and the hand image information;

[0023] An input information determination module is used to supplement the first positioning information according to the second yaw information and the spatial position information to obtain hand input information.

[0024] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the human-computer interaction method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0025] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the human-computer interaction method of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 is a flow chart of a human-computer interaction method according to an embodiment of the present invention;

[0028] Figure 2 is a schematic diagram of a human-computer interaction method according to an embodiment of the present invention;

[0029] Figure 3 is a flow chart of another human-computer interaction method according to an embodiment of the present invention;

[0030] Figure 4 is a flow chart of a human-computer interaction method according to an embodiment of the present invention;

[0031] Figure 5 is a structural block diagram of a human-computer interaction device according to an embodiment of the present invention;

[0032] Figure 6 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0034] In terms of human-computer interaction on computing platforms, in the field of personal computers, mouse and keyboard are generally used for human-computer interaction. Corresponding to the computer (PC) flat-panel display, the mouse only needs to input two degrees of freedom (DoF) of the cursor position (up and down, left and right) on the flat-panel display, and rely on the left and right buttons to implement specific operations.

[0035] In extended reality (XR) applications such as virtual reality (VR) and augmented reality (AR), because the display has changed from 2D to 3D, the corresponding input devices have undergone a fundamental change in demand. Compared with mouse devices that only need to locate the plane position, XR input devices need to have spatial positioning capabilities, involving 6 degrees of freedom, of which the position in the world coordinate system is 3 degrees of freedom: up and down, left and right, and front and back, and the three angles in the involved coordinate system are another 3 degrees of freedom: pitch, roll, and yaw angles.

[0036] In existing VR applications, a dedicated handle device is usually used for 6DoF input. This handle needs to have an optical positioning marker or an optical automatic tracking camera. The advantages are high input accuracy, stability and reliability, but the disadvantages are large size, heavy weight and high power consumption. There are also 6DoF positioning systems based on bare hands, which use the camera on the head-mounted display (HMD) to locate both hands, determine the spatial coordinates and rotation direction of the hands, and achieve 6DoF input. The advantage of this method is that it does not require additional hardware equipment, but the disadvantage is that the operating range is limited by the observation range of the camera, the input reliability is insufficient due to self-occlusion problems, and there is no tactile feedback.

[0037] There are also input devices based solely on inertial measurement units (IMUs), which can realize input of three rotational degrees of freedom (pitch, roll, and yaw). Rays at different angles are emitted from a fixed position, pointing to the operating area on the screen, and buttons are used to confirm specific input. The advantages of this method are simplicity, compactness, and low power consumption. The disadvantages are that it does not have complete six-degree-of-freedom functions, and errors will accumulate over time in the yaw angle direction, resulting in a decrease in accuracy (time drift).

[0038] In this embodiment, a human-computer interaction method is provided. Figure 1 It is a flowchart of a human-computer interaction method according to an embodiment of the present invention. Applied to wearable devices, the wearable devices include hand-worn devices and eye-worn devices. The method of the embodiment of the present invention can be implemented by the processor of the above-mentioned wearable device or an external computer device. According to an embodiment of the present invention, an embodiment of a human-computer interaction method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0039] like Figure 1 As shown, the process of the human-computer interaction method includes the following steps:

[0040] Step S101, collecting first positioning information of the hand-worn device, where the first positioning information includes first pitch information, first roll information, and first yaw information.

[0041] For example, the wearable device on the hand may be a watch, a bracelet, or a glove. The first positioning information may collect IMU information in the wearable device on the hand. The IMU is used to obtain three angle information of pitch, roll, and yaw. Specifically,

[0042] (a) Setting the 0 point of the three angles at time T0 is calibration.

[0043] (b) At time T1, the IMU will record the angular acceleration in three directions (pitch, roll, and yaw);

[0044] (c) Integrating the angular acceleration gives the angular velocity;

[0045] (d) Integrating the angular velocity gives the angle.

[0046] Step S102: collecting hand image information using an eyewear device.

[0047] Step S103: Detect second positioning information in the hand image information, where the second positioning information includes second yaw information and spatial position information of the hand, and the spatial position information is determined based on pre-acquired parameter information of the camera device and the hand image information.

