The Modular Prosthetic Limb

Johannes, Matthew S.; Faulring, Eric L.; Katyal, Kapil D.; Para, Matthew P.; Helder, John B.; Makhlin, A. R.; Moyer, Tom; Wahl, Daniel R.; Solberg, James J.; Clark, Steve; Armiger, Robert S.; Lontz, Travis; Geberth, Kathryn; Moran, Courtney W.; Wester, Brock A.; Doren, T. Van; Santos-Munne, J.J.

doi:10.1016/b978-0-12-814659-0.00021-7

Cited by 25 publications

(32 citation statements)

References 26 publications

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“…Bimanual gesture combinations were predicted using two hierarchical linear classifiers, each trained for a specific hand using inputs from the contralateral motor and somatosensory cortices. Furthermore, by mapping a set of hand gestures to four directions on a 2D plane, the participant was able to use the gesture classifiers to simultaneously control two JHU/APL Modular Prosthetic limbs (MPLs;Johannes et al, 2011Johannes et al, , 2020 in center-out movement tasks. Online performance was explored as a function of the complexity of bilateral gesture combinations.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous classification of bilateral hand gestures using bilateral microelectrode recordings in a tetraplegic patient

Thomas

Nickl

Thompson

et al. 2020

Preprint

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Most daily tasks require simultaneous control of both hands. Here we demonstrate simultaneous classification of gestures in both hands using multi-unit activity recorded from bilateral motor and somatosensory cortices of a tetraplegic participant. Attempted gestures were classified using hierarchical linear discriminant models trained separately for each hand. In an online experiment, gestures were continuously classified and used to control two robotic arms in a center-out movement task. Bimanual trials that required keeping one hand still resulted in the best performance (70.6%), followed by symmetric movement trials (50%) and asymmetric movement trials (22.7%). Our results indicate that gestures can be simultaneously decoded in both hands using two independently trained hand models concurrently, but online control using this approach becomes more difficult with increased complexity of bimanual gesture combinations. This study demonstrates the potential for restoring simultaneous control of both hands using a bilateral intracortical brain-machine interface.

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Section: Introductionmentioning

confidence: 99%

Simultaneous classification of bilateral hand gestures using bilateral microelectrode recordings in a tetraplegic patient

Thomas

Nickl

Thompson

et al. 2020

Preprint

Self Cite

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“…Online decoding was achieved by binning the incoming neural signal and averaging the normalized firing rates from each electrode across a 240 ms buffer. A gesture prediction from the neural signal was made every 30 ms and the output was transmitted using a custom software interface to send control commands to the robot control system controlling the MPL (Johannes et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

“…Researchers have demonstrated bimanual control of virtual arms using neural signals from bilateral frontal and parietal cortical areas in non-human primates (Ifft et al, 2013 ). Prior work has demonstrated effective control of seven (Collinger et al, 2013 ) and even up to 10 (Wodlinger et al, 2015 ) DOFs in individuals using an invasive cortical BMI to move anthropomorphic robotic limbs; however, despite these impressive advances the need for bimanual control of two robotic limbs for more complex tasks of daily living requires control over as many as 34 DOFs, if using highly dexterous robotic limbs (Johannes et al, 2020 ). To address this challenge, advanced strategies, such as shared control, could help significantly reduce the required DOFs needed to effectively complete tasks requiring two arms while using a BMI.…”

Section: Introductionmentioning

confidence: 99%

Shared Control of Bimanual Robotic Limbs With a Brain-Machine Interface for Self-Feeding

et al. 2022

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Advances in intelligent robotic systems and brain-machine interfaces (BMI) have helped restore functionality and independence to individuals living with sensorimotor deficits; however, tasks requiring bimanual coordination and fine manipulation continue to remain unsolved given the technical complexity of controlling multiple degrees of freedom (DOF) across multiple limbs in a coordinated way through a user input. To address this challenge, we implemented a collaborative shared control strategy to manipulate and coordinate two Modular Prosthetic Limbs (MPL) for performing a bimanual self-feeding task. A human participant with microelectrode arrays in sensorimotor brain regions provided commands to both MPLs to perform the self-feeding task, which included bimanual cutting. Motor commands were decoded from bilateral neural signals to control up to two DOFs on each MPL at a time. The shared control strategy enabled the participant to map his four-DOF control inputs, two per hand, to as many as 12 DOFs for specifying robot end effector position and orientation. Using neurally-driven shared control, the participant successfully and simultaneously controlled movements of both robotic limbs to cut and eat food in a complex bimanual self-feeding task. This demonstration of bimanual robotic system control via a BMI in collaboration with intelligent robot behavior has major implications for restoring complex movement behaviors for those living with sensorimotor deficits.

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“…Their prosthesis is a mixture of sensors, actuators, neuroscience and complex software with the goal of creating a novel prosthesis [20]. Despite their serious injuries, the modular prosthetic limbs allow upper extremity amputees to perform ADL [21,22]. The University of Utah has also developed a prototype arm called LUKE (Figure 5), which has the ability to interact with the nerves of the amputee and introduce touch into prosthesis through the use of advanced interfaces between the machine and body [24].…”

Section: Current Prosthetic Handsmentioning

confidence: 99%

Can Prosthetic Hands Mimic a Healthy Human Hand?

Nazari

Alam

2021

Prosthesis

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Historical evidence suggests that prostheses have been used since ancient Egyptian times. Prostheses were usually utilized for function and cosmetic appearances. Nowadays, with the advancement of technology, prostheses such as artificial hands can not only improve functional, but have psychological advantages as well and, therefore, can significantly enhance an individual’s standard of living. Combined with advanced science, a prosthesis is not only a simple mechanical device, but also an aesthetic, engineering and medical marvel. Prosthetic limbs are the best tools to help amputees reintegrate into society. In this article, we discuss the background and advancement of prosthetic hands with their working principles and possible future implications. We also leave with an open question to the readers whether prosthetic hands could ever mimic and replace our biological hands.

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The Modular Prosthetic Limb

Cited by 25 publications

References 26 publications

Simultaneous classification of bilateral hand gestures using bilateral microelectrode recordings in a tetraplegic patient

Simultaneous classification of bilateral hand gestures using bilateral microelectrode recordings in a tetraplegic patient

Shared Control of Bimanual Robotic Limbs With a Brain-Machine Interface for Self-Feeding

Can Prosthetic Hands Mimic a Healthy Human Hand?

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