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People use their hands for intricate tasks like playing musical instruments, employing myriad touch sensations to inform motor control. In contrast, current prosthetic hands lack comprehensive haptic feedback and exhibit rudimentary multitasking functionality. Limited research has explored the potential of upper limb amputees to feel, perceive, and respond to multiple channels of simultaneously activated haptic feedback to concurrently control the individual fingers of dexterous prosthetic hands. This study introduces a novel control architecture for three amputees and nine additional subjects to concurrently control individual fingers of an artificial hand using two channels of context-specific haptic feedback. Artificial neural networks (ANNs) recognize subjects’ electromyogram (EMG) patterns governing the artificial hand controller. ANNs also classify the directions objects slip across tactile sensors on the robotic fingertips, which are encoded via the vibration frequency of wearable vibrotactile actuators. Subjects implement control strategies with each finger simultaneously to prevent or permit slip as desired, achieving a 94.49% ± 8.79% overall success rate. Although no statistically significant difference exists between amputees’ and non-amputees’ success rates, amputees require more time to respond to simultaneous haptic feedback signals, suggesting a higher cognitive load. Nevertheless, amputees can accurately interpret multiple channels of nuanced haptic feedback to concurrently control individual robotic fingers, addressing the challenge of multitasking with dexterous prosthetic hands.
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Dexterous Manipulation with a Bi-Manual AnthropomorphicTeleoperation Robot
We present a new upper-limb anthropomorphic dexterous telemanipulation system, the Dexterity Testbed Nexus(DexNex). DexNex is teleoperated by a human user in theOperator Station who controls the Avatar Station to complete temanipulation tasks. The Avatar replicates the upper limbs of a human and is statically mounted to the workspace. Three benchmarking tasks were used: box & blocks, the MinnesotaTurning Test revised form (MTTrf), and a table setting task.Subjects completed the tasks with their natural bodies to provide normative data. Subjects then attempted the same tasks with haptic feedback enabled or disabled. The utility of haptics was computed for four metrics. Haptic feedback improved performance for three of the four metrics (26% increase in Box& Blocks score, 12% increased Table Setting success rate, and 1.3x faster time per success in Table Setting).
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- Award ID(s):
- 2221571
- PAR ID:
- 10627037
- Editor(s):
- Behnke, Sven; Ryu, Jee-Hwan; Pucci, Daniele; Santos, Veronica J
- Publisher / Repository:
- https://www.ais.uni-bonn.de/IROS2024AvatarWS/
- Date Published:
- Edition / Version:
- 1
- Subject(s) / Keyword(s):
- dexterous robotics telemanipulation
- Format(s):
- Medium: X Size: 290kB Other: pdf
- Size(s):
- 290kB
- Location:
- Abu Dhabi, UAE
- Sponsoring Org:
- National Science Foundation
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