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Mechanical Imaging of Soft Tissues With Miniature Climbing Robots

Systematically mapping the mechanical properties of skin and tissue is useful for biomechanics research and disease diagnostics. For example, later stage breast cancer and lymphoma manifest themselves as hard nodes under the skin. Currently, mechanical measurements are done manually, with a sense of touch or a handheld tool. Manual measurements do not provide quantitative information and vary depending on the skill of the practitioner. Research shows that tactile sensors could be more sensitive than a hand. We propose a method that uses our previously developed skin-crawling robots to noninvasively test the mechanical properties of soft tissue. Robots are more systematic and repeatable than humans. Using the data collected with a cutomoter or indenter integrated into the miniature robot, we trained a convolutional neural network to classify the size and depth of the lumps. The classification works with 98.8% accuracy for cutometer and 99.6% for indenter for lump size with a diameter of 0 to 10 mm embedded in depth of 1 to 5 mm in a simulated tissue. We conducted a limited evaluation on a forearm, where the robot imaged dry skin with a cutometer. We hope to improve the ability to test tissues noninvasively, and ultimately provide better sensitivity and systematic data collection.