SmartHand: Towards Embedded Smart Hands for Prosthetic and Robotic Applications
Xiaying Wang, Fabian Geiger, Vlad Niculescu, Michele Magno, Luca, Benini

TL;DR
SmartHand introduces an embedded tactile sensing system with high-resolution data processing for prosthetic and robotic hands, achieving high accuracy with low latency and power consumption.
Contribution
The paper presents a novel embedded system with a compact neural network for real-time tactile data classification, improving resolution and efficiency over existing solutions.
Findings
Achieved up to 98.86% top-1 accuracy in tactile classification.
Reduced computational requirements by 15.6x compared to related work.
Maintained high accuracy with low power consumption (505mW).
Abstract
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…
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Taxonomy
TopicsAdvanced Sensor and Energy Harvesting Materials · EEG and Brain-Computer Interfaces · Muscle activation and electromyography studies
