A body machine interface based on inertial sensors

Farshchiansadegh, Ali; Abdollahi, Farnaz; Chen, David; Lee, Mei Hua; Pedersen, Jessica Presperin; Pierella, Camilla; Roth, Elliot J.; Gonzalez, Ismael Seanez; Thorp, Elias B.; Mussa-Ivaldi, Ferdinando A.

doi:10.1109/embc.2014.6945026

Cited by 29 publications

(37 citation statements)

References 17 publications

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“…The BMI utilized in this study has the distinctive feature of being based on upper-body motions captured by multiple inertial sensors with the combined purpose of operating external devices and of promoting, preserving and remapping residual mobility that remains available to persons with severe paralysis. Our previous work involved using the BMI to address 2-D control problems [19], [20]—control the speed and heading of a powered wheelchair, a cursor position on a screen, typing on a virtual keyboard and playing games including pong. In this paper, we use our BMI within a framework to facilitate the control of high-dimensional assistive robotic arms.…”

Section: Related Workmentioning

confidence: 99%

“…The first two principal eigenvectors of the calibration data are extracted to form a 2-D control space. For further details of the interface and calibration, see [20]. …”

Section: System Descriptionmentioning

confidence: 99%

“…All participants gave their informed, signed consent to participate in this experiment, which was approved by Northwestern University’s Institutional Review Board. The SCI participant and one of the control participants were not naive to the BMI due to their previous participation in another study [20]. The remaining participants did not have any prior experience with the interface.…”

Section: System Implementation and Evaluationmentioning

confidence: 99%

“… 1 We begin with a mapping to 2-D, as this has been shown in our previous work to be both feasible and effective [20]. Future work will scale up C to higher dimensions.…”

mentioning

confidence: 99%

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Assistive robotic manipulation through shared autonomy and a Body-Machine Interface

Jain

Farshchiansadegh

Broad

et al. 2015

2015 IEEE International Conference on Rehabilitation Robotics (ICORR)

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Assistive robotic manipulators have the potential to improve the lives of people with motor impairments. They can enable individuals to perform activities such as pick-and-place tasks, opening doors, pushing buttons, and can even provide assistance in personal hygiene and feeding. However, robotic arms often have more degrees of freedom (DoF) than the dimensionality of their control interface, making them challenging to use—especially for those with impaired motor abilities. Our research focuses on enabling the control of high-DoF manipulators to motor-impaired individuals for performing daily tasks. We make use of an individual’s residual motion capabilities, captured through a Body-Machine Interface (BMI), to generate control signals for the robotic arm. These low-dimensional controls are then utilized in a shared-control framework that shares control between the human user and robot autonomy. We evaluated the system by conducting a user study in which 6 participants performed 144 trials of a manipulation task using the BMI interface and the proposed shared-control framework. The 100% success rate on task performance demonstrates the effectiveness of the proposed system for individuals with motor impairments to control assistive robotic manipulators.

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Section: Related Workmentioning

confidence: 99%

“…The first two principal eigenvectors of the calibration data are extracted to form a 2-D control space. For further details of the interface and calibration, see [20]. …”

Section: System Descriptionmentioning

confidence: 99%

Section: System Implementation and Evaluationmentioning

confidence: 99%

“… 1 We begin with a mapping to 2-D, as this has been shown in our previous work to be both feasible and effective [20]. Future work will scale up C to higher dimensions.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Assistive robotic manipulation through shared autonomy and a Body-Machine Interface

Jain

Farshchiansadegh

Broad

et al. 2015

2015 IEEE International Conference on Rehabilitation Robotics (ICORR)

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“…This type of interface has been shown to be effective for controlling a computer cursor to complete a variety of tasks including center out reaching, playing virtual ping-pong, or playing cards [31]. However, little work has been done to investigate whether users can learn to use this type of interface to control a power wheelchair safely and effectively.…”

Section: Introductionmentioning

confidence: 99%

Upper Body-Based Power Wheelchair Control Interface for Individuals With Tetraplegia

Thorp

Abdollahi

Chen

et al. 2016

IEEE Trans. Neural Syst. Rehabil. Eng.

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Many power wheelchair control interfaces are not sufficient for individuals with severely limited upper limb mobility. The majority of controllers that do not rely on coordinated arm and hand movements provide users a limited vocabulary of commands and often do not take advantage of the user’s residual motion. We developed a body-machine interface (BMI) that leverages the flexibility and customizability of redundant control by using high dimensional changes in shoulder kinematics to generate proportional controls commands for a power wheelchair. In this study, three individuals with cervical spinal cord injuries were able to control the power wheelchair safely and accurately using only small shoulder movements. With the BMI, participants were able to achieve their desired trajectories and, after five sessions driving, were able to achieve smoothness that was similar to the smoothness with their current joystick. All participants were twice as slow using the BMI however improved with practice. Importantly, users were able to generalize training controlling a computer to driving a power wheelchair, and employed similar strategies when controlling both devices. Overall, this work suggests that the BMI can be an effective wheelchair control interface for individuals with high-level spinal cord injuries who have limited arm and hand control.

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Controlling an Assistive Robotic Manipulator via a Non-linear Body-Machine Interface

Giordano

Rizzoglio

Ballardini

et al. 2021

Biosystems &Amp; Biorobotics

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A body machine interface based on inertial sensors

Cited by 29 publications

References 17 publications

Assistive robotic manipulation through shared autonomy and a Body-Machine Interface

Assistive robotic manipulation through shared autonomy and a Body-Machine Interface

Upper Body-Based Power Wheelchair Control Interface for Individuals With Tetraplegia

Controlling an Assistive Robotic Manipulator via a Non-linear Body-Machine Interface

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