  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.