[0048] Exemplarily, an image acquisition device is provided in the eyewear device, and the image acquisition device can continuously acquire corresponding hand image information, wherein the hand image information can accurately identify the second yaw information of the corresponding hand, etc. The first yaw information acquired by the handwear device is inaccurate due to interference from the electronic device, so the first yaw information can be corrected or supplemented according to the second yaw information.

[0049] Acquire hand image information, and acquire corresponding second positioning information according to the hand image information. Specifically, the following method can be adopted:

[0050] a) The camera shoots at a frame rate of 30fps to obtain a hand image;

[0051] b) using deep learning methods to detect gestures and obtain detection frames;

[0052] c) Hand tracking using deep learning methods to obtain the gesture posture of the frame diagram b) (palm angle, pitch angle Pitch, roll angle Roll, yaw angle Yaw).

[0053] Step S104: supplement the first positioning information according to the second yaw information and the spatial position information to obtain hand input information.

[0054] For example, if the camera device uses a binocular camera to obtain the image, the spatial position XYZ of the palm is obtained by using the triangulation calculation of the binocular camera. Figure 2 The figure shows a schematic diagram of triangulation calculation using a binocular camera. If the hand image information is obtained by a monocular camera, the palm size is fixed, and the palm position XYZ relative to the camera is located based on the principle that the palm is larger when it is closer and smaller when it is farther away in the monocular camera. XYZ and Pitch, Roll, and Yaw are the palm 6Dof.

[0055] The acquisition of bare hand image information is limited by factors such as the shooting range of the camera equipment, and hand data cannot be acquired all the time. Therefore, when the corresponding hand data is acquired, the data of the hand wearable device can be supplemented according to the information recognized by the hand image data, thereby improving the accuracy of the hand wearable device data. The first positioning information can be supplemented according to the second positioning information and spatial position information to obtain the hand input information. In order to solve the problem of unstable gesture detection, when the gesture is lost in a short time (for example, 100ms), the IMU integral data is used to supplement the gesture 6DoF data.

[0056] Specifically, the IMU information (position acceleration, angular velocity) of the ring is used to perform numerical integration on the gesture 6DoF data, and the low-frequency data is interpolated into high-frequency data. For example, if the gesture output frequency is 30Hz, and a 6DoF information is originally output every 33ms, the present invention uses the IMU data to perform an integral calculation every 1ms (the integral algorithm is not included in the present invention), and expands the 30Hz data to 1000Hz data.

[0057] For example, the integration and interpolation methods are as follows:

[0058] A. Gesture 6DoF at time T0: X\Y\Z\P(itch)\R(oll)\Y(aw);

[0059] B. At T1, Nms have passed compared to T0. At this time, there is no new hand 6DoF from image tracking, but there are acceleration (Xa, Ya, Za) and angular acceleration data (Pa, Ra, Ya) recorded by N IMUs;

[0060] C. Integrate N acceleration and angular acceleration data. The integration method of discrete data is accumulation. Then the speed is:

[0061] Xv=Xa1+Xa2+...+Xan

[0062] Yv=Ya1+Ya2+...+Yan

[0063] Zv=Za1+Za2+...+Zan

[0064] Pv=Pa1+Pa2+...+Pan

[0065] Rv=Ra1+Ra2+...+Ran

[0066] Y'v=Ya1+Ya2+...+Yan

[0067] The integral of the velocity is the displacement:

[0068] Xs=Xv1+Xv2+...+Xvn

[0069] Ys=Yv1+Yv2+...+Yvn

[0070] Zs=Zv1+Zv2+...+Zvn

[0071] Ps=Pv1+Pv2+...+Pvn

[0072] Rs=Rv1+Rv2+...+Rvn

[0073] Y's=Yawv1+Yawv2+...+Yawvn

[0074] D. The palm 6DoF at T1 is:

[0075] Xt1=Xt0+Xs

[0076] Yt1=Yt0+Ys

[0077] Zt1=Zt0+Zs

[0078] Pt1=Pt0+Ps

[0079] Rt1=Rt0+Rs

[0080] Y't1=Y't0+Y's.

[0081] The human-computer interaction method provided in this embodiment collects the first positioning information obtained by the hand-worn device and the hand image information collected by the eye-worn device, detects the second positioning information in the hand image information, and accurately determines the position information of the hand in space based on the pre-acquired parameter information of the camera device and the hand image information; the first positioning information can be supplemented according to the second yaw information and the spatial position information to obtain accurate hand input information, thereby improving the accuracy of the three-dimensional input information and reducing the power consumption of the wearable device.

