Accurate Decoding of Material Textures Using a finger Mounted Accelerometer

Tactile feedback plays a crucial role in our experience and control of physical interaction with objects in our environment. However, the technology for low-cost and efficient tactile 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.