CN108073283B - Hand joint calculation method and glove - Google Patents
Hand joint calculation method and glove Download PDFInfo
- Publication number
- CN108073283B CN108073283B CN201711283329.3A CN201711283329A CN108073283B CN 108073283 B CN108073283 B CN 108073283B CN 201711283329 A CN201711283329 A CN 201711283329A CN 108073283 B CN108073283 B CN 108073283B
- Authority
- CN
- China
- Prior art keywords
- vector
- sensor
- joint
- coordinate system
- palm
- 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.)
- Active
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
- G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
- G06F3/014—Hand-worn input/output arrangements, e.g. data gloves
Abstract
The invention provides a hand joint calculation method and gloves, wherein the method comprises the following steps: obtaining quaternion by fusing a middle finger first joint sensor, an index finger first joint sensor and a palm sensor, and establishing a coordinate axis vector x1 and a vector y1 of the index finger first joint sensor; transforming the vector into a geographic coordinate system vector by using quaternion multiplication, and calculating an included angle alpha of the vector and an included angle beta of the vector; and simulating the finger posture according to the alpha and the beta. The invention has the advantage of good user experience.
Description
Technical Field
The invention relates to the field of electronics and sports, in particular to a hand joint calculation method and gloves.
Background
The method is one of the main technologies for capturing motion postures in recent years, in which an Inertial Measurement Unit (IMU) closely connected with body joints is used to acquire rotation postures of local joints in a geographic space independently and further comprehensively acquire overall space postures (such as motion trends). The palm part has dense joints, strong flexibility, complex motion and various achievable actions, and can be specially used for carrying out posture capture research on the palm part. In order to obtain the complete hand motion state, two to three inertial measurement units are generally required to be arranged on each finger, corresponding to each finger joint. And one on the back of the hand to connect the knuckles. Each measurement unit typically incorporates an accelerometer or gyroscope or magnetometer to measure acceleration, angular velocity and magnetic field strength in the X, Y and Z directions, respectively. Before and even during measurement, error correction such as offset and scale needs to be carried out on the three sensors respectively, obtained data are fused, and finally the attitude of each sensor in the geographic space is obtained, wherein the attitude is generally expressed as a quaternion or an Euler angle.
After attitude data of all joints in a geographic coordinate system is obtained, the relative position relation between the lower joint and the upper joint is calculated by taking the geographic coordinate system as a medium, and the form of the whole palm can be reconstructed. Convenient and fast, the treatment effeciency is high. Quaternions, however, do not reflect well the relationship of the various joints to each other and the results are not intuitive to a layperson.
The existing attitude solution adopts quaternion direct solution. The posture data of the lower-level joint calculated by the method is only related to the directly-subordinate upper-level joint, and the motion rule of each joint, the sibling joints and some implicit relative position conditions between non-sibling joints without direct dependency relationship are ignored. In actual operation, certain errors exist in the setting and fixing of the sensor and data obtained by the sensor, the calculated posture is probably not in accordance with the natural law due to the errors, and conditions such as torsion of finger joints taking finger pointing as an axis, overlapping of positions of adjacent fingers and the like occur, so that the accuracy of the existing posture settlement method is not high, and the user experience degree is low.
Disclosure of Invention
The hand joint calculation method maps the posture data of the sensor to the hand joint angle calculation mode so as to introduce the hand limitation condition into a calculation model to ensure that the reproduced hand is in accordance with the natural law, and therefore the hand joint calculation method has the advantages of being high in accuracy and good in user experience degree.
In one aspect, a hand joint calculation method is provided, the method comprising:
obtaining a quaternion by fusing a first joint sensor of a middle finger, a first joint sensor of an index finger and a palm sensor, wherein the quaternion specifically comprises: q1, q2 and q 0;
establishing a coordinate axis vector x1 and a vector y1 of the index finger first joint sensor;
using quaternion multiplication and multiplying the vector q1y1q1 -1Variable q1x1q1 -1Conversion to geographic coordinate system vector yG1xG1；
Using quaternion multiplicationq0 -1yG1q0And q is0 -1xG1q0Transforming the geographic coordinate system vector into the vector y under the palm coordinate systemH1And xH1；
Establishing coordinate axis vectors x2 and y2 of the middle finger first joint sensor 105, and converting the vectors x2 and y2 into vectors under a palm coordinate system;
setting the vector z-axis coordinate under the palm coordinate system to zero, yH1P、xH1PAnd yH2P、xH2P(ii) a Obtain the vector
Calculating the vector xH1pAnd xH2pAngle alpha and vector yH1pAnd yH2pThe included angle beta of;
and simulating the finger posture according to the alpha and the beta.
