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Mendeley readers
Attention Score in Context
| Title |
Closed-Loop Hybrid Gaze Brain-Machine Interface Based Robotic Arm Control with Augmented Reality Feedback
|
|---|---|
| Published in |
Frontiers in Neurorobotics, October 2017
|
| DOI | 10.3389/fnbot.2017.00060 |
| Pubmed ID | |
| Authors |
Hong Zeng, Yanxin Wang, Changcheng Wu, Aiguo Song, Jia Liu, Peng Ji, Baoguo Xu, Lifeng Zhu, Huijun Li, Pengcheng Wen |
| Abstract |
Brain-machine interface (BMI) can be used to control the robotic arm to assist paralysis people for performing activities of daily living. However, it is still a complex task for the BMI users to control the process of objects grasping and lifting with the robotic arm. It is hard to achieve high efficiency and accuracy even after extensive trainings. One important reason is lacking of sufficient feedback information for the user to perform the closed-loop control. In this study, we proposed a method of augmented reality (AR) guiding assistance to provide the enhanced visual feedback to the user for a closed-loop control with a hybrid Gaze-BMI, which combines the electroencephalography (EEG) signals based BMI and the eye tracking for an intuitive and effective control of the robotic arm. Experiments for the objects manipulation tasks while avoiding the obstacle in the workspace are designed to evaluate the performance of our method for controlling the robotic arm. According to the experimental results obtained from eight subjects, the advantages of the proposed closed-loop system (with AR feedback) over the open-loop system (with visual inspection only) have been verified. The number of trigger commands used for controlling the robotic arm to grasp and lift the objects with AR feedback has reduced significantly and the height gaps of the gripper in the lifting process have decreased more than 50% compared to those trials with normal visual inspection only. The results reveal that the hybrid Gaze-BMI user can benefit from the information provided by the AR interface, improving the efficiency and reducing the cognitive load during the grasping and lifting processes. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 25% |
| United States | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 116 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 116 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 22 | 19% |
| Student > Bachelor | 15 | 13% |
| Student > Master | 12 | 10% |
| Student > Doctoral Student | 9 | 8% |
| Researcher | 9 | 8% |
| Other | 10 | 9% |
| Unknown | 39 | 34% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 34 | 29% |
| Computer Science | 20 | 17% |
| Neuroscience | 6 | 5% |
| Medicine and Dentistry | 5 | 4% |
| Nursing and Health Professions | 3 | 3% |
| Other | 10 | 9% |
| Unknown | 38 | 33% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 November 2017.
All research outputs
#13,220,095
of 23,007,053 outputs
Outputs from Frontiers in Neurorobotics
#238
of 879 outputs
Outputs of similar age
#158,793
of 328,927 outputs
Outputs of similar age from Frontiers in Neurorobotics
#2
of 15 outputs
Altmetric has tracked 23,007,053 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 879 research outputs from this source. They receive a mean Attention Score of 4.1. This one has gotten more attention than average, scoring higher than 72% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 328,927 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.