Teleoperation control of an exoskeleton robot using brain machine interface and visual compressive sensing

Li, Zhijun; He, Wei; Yang, Chenguang; Qiu, Shiyuan; Zhang, Longbin; Su, Chun‐Yi

doi:10.1109/wcica.2016.7578669

Cited by 7 publications

(5 citation statements)

References 15 publications

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“…Spelling applications are our key focus of this review. Nevertheless, it is worth mentioning that BCIs are applied for a lot of other purposes, e.g., robot control [ 115 , 116 , 117 ], wheelchair control [ 118 , 119 ], web browsing [ 120 ], general control of an operating system with a virtual keyboard, as presented in [ 121 ], gaming [ 122 ], every-day electronic device control [ 123 , 124 , 125 ], and ADHD attention training [ 126 ], just to mention a few. A lot of these applications are also useful for people with neuromuscular malfunctions, and a number of them can be suitable for healthy users.…”

Section: Discussionmentioning

confidence: 99%

Brain–Computer Interface Spellers: A Review

et al. 2018

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A Brain–Computer Interface (BCI) provides a novel non-muscular communication method via brain signals. A BCI-speller can be considered as one of the first published BCI applications and has opened the gate for many advances in the field. Although many BCI-spellers have been developed during the last few decades, to our knowledge, no reviews have described the different spellers proposed and studied in this vital field. The presented speller systems are categorized according to major BCI paradigms: P300, steady-state visual evoked potential (SSVEP), and motor imagery (MI). Different BCI paradigms require specific electroencephalogram (EEG) signal features and lead to the development of appropriate Graphical User Interfaces (GUIs). The purpose of this review is to consolidate the most successful BCI-spellers published since 2010, while mentioning some other older systems which were built explicitly for spelling purposes. We aim to assist researchers and concerned individuals in the field by illustrating the highlights of different spellers and presenting them in one review. It is almost impossible to carry out an objective comparison between different spellers, as each has its variables, parameters, and conditions. However, the gathered information and the provided taxonomy about different BCI-spellers can be helpful, as it could identify suitable systems for first-hand users, as well as opportunities of development and learning from previous studies for BCI researchers.

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Section: Discussionmentioning

confidence: 99%

Brain–Computer Interface Spellers: A Review

et al. 2018

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“…For vision-based HCI, multi-camera, Kinect and leap motion controller are the main technological means for hand motion recognition, and the VR technology makes the interaction between human and computer more natural and more advanced. Sensor-based HCI methods mainly include tactile sensor, pressure sensor and motion tracking sensor [115]. These advanced techniques can realize the virtual mapping of real actions by acquiring feature information of HHMs, and be applied to the medicosurgical dexterous manipulation [116].…”

Section: A Human Computer Interactionmentioning

confidence: 99%

Multimodal Human Hand Motion Sensing and Analysis—A Review

Xue,

Ju,

Xiang

et al. 2019

IEEE Trans. Cogn. Dev. Syst.

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Human hand motion analysis is an essential research topic in recent applications, especially for dexterous robot hand manipulation learning from human hand skills. It provides important information about the gestures, tactile, speed and contact force, captured via multiple sensing technologies. This paper introduces a comprehensive survey of current hand motion sensing technologies and analysis approaches in recent emerging applications. Firstly, the nature of human hand motions is discussed in terms of simple motions, such as grasps and gestures, and complex motions, e.g. in-hand manipulations and re-grasps; secondly, different techniques for hand motion sensing, including contact-based and non-contact-based approaches, are discussed with comparisons with their pros and cons; then, the state-of-theart analysis methods are introduced, with a particular focus on the multimodal hand motion sensing and analysis; finally, cuttingedge applications of hand motion analysis are reviewed, with further discussion on facing challenges and new future directions.

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“…[1][2][3][4][5] Mechanical motion capture systems are often used in early days 6,7 and brain machine interface is emerging. 8 The motion capture systems composed of inertial measurement units (IMUs) have many attractive advantages. They are technically sound, reliable, economically efficient, informative, can be made compact and light weight.…”

Section: Introductionmentioning

confidence: 99%

Motion prediction and delay compensation for improved teleoperation of an exoskeletal robot

Duan

et al. 2018

Advances in Mechanical Engineering

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Delay is a major challenge in teleoperation. To teleoperate a robot in real-time with specific human motion such as walking, prediction and compensation algorithms are needed to minimize the tracking delay. Human walking possesses the inherent features of synergy and quasi-periodicity with nonlinearity. By analyzing these features, three prediction methods were designed. A delay-coordinate phase space reconstruction method is based on the delay embedding theorem and uses the historical data. The echo prediction method uses data from the coupling joints of the other body side and takes the Euclidean distances in the delay-coordinate phase space reconstruction method for data search and match. The fused prediction method combines the delay-coordinate phase space reconstruction and echo prediction methods, uses the best matching historical data of the same joint and the corresponding joint of the other limb. These methods were implemented for teleoperation of an exoskeletal robot with inertial measurement unit measurements of human walking. The predicted trajectories were used to compensate the teleoperation delay and improve the robot tracking performance. Experimental results demonstrated that the fused method brought apparent improvement over other methods and can serve as a promising solution for time delay estimation, prediction, and compensation of human walking tracking and teleoperation.

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Teleoperation control of an exoskeleton robot using brain machine interface and visual compressive sensing

Cited by 7 publications

References 15 publications

Brain–Computer Interface Spellers: A Review

Brain–Computer Interface Spellers: A Review

Multimodal Human Hand Motion Sensing and Analysis—A Review

Motion prediction and delay compensation for improved teleoperation of an exoskeletal robot

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