Artificial lower limb motion intention recognition algorithm based on BPSOGWO-KNN

A motion intention and recognition algorithm technology, applied in the field of pattern recognition, can solve problems such as unsatisfactory effects, and achieve the effect of improving the accuracy of the algorithm

Pending Publication Date: 2022-04-15
JILIN UNIV
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AI Technical Summary

Problems solved by technology

[0004] Due to the various defects of the existing algorithm, the effect in practical application is not ideal. The present invention proposes to use the swarm intelligence optimization algorithm for feature scree

Method used

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  • Artificial lower limb motion intention recognition algorithm based on BPSOGWO-KNN
  • Artificial lower limb motion intention recognition algorithm based on BPSOGWO-KNN
  • Artificial lower limb motion intention recognition algorithm based on BPSOGWO-KNN

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Embodiment Construction

[0071] like figure 1 As shown, a lower limb prosthetic movement intention recognition algorithm based on BPSOGWO-KNN, the method includes the following steps:

[0072] 1.1. Obtain the motion data collected by each sensor in the knee prosthesis, including the following steps:

[0073] 1.1.1. Use the knee joint angle sensor, load cell and IMU sensor placed on the knee prosthesis to collect 8 disabled subjects to perform slow horizontal walking, normal speed horizontal walking, fast horizontal walking, uphill, downhill Data during slope, sitting, standing, going up and down stairs; before collecting data, each subject had to wear a prosthesis for ten hours of adaptive training; 8 disabled subjects included a female subject ;

[0074] 1.1.2. The data collected by the sensor includes: knee joint angle, ground reaction force, IMU sensor X-axis acceleration, IMU sensor Y-axis acceleration, IMU sensor Z-axis acceleration, IMU sensor X-axis angular velocity, IMU sensor Y-axis angular...

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Abstract

An artificial lower limb motion intention recognition algorithm based on BPSOGWO-KNN belongs to the technical field of mode recognition, and comprises the following steps: firstly, extracting multi-sensor data in a 200ms time window when a weighing sensor value is just greater than 16N in each gait cycle, denoising and removing abnormal data, and adding a classification label to normal data; extracting seven time domain feature values of data of each dimension in the time window, and performing feature selection by using a BPSOGWO-KNN algorithm; performing optimization training on a nearest neighbor value K in the KNN classifier and a feature weight in an optimal feature subset selected by a BPSOGWO-KNN algorithm by using a BBO algorithm; inputting the nearest neighbor value K and the feature weight value obtained through optimization into an improved KNN classifier; according to the method, redundant feature values are removed, different weights are given according to different contributions of the feature values, the distance mean values of K neighbor values closest to the target under each classification category are compared, pattern recognition is carried out, the algorithm accuracy is greatly improved, and the use safety of a lower limb amputation patient is guaranteed.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, and in particular relates to a BPSOGWO-KNN-based motion intention recognition algorithm for a lower limb prosthesis. Background technique [0002] Lower extremity amputations are a growing problem requiring advanced technology to help disabled people restore natural gait function in a variety of terrains; most commercially available prosthetic solutions are passive and simple mechanisms make them easy to implement in the clinic However, there are some problems. For example, amputee patients will consume more energy than normal people, causing compensatory movement and causing new damage to other uninjured tissues or parts; powered prosthetics will reduce compensation strategies and increase their own The selected walking speed and the range of comfortable walking speeds, but more advanced and intuitive control strategies are needed before this device can be widely adopted, and it is e...

Claims

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Application Information

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IPC IPC(8): G06V40/20G06N3/00G06K9/62G06V10/764
Inventor 任雷张尧修豪华阎凌云韩阳王旭钱志辉任露泉
Owner JILIN UNIV
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