Optionally, the method further includes:
establishing a vector of each joint sensor in each finger, converting the vector of each joint sensor into a vector of a palm sensor coordinate system, projecting the vector of the palm sensor coordinate system to an X-0-Y plane, calculating an angle between two vectors of the same joint of adjacent fingers on the X-0-Y plane, and simulating the gesture of the hand according to the angle between the two vectors of the same joint of the adjacent fingers.
In a second aspect, there is provided a glove, the glove comprising: a sensor, a processing unit, the sensor being disposed at a joint of each finger;
the processor is configured to obtain a quaternion from the fusion of the first middle finger joint sensor, the first index finger joint sensor, and the palm sensor, where the quaternion specifically includes: q1, q2 and q 0; establishing a coordinate axis vector x1 and a vector y1 of the index finger first joint sensor; using quaternion multiplication and transforming the vector to a geographic coordinate system vector; transforming the geographic coordinate system vector to a palm coordinate system lower vector by using quaternion multiplication, establishing coordinate axis vectors x2 and y2 of the middle finger first joint sensor, and converting the vectors x2 and y2 to the palm coordinate system lower vector;
the processor is used for setting the vector z-axis coordinate under the palm coordinate system to zeroTo obtain a vector yH1P、xH1PAnd yH2P、xH2P；
Calculating the vector xH1pAnd xH2pAngle alpha and vector yH1pAnd yH2pThe included angle beta of; and simulating the finger posture according to the alpha and the beta.
Optionally, the processor is further configured to establish a vector of each joint sensor in each finger, convert the vector of each joint sensor into a vector of a palm sensor coordinate system, project the vector of the palm sensor coordinate system onto an X-0-Y plane, calculate an angle between two vectors of the same joint of adjacent fingers on the X-0-Y plane, and simulate the posture of the hand according to the angle between two vectors of the same joint of adjacent fingers.
According to the technical scheme provided by each embodiment, the gesture data of the sensor are mapped to the hand joint angle in a new calculation mode, so that the constraint conditions of the hand are introduced into a calculation model to ensure that the reproduced hand is in accordance with the natural law, and the method has the advantages of accurate calculation and high user experience.
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 of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a hand joint calculation method according to a first preferred embodiment of the present invention.
Fig. 2(a) is a vector diagram of hand joints according to the first preferred embodiment of the present invention.
Fig. 2(B) is a schematic diagram of hand joint angles provided by the first preferred embodiment of the present invention.
Fig. 3 is a schematic diagram of a sensor distribution according to a first preferred embodiment of the present invention.
Fig. 4 is a schematic view of a glove structure provided by the present invention.
Fig. 5 is a schematic structural diagram of the smart glove of the present invention.
Fig. 6(a) is a vector diagram of hand joints according to the first preferred embodiment of the present invention.
Fig. 6(B) is a schematic diagram of hand joint angles provided by the first preferred embodiment of the present invention.
Detailed Description
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel, concurrently, or simultaneously. In addition, the order of the operations may be re-arranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
The term "computer device" or "computer" in this context refers to an intelligent electronic device that can execute predetermined processes such as numerical calculation and/or logic calculation by running predetermined programs or instructions, and may include a processor and a memory, wherein the processor executes a pre-stored instruction stored in the memory to execute the predetermined processes, or the predetermined processes are executed by hardware such as ASIC, FPGA, DSP, or a combination thereof. Computer devices include, but are not limited to, servers, personal computers, laptops, tablets, smart phones, and the like.
