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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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Hierarchical Tactile Sensation Integration from Prosthetic Fingertips Enables Multi-Texture Surface Recognition
Multifunctional flexible tactile sensors could be useful to improve the control of prosthetic hands. To that end, highly stretchable liquid metal tactile sensors (LMS) were designed, manufactured via photolithography, and incorporated into the fingertips of a prosthetic hand. Three novel contributions were made with the LMS. First, individual fingertips were used to distinguish between different speeds of sliding contact with different surfaces. Second, differences in surface textures were reliably detected during sliding contact. Third, the capacity for hierarchical tactile sensor integration was demonstrated by using four LMS signals simultaneously to distinguish between ten complex multi-textured surfaces. Four different machine learning algorithms were compared for their successful classification capabilities: K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and neural network (NN). The time-frequency features of the LMSs were extracted to train and test the machine learning algorithms. The NN generally performed the best at the speed and texture detection with a single finger and had a 99.2 ± 0.8% accuracy to distinguish between ten different multi-textured surfaces using four LMSs from four fingers simultaneously. The capability for hierarchical multi-finger tactile sensation integration could be useful to provide a higher level of intelligence for artificial hands.
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- Award ID(s):
- 1659484
- PAR ID:
- 10314508
- Date Published:
- Journal Name:
- Sensors
- Volume:
- 21
- Issue:
- 13
- ISSN:
- 1424-8220
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/s21134324