[0082] On the basis of the above embodiments, in order to further improve the accuracy of human-computer interaction and reduce the errors that may occur during the tracking process, Figure 3 As shown, the method also includes:

[0083] Step S105, obtaining a first positioning information set of the hand-worn device within a preset historical time period and actual measurement information at the current moment, wherein the last moment of the preset historical time period is the moment before the current moment.

[0084] For example, when predicting the data at the current moment, the previous historical data is used to predict the position at the current moment, so it is necessary to obtain data within the historical time period for prediction. Specifically, 6DoF data of 5 frames before the current moment can be obtained.

[0085] Step S106: determining an average speed in a preset historical time period according to each piece of first positioning information.

[0086] Step S107, determining the prediction information at the current moment according to the first positioning information corresponding to the last moment in the preset historical time period and the average speed.

[0087] Exemplarily, after obtaining each first positioning information, the average speed in a preset historical time period can be determined according to each first positioning information. After obtaining the first positioning information corresponding to the preset historical time period, the average speed in the preset historical time period is solved: Dv = (D1 + D2 + D3 + D4 + D5) / 5, where D contains 6 components.

[0088] Step S108, determining the current positioning information according to the measured information, the predicted information and the preset threshold;

[0089] Exemplarily, D5 is the second positioning information corresponding to the last moment in the preset historical time period, Dv is the average speed, and according to the inertia of hand movement and other relationships, the predicted information at the current moment is D5+Dv.

[0090] The measured information obtained at the current moment, that is, only the first positioning information corresponding to the current moment can be obtained. Therefore, the positioning information at the current moment can be determined according to the predicted information and the first positioning information in the measured information (three degrees of freedom, pitch, roll and yaw). When there is a deviation between the first positioning information in the predicted information and the first positioned information in the measured information, and the deviation is greater than a preset threshold, the predicted information can be used as the positioning information at the current moment. When the deviation is less than the preset threshold, the spatial position in the predicted information and the first positioning information in the measured information are used as the positioning information at the current moment.

[0091] Similarly, when the measured information obtained at the current moment includes hand image information, when the deviation value between the measured information obtained based on the hand image information and the predicted information is greater than a preset threshold, the predicted information is used as the positioning information at the current moment, which improves the accuracy of the data and avoids the problem of inaccurate positioning information caused by calculation errors and other reasons.

[0092] In a preferred embodiment, the hand-worn device is provided with a button, and the button is used to provide feedback on the operation result. The hand-worn device is equipped with a physical button or a touch button to realize the confirmation action in the human-computer interaction, and is equipped with a vibration motor to provide vibration feedback on the operation result. Effective feedback on the interaction result can be achieved during the human-computer interaction process.

[0093] In a preferred embodiment, the hand-worn device is provided with an indicator light. An infrared indicator light is installed on the hand-worn device, located in the lower right corner of the ring face. In specific application scenarios, a good observation angle of the HMD-end camera can be maintained to improve the accuracy of the ring positioning. For example, fast finger tapping input on a virtual keyboard on a physical plane can be accurately positioned using the infrared indicator light, thereby completing efficient virtual keyboard input. In a preferred embodiment, the hand-worn device is a ring, and the indicator light is provided in the lower right corner of the ring face. The following items can be provided inside the ring: IMU device, physical button / capacitive screen, wireless transmission device, infrared / visible light indicator light.

[0094] like Figure 4 As shown in the figure, it is the process of determining gesture output involved in the embodiment of the present invention, the IMU integrates and obtains the first positioning data of 3DoF, the hand image data obtained when the camera device detects the palm, and the indicator light detection instruction detects the indicator light, and obtains data at the same time. The obtained data is fused, and finally the fused data is used as the final three-dimensional input data.

[0095] When using the above wearable device for 3D input, it includes the following three modes. Specifically, (1) 3DoF power saving mode, in which only the ring 3DoF mode is turned on, the gesture 6DoF correction of the ring is only turned on at a very low frequency, and the indicator light detection mode remains off. (2) 6DoF normal mode

[0096] In normal mode, high-frequency 6DoF output is performed by integrating the above-mentioned gestures and rings, and the indicator light detection mode remains off. (3) 6DoF performance mode. In performance mode, the IMU, gesture detection, and indicator light are all in working order to cope with certain scenarios that require high input accuracy and input frequency (such as games).