The methods discussed below, some of which are illustrated by flow diagrams, may be implemented by hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware or microcode, the program code or code segments to perform the necessary tasks may be stored in a machine or computer readable medium such as a storage medium. The processor(s) may perform the necessary tasks.
Specific structural and functional details disclosed herein are merely representative and are provided for purposes of describing example embodiments of the present invention. The present invention may, however, be embodied in many alternate forms and should not be construed as limited to only the embodiments set forth herein.
It will be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element may be termed a second element, and, similarly, a second element may be termed a first element, without departing from the scope of example embodiments. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms "a", "an" 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 herein, 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 should also be noted that, in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed substantially concurrently, or the figures may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
Referring to fig. 1, fig. 1 is a diagram of a hand joint calculation method implemented by an intelligent terminal, the method is used for calculating hand joints, which simulates the posture of a hand, as shown in fig. 2, each finger has three movable joints, and a first finger joint, a second finger joint and a third finger joint, for example, an index finger, are sequentially arranged from the palm to the fingertip as shown in fig. 2(a) 201, 202 and 203. Wherein the range of motion of joint 203 is small and there is a fixed correlation with the motion of joint 202 during most natural movements. In order to reduce the finger burden and reduce the system construction and operation cost, in an application scenario in which the joint activity has less influence, the activity of the joint is generally not tracked separately, but is estimated by using the activity of the joint 202. The sensor layout is shown as a circle in fig. 3, and no sensor is provided on the fingertip. The sensor is specifically arranged in a manner of referring to a coordinate system established at the joint in fig. 2(a), that is, the Y axis of the sensor is parallel to the direction of the finger, and the Z axis is perpendicular to the plane of the back of the finger and faces upwards.
The calculation of the angle is illustrated by the movement of the first knuckle of the index finger. As shown in fig. 2(B), the first knuckle has two degrees of freedom of movement, i.e., rotation about the z-axis and the x-axis, respectively, but not rotation about the y-axis. Rotation about the z-axis forms an angle of rotation alpha in the horizontal plane and rotation about the x-axis forms an angle of rotation beta in the vertical plane. These two angles will be calculated in turn to represent the pose of the knuckle instead of a quaternion. The calculation steps are as follows as shown in fig. 1:
step S11, a quaternion is obtained by fusing the middle finger first joint sensor 105, the index finger first joint sensor 107 and the palm sensor 100, where the quaternion may specifically include: q1, q2 and q 0.
Step S12, establishing a coordinate axis vector x1 and a vector y1 of the index finger first joint sensor 107;
step S13, multiplying q by quaternion1y1q1 -1And q is1x1q1 -1Transforming the vector into a geographic coordinate system vector yG1 xG1
Step S14, multiplying q by quaternion0 -1yG1q0And q is0 -1xG1q0Transforming the geographic coordinate system vector into the vector y under the palm coordinate systemH1And xH1
Step S15, establishing coordinate axis vector x2 and vector of the middle finger first joint sensor 105y2, converting the vector x2 and the vector y2 into the vector y under the palm coordinate systemH1And xH1
Step S16, zero-setting the vector z-axis coordinate under the palm coordinate system, and yH1P、xH1PAnd yH2P、xH2PObtain the vector
Step S17, calculating vector xH1pAnd xH2pAngle alpha and vector yH1pAnd yH2pThe included angle beta of (a).
Step S18, simulating the finger posture according to the angles α and β.
The invention provides a new calculation mode for mapping the attitude data of the sensor to the hand joint angle, so that the constraint conditions of the hand are introduced into a calculation model to ensure that the reproduced hand conforms to the natural law.
The vector diagram can be seen in fig. 6(a) or fig. 6 (B).
Optionally, after step S18, the method may further include:
establishing a vector of each joint sensor in each finger, converting the vector of each joint sensor into a vector of a palm sensor coordinate system, projecting the vector of the palm sensor coordinate system to an X-0-Y plane, calculating an angle between two vectors of the same joint of adjacent fingers on the X-0-Y plane, and simulating the gesture of the hand according to the angle between the two vectors of the same joint of the adjacent fingers.
