Accurate decoding of materials using a finger mounted accelerometer

Kuniharu Sakurada 1 Gowrishankar Ganesh 2 Wenwei Yu 1 Kahori Kita 1
2 IDH - Interactive Digital Humans
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
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.
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Kuniharu Sakurada, Gowrishankar Ganesh, Wenwei Yu, Kahori Kita. Accurate decoding of materials using a finger mounted accelerometer. ROBIO: Robotics and Biomimetics, Dec 2018, Kualalampur, Malaysia. pp.1316-1321, ⟨10.1109/robio.2018.8665189⟩. ⟨lirmm-02158999⟩

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