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 esti-
mation 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.
Using ProACT with non-amputee participants, we compare
performance in a Box-and-Blocks Task using a virtual myoelectric prosthetic arm, with and without intent estimation.
Our results show that methods using intent estimation
improve both user satisfaction and the degree of success in
the task. 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
will be freely available after publication.