Evidence map›Paper›PMID 32038219›Full record

ArticleFrontiers in neurorobotics2019

Semi-Autonomous Robotic Arm Reaching With Hybrid Gaze-Brain Machine Interface.

Hong Zeng, Yitao Shen, Xuhui Hu, Aiguo Song, Baoguo Xu, Huijun Li, Yanxin Wang, Pengcheng Wen

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neurorobotics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
2.6field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 33 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 1 country.

Hong ZengSchool of Instrument Science and Engineering, Southeast University, Nanjing, China.
Yitao ShenSchool of Instrument Science and Engineering, Southeast University, Nanjing, China.
Xuhui HuSchool of Instrument Science and Engineering, Southeast University, Nanjing, China.
Aiguo SongState Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, China.
Baoguo XuSchool of Instrument Science and Engineering, Southeast University, Nanjing, China.
Huijun LiSchool of Instrument Science and Engineering, Southeast University, Nanjing, China.
Yanxin WangSchool of Instrument Science and Engineering, Southeast University, Nanjing, China.
Pengcheng WenAVIC Aeronautics Computing Technique Research Institute, Xi'an, China.
Southeast University · CNXi'an Aeronautical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent developments in the non-muscular human-robot interface (HRI) and shared control strategies have shown potential for controlling the assistive robotic arm by people with no residual movement or muscular activity in upper limbs. However, most non-muscular HRIs only produce discrete-valued commands, resulting in non-intuitive and less effective control of the dexterous assistive robotic arm. Furthermore, the user commands and the robot autonomy commands usually switch in the shared control strategies of such applications. This characteristic has been found to yield a reduced sense of agency as well as frustration for the user according to previous user studies. In this study, we firstly propose an intuitive and easy-to-learn-and-use hybrid HRI by combing the Brain-machine interface (BMI) and the gaze-tracking interface. For the proposed hybrid gaze-BMI, the continuous modulation of the movement speed via the motor intention occurs seamlessly and simultaneously to the unconstrained movement direction control with the gaze signals. We then propose a shared control paradigm that always combines user input and the autonomy with the dynamic combination regulation. The proposed hybrid gaze-BMI and shared control paradigm were validated for a robotic arm reaching task performed with healthy subjects. All the users were able to employ the hybrid gaze-BMI for moving the end-effector sequentially to reach the target across the horizontal plane while also avoiding collisions with obstacles. The shared control paradigm maintained as much volitional control as possible, while providing the assistance for the most difficult parts of the task. The presented semi-autonomous robotic system yielded continuous, smooth, and collision-free motion trajectories for the end effector approaching the target. Compared to a system without assistances from robot autonomy, it significantly reduces the rate of failure as well as the time and effort spent by the user to complete the tasks.

Indexed as

brain–machine interfacecontinuous shared controlgaze trackinghuman–robot interfacerobotic arm reaching

Identifiers

PMID32038219
PMCPMC6992643
OpenAlexW3003022750

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.