At First Contact: Stiffness Estimation Using Vibrational Information for Prosthetic Grasp Modulation

TL;DR

This paper introduces a vibrational sensing framework using machine learning for real-time stiffness estimation at first contact in prosthetic grasping, improving safety and control by enabling early grasp modulation.

Contribution

It develops a multimodal tactile sensor inspired by human skin and demonstrates that vibrational signals can accurately estimate object stiffness within 15 ms of contact.

Findings

Achieves up to 98.6% classification accuracy.

Regression errors as low as 2.39 Shore A.

Inference times under 1.5 ms.

Abstract

Stiffness estimation is crucial for delicate object manipulation in robotic and prosthetic hands but remains challenging due to dependence on force and displacement measurement and real-time sensory integration. This study presents a piezoelectric sensing framework for stiffness estimation at first contact during pinch grasps, addressing the limitations of traditional force-based methods. Inspired by human skin, a multimodal tactile sensor that captures vibrational and force data is developed and integrated into a prosthetic hand's fingertip. Machine learning models, including support vector machines and convolutional neural networks, demonstrate that vibrational signals within the critical 15 ms after first contact reliably encode stiffness, achieving classification accuracies up to 98.6% and regression errors as low as 2.39 Shore A on real-world objects of varying stiffness. Inference times of less than 1.5 ms are significantly faster than the average grasp closure time (16.65 ms in our dataset), enabling real-time stiffness estimation before the object is fully grasped. By leveraging the transient asymmetry in grasp dynamics, where one finger contacts the object before the others, this method enables early grasp modulation, enhancing safety and intuitiveness in prosthetic hands while offering broad applications in robotics.