[0097] In this embodiment, a human-computer interaction device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0098] This embodiment provides a human-computer interaction device, such as Figure 5 The invention is applied to a wearable device, wherein the wearable device includes a hand wearable device and an eye wearable device, and the device includes:

[0099] An information collection module, used to collect first positioning information of the hand-worn device, where the first positioning information includes first pitch information, first roll information, and first yaw information;

[0100] An image acquisition module, used to acquire hand image information using the eyewear device;

[0101] an information detection module, configured to detect second positioning information in the hand image information, wherein the second positioning information includes second yaw information and spatial position information of the hand, wherein the spatial position information is determined according to pre-acquired parameter information of a camera device and the hand image information;

[0102] An input information determination module is used to supplement the first positioning information according to the second yaw information and the spatial position information to obtain hand input information.

[0103] In some optional embodiments, the device further comprises:

[0104] A historical data acquisition module is used to acquire a first positioning information set of the hand-worn device within a preset historical time period and actual measurement information at the current moment, wherein the last moment of the preset historical time period is the moment before the current moment;

[0105] An average speed determination module, used to determine an average speed within the preset historical time period according to each first positioning information;

[0106] A prediction information determination module, used to determine the prediction information at the current moment according to the second positioning information corresponding to the last moment in a preset time period and the average speed;

[0107] The positioning information determination module determines the current positioning information based on the measured information, the predicted information and the preset threshold.

[0108] In some optional implementations, the hand-worn device is provided with buttons, and the buttons are used to provide feedback on operation results.

[0109] In some optional embodiments, the hand-worn device is provided with an indicator light.

[0110] In some optional embodiments, the hand-worn device is a ring.

[0111] In some optional embodiments, the indicator light is disposed at the lower right corner of the ring face.

[0112] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0113] The human-computer interaction device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0114] The embodiment of the present invention also provides a computer device having the above Figure 5 A human-computer interaction device is provided.

[0115] See also Figure 6 , Figure 6 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 6As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 6 A processor 10 is taken as an example.

[0116] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0117] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0118] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0119] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0120] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0121] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0122] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A human-computer interaction method, characterized in that: Applied to a wearable device, the wearable device includes a hand-worn device and an eye-worn device, and the method includes: Collecting first positioning information of the hand-worn device, where the first positioning information includes first pitch information, first roll information, and first yaw information; Collecting hand image information using the eyewear device; Detecting second positioning information in the hand image information, where the second positioning information includes second yaw information and spatial position information of the hand, where the spatial position information is determined based on pre-acquired parameter information of a camera device and the hand image information; The first positioning information is supplemented according to the second yaw information and the spatial position information to obtain hand input information.

2. The method according to claim 1, characterized in that The method further comprises: Obtaining a first positioning information set of the hand-worn device within a preset historical time period and actual measurement information at the current moment, wherein the last moment of the preset historical time period is a moment before the current moment; Determine an average speed within the preset historical time period according to each piece of first positioning information; Determining the prediction information of the current moment according to the first positioning information corresponding to the last moment in the preset historical time period and the average speed; The positioning information at the current moment is determined according to the measured information, the predicted information and a preset threshold.

3. The method according to claim 1 or 2, characterized in that: The hand-worn device is provided with a button, and the button is used to provide feedback on the operation result.

4. The method according to claim 3, characterized in that The hand-worn device is provided with an indicator light.

5. The method according to claim 4, characterized in that The hand-worn device is a ring.

6. The method according to claim 5, characterized in that The indicator light is arranged at the lower right corner of the ring face.

7. A human-computer interaction device, characterized in that: Applied to a wearable device, the wearable device includes a hand wear device and an eye wear device, and the device includes: An information collection module, used to collect first positioning information of the hand-worn device, where the first positioning information includes first pitch information, first roll information, and first yaw information; An image acquisition module, used to acquire hand image information using the eyewear device; an information detection module, configured to detect second positioning information in the hand image information, wherein the second positioning information includes second yaw information and spatial position information of the hand, wherein the spatial position information is determined according to pre-acquired parameter information of a camera device and the hand image information; An input information determination module is used to supplement the first positioning information according to the second yaw information and the spatial position information to obtain hand input information.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the human-computer interaction method according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the human-computer interaction method according to any one of claims 1 to 6.

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