BMI control of a third arm for multitasking

Peñaloza, Christian; Nishio, S.

doi:10.1126/scirobotics.aat1228

Cited by 128 publications

(93 citation statements)

References 29 publications

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“…We further show that hand augmentation resulted in increased sense of embodiment over the Thumb -a key goal for successful augmentation (22). By demonstrating successful adaptation to motor augmentation under diverse ecological task demands, our findings constitute a leap beyond earlier pioneering proof-of-concept accounts of successful usage of extra robotic fingers (1, 4, [23][24][25] or arms (3,5) under restricted circumstances.…”

Section: Discussionmentioning

confidence: 53%

Neurocognitive consequences of hand augmentation

Kieliba

Clode

Maimon-Mor

et al. 2020

Preprint

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From hand tools to cyborgs, humans have long been fascinated by the opportunities afforded by augmenting ourselves. Here, we studied how motor augmentation with an extra robotic thumb (the Third Thumb) impacts the biological hand representation in the brains of ablebodied people. Participants were tested on a variety of behavioural and neuroimaging tests designed to interrogate the augmented hand's representation before and after 5-days of semi-intensive training. Training improved the Thumb's motor control, dexterity and handrobot coordination, even when cognitive load was increased or when vision was occluded, and resulted in increased sense of embodiment over the robotic Thumb. Thumb usage also weakened natural kinematic hand synergies. Importantly, brain decoding of the augmented hand's motor representation demonstrated mild collapsing of the canonical hand structure following training, suggesting that motor augmentation may disrupt the biological hand representation. Together, our findings unveil critical neurocognitive considerations for designing human body augmentation.

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Section: Discussionmentioning

confidence: 53%

Neurocognitive consequences of hand augmentation

Kieliba

Clode

Maimon-Mor

et al. 2020

Preprint

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“…Human-machine interface (HMI) is the key approach to improving the satisfaction of peoplecentric service intelligence because intelligent HMI could reduce the interaction load of people in motion understanding and service recommendation. Current HMI is already improved by modern information and communication technologies (ICT), and some promising and innovative HMI approaches (i.e., eye motion [64], brain-machine interface systems [65,66]) are investigated by researchers in multi-disciplines. The intelligence challenges of HMI for smart cities include (1) the low-cost and ubiquitous HMI technologies within the urban environment; (2) people motion-driven and content-oriented HMI services based on multi-source information integration; and (3) transparent and culture friendly HMI with less media.…”

Section: Levelmentioning

confidence: 99%

People-Centric Service Intelligence for Smart Cities

Hong

Geng

2019

Smart Cities

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In the era of big data, smart cities have become a promising prospect for governments, citizens, and industrials. Many ideas and their derived systems for smart cities depend on big data for achieving a goal of data intelligence. However, there is an urgent transformation trend from data intelligence to service intelligence in the vision of smart cities due to the living requirements of citizens. People-centric service intelligence in smart cities has to support the realization of people’s needs within urban and social domains. This paper introduces a concept of people-centric service intelligence, defines the level of it and its challenges in the aspect of infrastructure, human dynamics, human understanding and prediction, and the human–machine interface. Then, this paper proposes the theoretical framework and technical frameworks of people-centric service intelligence, and the service intelligence schemas for future construction of smart cities. It will be helpful for governments and industries to design people-centric service intelligence for improving the quality of life, the capabilities of good sustainability, and better development.

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“…Besides, the user can only generate actions synchronously, resulting in a certain amount of time spent idle for users and thus slowing down the system. The motor imagery-based (MIbased) BMI, which does not depend on the external stimulus, allows for asynchronous control paradigms to move the robotic arm (Meng et al, 2016;Penaloza and Nishio, 2018). Nevertheless, the user has to switch many discrete MI states during the task; for instance, he/she needs to perform the left/right hand/both hands MI as well as both hands relaxing to move the endeffector leftward/rightward/upward/downward (limited discrete directions only) (Meng et al, 2016;Xia et al, 2017;Xu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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

Zeng

Shen

et al. 2020

Front. Neurorobot.

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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.

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BMI control of a third arm for multitasking

Cited by 128 publications

References 29 publications

Neurocognitive consequences of hand augmentation

Neurocognitive consequences of hand augmentation

People-Centric Service Intelligence for Smart Cities

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

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