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Researchers conventionally employ thermal imaging to monitor the health of animals, observe their habitat utilization, and track their activity patterns. These non-invasive methods can generate detailed images and offer valuable insights into behavior, movements, and environmental interactions. The aye-aye (Daubentonia madagascariensis), a rare and endangered lemur from Madagascar, possesses a uniquely slender third finger evolved for tapping surfaces at relatively high frequencies. The adaptation enables acoustic-based sensing to locate cavities with prey in trees to enhance their foraging abilities. The authors’ previous studies have demonstrated some descent simulating dynamic models of the aye-aye’s third digit referenced from limited data collected with monocular cameras, which can be challenging due to noisy and distorted images, impacting motion analysis adversely. In this proposed research, high-speed thermal cameras are employed to capture detailed finger position and orientation, providing a clearer understanding of the overall dynamic range. The improved biomimetic model aims to enhance tap-testing strategies in nondestructive evaluation for various inspection applications. 

The aye-aye (Daubentonia madagascariensis) is a nocturnal lemur native to the island of Madagascar with a unique thin middle finger. Its slender third digit has a remarkably specific adaptation, allowing them to perform tap-scanning to locate small cavities beneath tree bark and extract wood-boring larvae from it. As an exceptional active acoustic actuator, this finger makes an aye-aye’s biological system an attractive model for pioneering Nondestructive Evaluation (NDE) methods and robotic systems. Despite the important aspects of the topic in the aye-aye’s unique foraging and its potential contribution to the engineering sensory, little is known about the mechanism and dynamics of this unique finger. This paper used a motion-tracking approach for the aye-aye’s middle finger using simultaneous video graphic capture. To mimic the motion, a two-link robot arm model is designed to reproduce the trajectory. Kinematics formulations were proposed to derive the motion of the middle finger using the Lagrangian method. In addition, a hardware model was developed to simulate the aye-aye’s finger motion. To validate the model, different motion states such as trajectory paths and joint angles, were compared. The simulation results indicate the kinematics of the model were consistent with the actual finger movement. This model is used to understand the aye-aye’s unique tap-scanning process for pioneering new tap-testing NDE strategies for various inspection applications.