SmartHand: Towards Embedded Smart Hands for Prosthetic and Robotic Applications

The sophisticated sense of touch of the human hand significantly contributes to our ability to safely, efficiently, and dexterously manipulate arbitrary objects in our environment. Robotic and prosthetic devices lack refined, tactile feedback from their end-effectors, leading to counterintuitive and...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Wang, Xiaying, Geiger, Fabian, Niculescu, Vlad, Magno, Michele, Benini, Luca
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 23.07.2021
Subjects
Accuracy
Artificial neural networks
Embedded systems
End effectors
Hand (anatomy)
Microcontrollers
Power consumption
Prostheses
Robotics
Sensor arrays
Sensory feedback
Tactile sensors (robotics)
Temporal resolution
Online AccessGet full text

Cover

More Information
Summary:The sophisticated sense of touch of the human hand significantly contributes to our ability to safely, efficiently, and dexterously manipulate arbitrary objects in our environment. Robotic and prosthetic devices lack refined, tactile feedback from their end-effectors, leading to counterintuitive and complex control strategies. To address this lack, tactile sensors have been designed and developed, but they often offer an insufficient spatial and temporal resolution. This paper focuses on overcoming these issues by designing a smart embedded system, called SmartHand, enabling the acquisition and real-time processing of high-resolution tactile information from a hand-shaped multi-sensor array for prosthetic and robotic applications. We acquire a new tactile dataset consisting of 340,000 frames while interacting with 16 everyday objects and the empty hand, i.e., a total of 17 classes. The design of the embedded system minimizes response latency in classification, by deploying a small yet accurate convolutional neural network on a high-performance ARM Cortex-M7 microcontroller. Compared to related work, our model requires one order of magnitude less memory and 15.6x fewer computations while achieving similar inter-session accuracy and up to 98.86% and 99.83% top-1 and top-3 cross-validation accuracy, respectively. Experimental results show a total power consumption of 505mW and a latency of only 100ms.
ISSN:2331-8422