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Sensory feedback is the fundamental driving force behind motor control andlearning. However, the technology for low-cost and efficient sensory feedbackremains a big challenge during stroke rehabilitation, and for prostheticdesigns. Here we show that a low-cost accelerometer mounted on the finger canprovide accurate decoding of many daily life materials during touch. We firstdesigned a customized touch analysis system that allowed us to presentdifferent materials for touch by human participants, while controlling for thecontact 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 thematerials can be classified from accelerometer recordings with an accuracy of88% across materials and participants within 7 seconds of touch.

Accurate decoding of materials using a finger mounted accelerometer