Referring to fig. 4, fig. 4 provides a glove comprising: a sensor 401, a processing unit 402, said sensor being arranged at the joint of each finger;
a processor 402, configured to obtain a quaternion from the fusion of the first middle finger joint sensor, the first index finger joint sensor, and the palm sensor, where the quaternion specifically includes: q1, q2 and q 0; establishing a coordinate axis vector x1 and a vector y1 of the index finger first joint sensor; using quaternion multiplication and transforming the vector to a geographic coordinate system vector; transforming the geographic coordinate system vector to a palm coordinate system lower vector by using quaternion multiplication, establishing coordinate axis vectors x2 and y2 of the middle finger first joint sensor, and converting the vectors x2 and y2 to the palm coordinate system lower vector;
the processor is used for setting the vector z-axis coordinate under the palm coordinate system to zero to obtain a vector yH1P、xH1PAnd yH2P、xH2P；
Calculating the vector xH1pAnd xH2pAngle alpha and vector yH1pAnd yH2pThe included angle beta of; and simulating the finger posture according to the alpha and the beta.
Optionally, the processor is further configured to establish a vector of each joint sensor in each finger, convert the vector of each joint sensor into a vector of a palm sensor coordinate system, project the vector of the palm sensor coordinate system onto an X-0-Y plane, calculate an angle between two vectors of the same joint of adjacent fingers on the X-0-Y plane, and simulate the posture of the hand according to the angle between two vectors of the same joint of adjacent fingers.
Referring to fig. 5, fig. 5 shows a smart glove 500 according to the present invention, which includes a processor 501, a memory 502, a transceiver 503, a sensor 505, and a bus 504. The transceiver 503 is used for transmitting and receiving data to and from an external device. The number of processors 501 in smart glove 500 may be one or more. In some embodiments of the invention, the processor 501, the memory 502, the sensor 505, and the transceiver 503 may be connected by a bus system or other means. Smart glove 500 may be used to perform the method shown in fig. 1. With regard to the meaning and examples of the terms involved in the present embodiment, reference may be made to the embodiment corresponding to fig. 6. And will not be described in detail herein.
A transceiver 503 for receiving or transmitting commands;
the sensor 505 is used for detecting data of finger joints, and the sensor is a plurality of sensors and is arranged at the finger joints, and particularly, the arrangement diagram shown in fig. 3 can be referred to.
Wherein the memory 502 stores program code therein. The processor 501 is arranged to call program code stored in the memory 502 for performing the method as shown in fig. 1.
It should be noted that the processor 501 may be a single processing element or may be a general term for multiple processing elements. For example, the Processing element may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement embodiments of the present invention, such as: one or more microprocessors (digital signal processors, DSPs), or one or more Field Programmable Gate Arrays (FPGAs).
The memory 503 may be a single storage device or a combination of multiple storage elements, and is used for storing executable program codes or parameters, data, etc. required by the running device of the application program. And the memory 503 may include a Random Access Memory (RAM) or a non-volatile memory (non-volatile memory), such as a magnetic disk memory, a Flash memory (Flash), and the like.
The bus 504 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc.
The user equipment may also include input and output devices coupled to bus 504 for coupling to other components, such as processor 501, via the bus. The input and output device can provide an input interface for an operator so that the operator can select a control item through the input interface, and can also be other interfaces through which other equipment can be externally connected.
It should be noted that, for simplicity of description, the above-mentioned embodiments of the method are described as a series of acts or combinations, but those skilled in the art will recognize that the present invention is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required by the invention.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: flash Memory disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
The content downloading method, the related device and the system provided by the embodiment of the present invention are described in detail above, and a specific example is applied in the text to explain the principle and the embodiment of the present invention, and the description of the above embodiment is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (4)
1. A method of computing hand joints, the method comprising:
obtaining a quaternion by fusing a first joint sensor of a middle finger, a first joint sensor of an index finger and a palm sensor, wherein the quaternion specifically comprises: q1, q2 and q 0;
establishing a coordinate axis vector x1 and a vector y1 of the index finger first joint sensor;
using quaternion multiplication q1y1q1 -1And q is1x1q1 -1Transforming the vector into a geographic coordinate system vector yG1、xG1；
Using quaternion multiplication q0 -1yG1q0And q is0 -1xG1q0Transforming the geographic coordinate system vector into the vector y under the palm coordinate systemH1And xH1；
Establishing coordinate axis vectors x2 and y2 of the middle finger first joint sensor, and converting the vectors x2 and y2 into vectors under a palm coordinate system;
setting the vector z-axis coordinate under the palm coordinate system to zero to obtain a vector yH1P、xH1PAnd yH2P、xH2P；
Calculating the vector xH1pAnd xH2pAngle alpha and vector yH1pAnd yH2pThe included angle beta of;
and simulating the posture of the finger according to the included angles alpha and beta.
