Imperial College London
Alternate

ProfessorEtienneBurdet

Faculty of Engineering, Department of Bioengineering

Professor of Human Robotics
 
 
 
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Contact

 

e.burdet Website

 
 
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Location

 

419BSir Michael Uren HubWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Farkhatdinov:2017:1748-3190/aa80ad,
author = {Farkhatdinov, I and Roehri, N and Burdet, E},
doi = {1748-3190/aa80ad},
journal = {Bioinspiration and Biomimetics},
title = {Anticipatory detection of turning in humans for intuitive control of robotic mobility assistance},
url = {http://dx.doi.org/10.1088/1748-3190/aa80ad},
volume = {12},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Many wearable lower-limb robots for walking assistance have been developed in recent years. However, it remains unclear how they can be commanded in an intuitive and efficient way by their user. In particular, providing robotic assistance to neurologically impaired individuals in turning remains a significant challenge. The control should be safe to the users and their environment, yet yield sufficient performance and enable natural human-machine interaction. Here, we propose using the head and trunk anticipatory behaviour in order to detect the intention to turn in a natural, non-intrusive way, and use it for triggering turning movement in a robot for walking assistance. We therefore study head and trunk orientation during locomotion of healthy adults, and investigate upper body anticipatory behaviour during turning. The collected walking and turning kinematics data are clustered using the k-means algorithm and cross-validation tests and k-nearest neighbours method are used to evaluate the performance of turning detection during locomotion. Tests with seven subjects exhibited accurate turning detection. Head anticipated turning by more than 400–500 ms in average across all subjects. Overall, the proposed method detected turning 300 ms after its initiation and 1230 ms before the turning movement was completed. Using head anticipatory behaviour enabled to detect turning faster by about 100 ms, compared to turning detection using only pelvis orientation measurements. Finally, it was demonstrated that the proposed turning detection can improve the quality of human–robot interaction by improving the control accuracy and transparency.
AU  - Farkhatdinov,I
AU  - Roehri,N
AU  - Burdet,E
DO  - 1748-3190/aa80ad
PY  - 2017///
SN  - 1748-3182
TI  - Anticipatory detection of turning in humans for intuitive control of robotic mobility assistance
T2  - Bioinspiration and Biomimetics
UR  - http://dx.doi.org/10.1088/1748-3190/aa80ad
UR  - http://hdl.handle.net/10044/1/50351
VL  - 12
ER  -
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