Gait Recognition and Robust Autonomous Location Method of Exoskeleton Robot Based on Machine Learning

Gu, Cuihong; Gao, Minghan; Qian, Weixing; Deng, Qingyu; Cheng, Tianyu; Chen, Siliang

doi:10.1109/iccre.2019.8724217

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…Optoelectronic-based pose sensors can be used for pose measurement [11], triboelectric bidirectional sensors for DOF rotations detection [12]. Based on these sensors, a support vector machine (SVM) and neural network [13] are used to help enhance the sensing ability of exoskeletons, which will be discussed in detail in 2.3. Besides, some medical sensor technologies are also used here, such as integrated electromyography signal sensors (EMGs) [14] and even brain-computer interface (BCI) based on EEG [15].…”

Section: Related Work 21 Powered Exoskeletonmentioning

confidence: 99%

A method for patient gait real-time monitoring based on powered exoskeleton and digital twin

Wei

2023

Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022)

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Powered exoskeletons are a kind of wearable robotics system attached outside the limbs, providing additional force for the limbs, which plays an important role in limb rehabilitation and patient assistance. As the patient gradually recovers, the patient’s gait will change over time. Therefore, the author hopes to get real-time gait data from patients in order to provide medical guidance and help the patients recover better. Inspired by the real-time monitoring of industrial robots, the author puts forward a method of medical monitoring using digital twin onto powered exoskeletons. For cost reasons, the author uses commercially available sensors to build this system. Further, the author fabricates a demonstration system for the exoskeleton to achieve this goal together with collecting and analyzing the data.

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Section: Related Work 21 Powered Exoskeletonmentioning

confidence: 99%

A method for patient gait real-time monitoring based on powered exoskeleton and digital twin

Wei

2023

Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022)

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“…The studies aforementioned are all based on pre-programmed control and rarely take into account the wearer's human-computer interaction experience. To realize the interaction control between wearer and exoskeleton, this paper obtains human motion intention through support vector machine (Support Vector Machine, SVM) training arm sEMG signals [22][23][24][25][26][27]; establishes exoskeleton motion model and movement gait by real-time prediction of human motion intention, dividing gait phase; and constructs motion gait logic of exoskeleton device by real-time sEMG signal recognition. The overall structure is illustrated in…”

Section: Introductionmentioning

confidence: 99%

A real-time stable-control gait switching strategy for lower-limb rehabilitation exoskeleton

2020

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Switching different gait according to different movements is an important direction in the study of exoskeleton robot. Identifying the movement intention of the wearer to control the gait planning of the exoskeleton robot can effectively improve the man-machine interaction experience after the exoskeleton. This paper uses a support vector machine (SVM) to realize wearer's motion posture recognition by collecting sEMG signals on the human surface. The moving gait of the exoskeleton is planned according to the recognition results, and the decoding intention signal controls gait switching. Meanwhile, the stability of the planned gait during the movement was analyzed. Experimental results show that the sEMG signal decoding human motion intentional, and control exoskeleton robot gait switching has good accuracy and real-time performance. It helps patients to complete rehabilitation training more safely and quickly.

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Coordination Design of a Power-Assisted Ankle Exoskeleton Robot Based on Active-Passive Combined Drive

Huang

2023

J. Shanghai Jiaotong Univ. (Sci.)

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Gait Recognition and Robust Autonomous Location Method of Exoskeleton Robot Based on Machine Learning

Cited by 3 publications

References 4 publications

A method for patient gait real-time monitoring based on powered exoskeleton and digital twin

A method for patient gait real-time monitoring based on powered exoskeleton and digital twin

A real-time stable-control gait switching strategy for lower-limb rehabilitation exoskeleton

Coordination Design of a Power-Assisted Ankle Exoskeleton Robot Based on Active-Passive Combined Drive

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