Accurate decoding of materials using a finger mounted accelerometer

TL;DR

This study demonstrates that a low-cost finger-mounted accelerometer can accurately identify various everyday materials during touch, offering potential for improved sensory feedback in prosthetics and rehabilitation.

Contribution

The paper introduces a novel, low-cost finger-mounted accelerometer system capable of accurately decoding multiple materials during touch, advancing sensory feedback technology.

Findings

Materials classified with 88% accuracy within 7 seconds

Accelerometer data effectively distinguishes different materials

Method applicable across multiple participants

Abstract

Sensory feedback is the fundamental driving force behind motor control and learning. However, the technology for low-cost and efficient sensory feedback remains a big challenge during stroke rehabilitation, and for prosthetic designs. Here we show that a low-cost accelerometer mounted on the finger can provide accurate decoding of many daily life materials during touch. We first designed a customized touch analysis system that allowed us to present different materials for touch by human participants, while controlling for the contact force and touch speed. Then, we collected data from six participants, who touched seven daily life materials-plastic, cork, wool, aluminum, paper, denim, cotton. We use linear sparse logistic regression and show that the materials can be classified from accelerometer recordings with an accuracy of 88% across materials and participants within 7 seconds of touch.