2. The method of claim 1, further comprising:
establishing a vector of each joint sensor in each finger, converting the vector of each joint sensor into a vector of a palm sensor coordinate system, projecting the vector of the palm sensor coordinate system to an X-0-Y plane, calculating an angle between two vectors of the same joint of adjacent fingers on the X-0-Y plane, and simulating the gesture of the hand according to the angle between the two vectors of the same joint of the adjacent fingers.
3. A glove, comprising: a sensor, a processor, the sensor disposed at a joint of each finger;
the processor is configured to obtain a quaternion from the fusion of the first middle finger joint sensor, the first index finger joint sensor, and the palm sensor, where the quaternion specifically includes: q1, q2 and q 0; establishing a coordinate axis vector x1 and a vector y1 of the index finger first joint sensor; using quaternion multiplication and transforming the vector to a geographic coordinate system vector; transforming the geographic coordinate system vector to a palm coordinate system lower vector by using quaternion multiplication, establishing coordinate axis vectors x2 and y2 of the middle finger first joint sensor, and converting the vectors x2 and y2 to the palm coordinate system lower vector;
the processor is used for setting the vector z-axis coordinate under the palm coordinate system to zero to obtain a vector yH1P、xH1PAnd yH2P、xH2P；
Calculating the vector xH1pAnd xH2pAngle alpha and vector yH1pAnd yH2pThe included angle beta of; and simulating the posture of the finger according to the included angles alpha and beta.
4. The glove of claim 3 wherein the processor is further configured to establish a vector for each joint sensor in each finger, convert the vector for each joint sensor into a vector for a palm sensor coordinate system, project the vector for the palm sensor coordinate system onto an X-0-Y plane, calculate an angle between two vectors for the same joint of adjacent fingers in the X-0-Y plane, and simulate the hand pose based on the angle between the two vectors for the same joint of adjacent fingers.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711283329.3A CN108073283B (en) | 2017-12-07 | 2017-12-07 | Hand joint calculation method and glove |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711283329.3A CN108073283B (en) | 2017-12-07 | 2017-12-07 | Hand joint calculation method and glove |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108073283A CN108073283A (en) | 2018-05-25 |
| CN108073283B true CN108073283B (en) | 2021-02-09 |
Family
ID=62157885
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711283329.3A Active CN108073283B (en) | 2017-12-07 | 2017-12-07 | Hand joint calculation method and glove |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108073283B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113268136A (en) * | 2020-02-14 | 2021-08-17 | 北京海益同展信息科技有限公司 | Method and device for resolving degree of freedom between thumb and palm and data glove |
| CN113467599A (en) * | 2020-03-31 | 2021-10-01 | 北京海益同展信息科技有限公司 | Method and device for resolving degree of freedom of flexion and extension between fingers and palm and data glove |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103984928A (en) * | 2014-05-20 | 2014-08-13 | 桂林电子科技大学 | Finger gesture recognition method based on field depth image |
| CN105389539A (en) * | 2015-10-15 | 2016-03-09 | 电子科技大学 | Three-dimensional gesture estimation method and three-dimensional gesture estimation system based on depth data |
| CN106445130A (en) * | 2016-09-19 | 2017-02-22 | 武汉元生创新科技有限公司 | Motion capture glove for gesture recognition and calibration method thereof |
| CN106529387A (en) * | 2016-08-31 | 2017-03-22 | 袁峰 | Motion state analysis method and terminal for football playing by player |
| CN107378944A (en) * | 2017-06-20 | 2017-11-24 | 东南大学 | A kind of multi-dimensional surface electromyographic signal prosthetic hand control method based on PCA |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9821224B2 (en) * | 2010-12-21 | 2017-11-21 | Microsoft Technology Licensing, Llc | Driving simulator control with virtual skeleton |
-
2017
