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Human body electromyographic signal direct-drive joint torque mapping method

A joint torque and electromyographic signal technology, applied in the field of exoskeleton robot auxiliary control, can solve the problems of complex model, discrete and cumbersome methods, etc.

Pending Publication Date: 2022-03-25
国创机器人创新中心(哈尔滨)有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

[0004] (1) The traditional nerve-muscle-skeletal model is complex, the method is cumbersome, and the solution takes a long time: the mathematical expression of the traditional nerve-muscle-skeletal model is a variable constant first-order differential equation, the model is relatively complex, and the solution process takes a long time, reducing Its application value on exoskeleton robots
[0005] (2) The solution of differential equations needs to go through the numerical solution method, which is discrete: the traditional method generally uses the grid method, which requires numerical solution, so it is discrete

Method used

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  • Human body electromyographic signal direct-drive joint torque mapping method
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  • Human body electromyographic signal direct-drive joint torque mapping method

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specific Embodiment 1

[0061] according to Figure 1 to Figure 2 As shown, the specific optimized technical solution adopted by the present invention to solve the above technical problems is: the present invention relates to a human body electromyographic signal direct drive joint torque mapping method.

[0062] A human body electromyographic signal direct drive joint torque mapping method, comprising the following steps:

[0063] Step 1: Perform nerve activation and muscle activation extraction to obtain nerve activation information;

[0064] Preferably, the step 1 is specifically:

[0065] The collected sEMG signal, extracting muscle activation from the sEMG signal;

[0066] After 50-500Hz band-pass filtering, rectification, and normalization, the neural activation information u(t) is determined according to:

[0067]

[0068] Among them, u(t) is the neural activation of the tth sampling point; α, β 1 ,β 2 is the neural activation coefficient; d is the time delay; T E is the sampling time...

specific Embodiment 2

[0111] Step 1: collect sEMG signals of human lower limbs and extract muscle activation information. The specific process is as follows:

[0112] The signal acquisition equipment adopts the Delsys Trigno EMG acquisition system, which includes a wireless communication base station and 16 wireless EMG electrodes, and each electrode has a built-in EMG signal collector. Delsys Trigno EMG acquisition system supports offline acquisition mode and online acquisition mode: in offline acquisition mode, the EMG signal is packaged and sent after the experiment is over, and in online acquisition mode, the length of a single transmission data is set. Whenever the EMG signal record length reaches The setpoint is transferred once. The surface skin of the vastus lateralis, vastus medialis, biceps femoris, semimembranosus, rectus femoris, lateral gastrocnemius and medial gastrocnemius of the test subject’s unilateral lower limbs was depilated and exfoliated, and then seven Delsys electrodes wer...

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Abstract

The invention relates to a human body electromyographic signal direct-drive joint torque mapping method. The invention relates to the technical field of exoskeleton robot auxiliary control, and the method comprises the steps: carrying out the nerve activation and muscle activation extraction, and obtaining the nerve activation information; establishing a muscle-tendon model relationship, and determining a muscle force-length relationship; according to a muscle force-length relation, determining a pinniform angle; according to the pinniform angle, determining the length of a muscle-tendon unit and the force of the muscle-tendon unit; according to the length of the muscle-tendon unit and the force of the muscle-tendon unit, the joint torque is determined, and parameter identification is carried out. According to the method, an original model is simplified and subjected to continuous processing, so that the model can be micronized, the model is embedded into a neural network, a few-sample learning strategy is adopted, and physiological parameters of different individuals are estimated according to similarity on the basis of multi-person physiological feature comparison.

Description

technical field [0001] The invention relates to the technical field of auxiliary control of exoskeleton robots, and relates to a human body electromyographic signal direct-drive joint torque mapping method. Background technique [0002] Exoskeleton robots can assist human movement and replace human joints to do work. It has unique significance and advantages in the rehabilitation of stroke and paraplegic patients, reducing labor injuries of manual workers, and assisting long-distance walking. The force of human joints is the key information in the process of exoskeleton robot assistance. Its accurate real-time estimation ensures human-machine collaboration and improves the efficiency of exoskeleton robot assistance. [0003] The present invention aims at proposing a novel mapping method from myoelectric signals to joint moments by using a nerve-muscle-skeletal model. The nerve-muscle-skeletal model is formed based on the physiological parameters of the human body based on a...

Claims

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Application Information

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IPC IPC(8): B25J9/16B25J13/08B25J19/02
CPCB25J13/087B25J9/161B25J9/163B25J9/1694B25J19/02
Inventor 赵永恩衣淳植姜峰杨炽夫
Owner 国创机器人创新中心(哈尔滨)有限公司
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