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On-line recognizing method of hand gesture mode established based on sEMG

A recognition method and pattern technology, applied in the field of online recognition of human hand posture patterns, can solve problems such as lack of real-time performance, poor bionic performance, and poor reliability of grasping objects, and achieve good real-time performance, high success rate, and good reliability.

Inactive Publication Date: 2009-02-25
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problems of lack of real-time performance, poor bionic performance, and poor reliability of grasping objects in the existing method for controlling artificial prosthetic hands with myoelectric information, the present invention further provides an online recognition method for human hand posture patterns based on sEMG

Method used

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  • On-line recognizing method of hand gesture mode established based on sEMG
  • On-line recognizing method of hand gesture mode established based on sEMG
  • On-line recognizing method of hand gesture mode established based on sEMG

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specific Embodiment approach 1

[0019] Specific implementation mode one: as Figure 1~4 And as shown in table 1, the online recognition method of hand posture pattern based on sEMG described in the present embodiment is to realize according to the following steps:

[0020] Step 1. Necessary planning for the posture mode of the human hand: take the thumb, index finger and other fingers (the three-finger linkage formed by the middle finger, ring finger and little finger) as a single degree of freedom, and take the relaxation, bending and stretching of the three as individual degrees of freedom. A "state" is represented by 0, -1, and 1 respectively. A total of 27 different hand posture modes are obtained by permutation and combination. Based on the simple transition performance between the modes during the training of the prosthetic hand user, all the modes have been redesigned. Arrangement, set mode 1 as the relaxed state, and mode 2 to mode 27 as the excited state; before the posture mode is recognized online...

specific Embodiment approach 2

[0030] Specific implementation mode two: as figure 1 As shown, in step 1, this embodiment comprehensively considers the difficulty of the palm gesture mode and the simple transition performance between the modes during training, and rearranges and plans all the modes as follows:

[0031]The basic modes include 1-relaxed state (0, 0, 0), 2-thumb flexion (-1, 0, 0), 7-thumb extension (1, 0, 0), 10-index finger flexion (0, -1, 0), 15- index finger stretch (0, 1, 0), 18- three-finger bend (0, 0, -1), 23- three-finger stretch (0, 0, 1), 26- full song (-1, - 1, -1), 27-full extension (1, 1, 1). The basic mode includes a single-degree-of-freedom finger for movement and a simple mode for all fingers to perform the same movement mode; extended I includes 3-(-1, 1, 1), 8-(1, -1, -1) , 11-(1, -1, 1), 16-(-1, 1, -1), 19-(1, 1, -1), 24-(-1, -1, 1), see extension I It is different from the "action finger" (ie, the index finger and three fingers in 2, the thumb and three fingers in 10, et...

specific Embodiment approach 3

[0032] Specific implementation mode three: combination figure 2 , image 3 And Table 1, this embodiment corresponds to the stretching and bending of the thumb, index finger and other fingers in step 2, each action will affect different muscles or muscle groups at the human forearm; according to the corresponding biological anatomy knowledge, the present invention utilizes human The five muscles in the forearm are extensor pollicis brevis, flexor hallucis longus, intrinsic extensor of index finger, superficial flexor digitorum and extensor digitorum. The electrodes on these muscles correspond to the six most basic hand posture modes , that is, thumb extension, thumb flexion, index finger extension, index finger flexion, three-finger extension, and three-finger flexion (as shown in Table 1), and are further used for the recognition of all 27 patterns. Other steps are the same as those in Embodiment 1 or 2.

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Abstract

An on-line identification method of hand posture modes which is established by being based on sEMG relates to an on-line identification method of hand posture modes. The method includes the following steps: hand posture modes are properly programmed; a myoelectric electrode is worn on the position of an electrode for identifying hand posture modes correspondingly to a position on the forearm muscle of a person using an artificial hand; the person using the artificial hand collects the primary myoelectric data in every excited mode according to various programmed posture modes and carries out feature extraction; two decision functions are used for realizing on-line identification control and different decision functions are respectively adopted for identifying various modes between relaxing states and excited states or among excited states. With the method of the invention, the artificial hand can achieve the advantages of good real-time property, fine bionic performance, excellent reliability when holding things, etc.

Description

technical field [0001] The invention relates to an online recognition method of human hand posture pattern based on sEMG, which belongs to the field of biological information recognition and control. Background technique [0002] Most commercial prosthetic hands have only one degree of freedom, and their lack of grasping ability and flexibility is one of the reasons why many disabled patients are reluctant to wear prosthetic hands. Therefore, many research institutions at home and abroad have carried out research on multi-degree-of-freedom and multi-functional prosthetic hands, such as the Italian Cyberhand, SmartHand, the British i-limb, and the HIT / DLR Prosthetic Hand of the Institute of Robotics of Harbin Institute of Technology, etc. Multiple degrees of freedom improve the dexterity of the prosthetic hand and enhance the grasping function, but it brings certain difficulties to the control. Surface electromyography (sEMG, surface Electromyography) is the most widely used...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61F2/72
Inventor 杨大鹏赵京东姜力刘宏
Owner HARBIN INST OF TECH
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