Walk the Robot: Exploring Soft Robotic Morphological Communication driven by Spiking Neural Networks

Recently, researchers have explored control methods that embrace nonlinear dynamic coupling instead of suppressing it. Such designs leverage dynamical coupling for communication between different parts of the robot. Morphological communication refers to when those dynamics can be used as an emergent data bus to facilitate coordination among independent controller modules within the same robot. Previous research with tensegrity-based robot designs has shown that evolutionary learning models that evolve spiking neural networks (SNN) as robot control mechanisms are effective for controlling non-rigid robots. Our own research explores the emergence of morphological communication in an SNN-based simulated soft robot in theEvoGym environment.

Enhanced Knee Kinematics: Leveraging Deep Learning and Morphing Algorithms for 3D Implant Modeling

Accurate reconstruction of implanted knee models is crucial in orthopedic surgery and biomedical engineering, enhancing preoperative planning, optimizing implant design, and improving surgical outcomes. Traditional methods rely on labor-intensive and error-prone manual segmentation. This study proposes a novel approach using machine learning (ML) algorithms and morphing techniques for precise 3D reconstruction of implanted knee models. The methodology begins with acquiring preoperative imaging data, such as fluoroscopy or X-ray images of the patient's knee joint. A convolutional neural network (CNN) is then trained to automatically segment the femur contour of the implanted components, significantly reducing manual effort and ensuring high accuracy. Following segmentation, a morphing algorithm generates a personalized 3D model of the implanted knee joint, using the segmented data and biomechanical principles. This algorithm considers implant position, size, and orientation to simulate the knee joint's shape. By integrating morphological data with implant-specific parameters, the reconstructed models accurately reflect the patient's implant anatomy and configuration. The approach's effectiveness is demonstrated through quantitative evaluations, including comparisons with ground truth data and existing techniques. In 19 test cases involving various implant types, the ML-based segmentation method showed superior accuracy and consistency compared to manual segmentation, with an average RMS error of 0.58 +/- 0.14 mm. This research advances orthopedic surgery by providing a robust framework for the automated reconstruction of implanted knee models. Leveraging ML and morphing algorithms, clinicians and researchers gain valuable insights into patient-specific knee anatomy, implant biomechanics, and surgical planning, leading to improved patient outcomes and enhanced quality of care.