ProACT: An Augmented Reality Testbed for Intelligent Prosthetic Arms

Upper-limb amputees face tremendous difficulty in operating dexterous powered prostheses. Previous work has shown that aspects of prosthetic hand, wrist, or elbow control can be improved through "intelligent" control, by combining movement-based or gaze-based intent estimation with low-level robotic autonomy. However, no such solutions exist for whole-arm control. Moreover, hardware platforms for advanced prosthetic control are expensive, and existing simulation platforms are not well-designed for integration with robotics software frameworks. We present the Prosthetic Arm Control Testbed (ProACT), a platform for evaluating intelligent control methods for prosthetic arms in an immersive (Augmented Reality) simulation setting. We demonstrate the use of ProACT through preliminary studies, with non-amputee participants performing an adapted Box-and-Blocks task with and without intent estimation. We further discuss how our observations may inform the design of prosthesis control methods, as well as the design of future studies using the platform. To the best of our knowledge, this constitutes the first study of semi-autonomous control for complex whole-arm prostheses, the first study including sequential task modeling in the context of wearable prosthetic arms, and the first testbed of its kind. Towards the goal of supporting future research in intelligent prosthetics, the system is built upon on existing open-source frameworks for robotics, and is available at https://arm.stanford.edu/proact.

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Publication Details

Journal
IEEE Transactions on Neural Systems and Rehabilitation Engineering
Published
2024-12-27
DOI
https://doi.org/10.1109/tnsre.2024.3521923
Primary Topic
Muscle activation and electromyography studies
Type
article
Field-Weighted Citation Impact
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article

ProACT: An Augmented Reality Testbed for Intelligent Prosthetic Arms

Monroe Kennedy, Shivani Guptasarma
IEEE Transactions on Neural Systems and Rehabilitation Engineering
Muscle activation and electromyography studies
article

ProACT: An Augmented Reality Testbed for Intelligent Prosthetic Arms

Monroe Kennedy, Shivani Guptasarma
article en

Abstract

Upper-limb amputees face tremendous difficulty in operating dexterous powered prostheses. Previous work has shown that aspects of prosthetic hand, wrist, or elbow control can be improved through "intelligent" control, by combining movement-based or gaze-based intent estimation with low-level robotic autonomy. However, no such solutions exist for whole-arm control. Moreover, hardware platforms for advanced prosthetic control are expensive, and existing simulation platforms are not well-designed for integration with robotics software frameworks. We present the Prosthetic Arm Control Testbed (ProACT), a platform for evaluating intelligent control methods for prosthetic arms in an immersive (Augmented Reality) simulation setting. We demonstrate the use of ProACT through preliminary studies, with non-amputee participants performing an adapted Box-and-Blocks task with and without intent estimation. We further discuss how our observations may inform the design of prosthesis control methods, as well as the design of future studies using the platform. To the best of our knowledge, this constitutes the first study of semi-autonomous control for complex whole-arm prostheses, the first study including sequential task modeling in the context of wearable prosthetic arms, and the first testbed of its kind. Towards the goal of supporting future research in intelligent prosthetics, the system is built upon on existing open-source frameworks for robotics, and is available at https://arm.stanford.edu/proact.

IEEE Transactions on Neural Systems and Rehabilitation EngineeringVol. 33
Stanford University (US)
Openalex Percentile: Top 77%
Muscle activation and electromyography studies
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