- 2017-12-07 CN CN201711283329.3A patent/CN108073283B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103984928A (en) * | 2014-05-20 | 2014-08-13 | 桂林电子科技大学 | Finger gesture recognition method based on field depth image |
| CN105389539A (en) * | 2015-10-15 | 2016-03-09 | 电子科技大学 | Three-dimensional gesture estimation method and three-dimensional gesture estimation system based on depth data |
| CN106529387A (en) * | 2016-08-31 | 2017-03-22 | 袁峰 | Motion state analysis method and terminal for football playing by player |
| CN106445130A (en) * | 2016-09-19 | 2017-02-22 | 武汉元生创新科技有限公司 | Motion capture glove for gesture recognition and calibration method thereof |
| CN107378944A (en) * | 2017-06-20 | 2017-11-24 | 东南大学 | A kind of multi-dimensional surface electromyographic signal prosthetic hand control method based on PCA |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108073283A (en) | 2018-05-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108874119B (en) | System and method for tracking arm movement to generate input for a computer system | |
| US11481029B2 (en) | Method for tracking hand pose and electronic device thereof | |
| US11474593B2 (en) | Tracking user movements to control a skeleton model in a computer system | |
| EP3120232B1 (en) | Determining user handedness and orientation using a touchscreen device | |
| US10976863B1 (en) | Calibration of inertial measurement units in alignment with a skeleton model to control a computer system based on determination of orientation of an inertial measurement unit from an image of a portion of a user | |
| CN108279773B (en) | Data glove based on MARG sensor and magnetic field positioning technology | |
| US11175729B2 (en) | Orientation determination based on both images and inertial measurement units | |
| CN111240469B (en) | Calibration method and device for hand motion capture, electronic equipment and storage medium | |
| CN108073283B (en) | Hand joint calculation method and glove | |
| WO2018064634A1 (en) | Determination of cursor position on remote display screen based on bluetooth angle of arrival | |
| CN111240468B (en) | Calibration method and device for hand motion capture, electronic equipment and storage medium | |
| WO2020009715A2 (en) | Tracking user movements to control a skeleton model in a computer system | |
| CN109866217B (en) | Robot mileage positioning method, device, terminal equipment and computer storage medium | |
| CN113119104A (en) | Mechanical arm control method, mechanical arm control device, computing equipment and system | |
| CN113084791B (en) | Mechanical arm control method, mechanical arm control device and terminal equipment | |
| Henderson | An inertial measurement system for hand and finger tracking | |
| TWI663526B (en) | Motion analysis device and motion analysis method | |
| CN113496165B (en) | User gesture recognition method and device, hand intelligent wearable device and storage medium | |
| CN109782999A (en) | A kind of input method, input equipment and a kind of computer-readable medium | |
| CN112433628B (en) | Rigid body pose determination method and device of double-light-ball interactive pen and computer equipment | |
| CN108151726B (en) | Motion information detection system and method | |
| CN112433629B (en) | Rigid body posture determination method and device of double-light-ball interactive pen and computer equipment | |
| CN116901059A (en) | Gesture sensor-based selection and solution method, device and system in teleoperation | |
| CN112947772A (en) | Rotation posture alignment method and device of double-light-ball interactive pen and computer equipment | |
| CN116994326A (en) | Gesture recognition method, device and system of wearable system and readable 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 | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20190424 Address after: 510000 Room 3001, Software South Building, Guangzhou Nansha Information Technology Park, No. 2 Huanshi Avenue, Nansha District, Guangzhou, Guangdong Province Applicant after: Guangzhou Deep Well Technology Co., Ltd. Address before: Room 1026, He Shanheng Engineering Building, Chinese University of Hong Kong, China Applicant before: Yuan Feng |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |