sensing

We propose a method that uses our previously developed skin-crawling robots to noninvasively test the mechanical properties of soft tissue. We explore the use of two miniature sensors: an indenter and a cutometer. We evaluate the sensor’s performance from data collected on simulated tissue, classifying the depth and size of a simulated lump with over 98.8% accuracy using convolutional neural nets. Finally, we do limited on-body testing to map dry skin on the forearm with a cutometer. We hope to improve the ability to test tissues noninvasively, providing better sensitivity and systematic data collection. read more