SRL ASSISTANCE DATASET

Citation Author(s):
JIE
ZUO
HUST
Submitted by:
Jie Zuo
Last updated:
Sun, 02/11/2024 - 10:51
DOI:
10.21227/1rx7-m124
License:
36 Views
Categories:
Keywords:
0
0 ratings - Please login to submit your rating.

Abstract 

Normal
0

7.8 磅
0
2

false
false
false

EN-US
ZH-CN
X-NONE

/* Style Definitions */
table.MsoNormalTable
{mso-style-name:普通表格;
mso-tstyle-rowband-size:0;
mso-tstyle-colband-size:0;
mso-style-noshow:yes;
mso-style-priority:99;
mso-style-parent:"";
mso-padding-alt:0cm 5.4pt 0cm 5.4pt;
mso-para-margin:0cm;
mso-para-margin-bottom:.0001pt;
mso-pagination:widow-orphan;
font-size:10.5pt;
mso-bidi-font-size:11.0pt;
font-family:等线;
mso-ascii-font-family:等线;
mso-ascii-theme-font:minor-latin;
mso-fareast-font-family:等线;
mso-fareast-theme-font:minor-fareast;
mso-hansi-font-family:等线;
mso-hansi-theme-font:minor-latin;
mso-font-kerning:1.0pt;}

In light of global aging and prevalent stroke-related hemiplegia, this study addresses challenges in robot-assisted Sit-to-Stand (STS) movements, a daily activity prone to falls. Supernumerary Robotic Legs (SRL) serve as independent support, enhancing stability and limb movement range. Existing coordination control methods lack personalization for STS assistance, requiring solutions for human intent transmission and rapidly optimize coordination control challenges in the non-coupled human-robot system. The proposed human-SRL coordination control algorithm, grounded in personalized SRL-human coupling models, incorporates surface electromyography (sEMG) signals to design an intent-driven variable stiffness impedance control. The inclusion of incremental learning enables rapid optimization of impedance parameters, facilitating real-time adjustments in SRL assistance for adaptive coupling with users. Practical experiments involving both healthy participants and hemiplegic patients validate the algorithm's effectiveness during STS. 

Instructions: 

In light of global aging and prevalent stroke-related hemiplegia, this study addresses challenges in robot-assisted Sit-to-Stand (STS) movements, a daily activity prone to falls. Supernumerary Robotic Legs (SRL) serve as independent support, enhancing stability and limb movement range. Existing coordination control methods lack personalization for STS assistance, requiring solutions for human intent transmission and rapidly optimize coordination control challenges in the non-coupled human-robot system. The proposed human-SRL coordination control algorithm, grounded in personalized SRL-human coupling models, incorporates surface electromyography (sEMG) signals to design an intent-driven variable stiffness impedance control. The inclusion of incremental learning enables rapid optimization of impedance parameters, facilitating real-time adjustments in SRL assistance for adaptive coupling with users. Practical experiments involving both healthy participants and hemiplegic patients validate the algorithm's effectiveness during STS.

Dataset Files

    Files have not been uploaded for this dataset