Conference Agenda

The course will primarily concentrate on establishing the fundamentals of biomechanics and constitutive modeling within biological tissues. It will lay the groundwork for understanding tensor algebra, continuum mechanics, and nonlinear elasticity, progressing to the study of constitutive modeling in biological solids. Additionally, the course will touch on topics such as computational methods, growth, and remodeling, with a consistent emphasis on theoretical analysis and mathematical modeling integrated into the curriculum.

Machine learning is a valuable tool in tackling a wide variety of problems, including those in computational biomechanics. This pre-course will provide a comprehensive introduction to the basics of machine learning tools and their application in computational biomechanics. The course will cover three topics: 1) constitutive modeling using Gaussian processes, 2) deep learning for image segmentation, and 3) physics-informed neural networks. For each topic, the underlying theory will be complemented with hands-on exercises in Python. The knowledge and experience will help researchers use advanced machine learning tools to improve and automate their computational biomechanical analysis.

Despite ISB recommendations aiming to standardise the reporting of kinematic signals, a lack of consensus around joint coordinate frame definitions remains. An approach capable of accommodating different axis methods and reconciling these differences in frame orientation and position is therefore crucial. In this workshop, we present REFRAME (REference FRame Alignment MEthod), an approach capable of providing kinematic patterns that can be reliably compared without requiring exact knowledge of the different segment frame definitions. REFRAME can thus enable the consistent interpretation and comparison of joint kinematics derived using different approaches and collected in different labs.

Digital Image Correlation (DIC) is a technique based on the recording of images of the surface of a test object exhibiting a contrasted patern of grey levels. Images are recorded either with one (2D) or two (Stereo) cameras and the algorithm processes the images for deformation. This provides thousands of individual displacement values at the studied surface. This can then be used to identify material behaviour, validate numerical models, explore material heterogeneities. This course aims a providing a primer of DIC to this audience to help potential users identify potential areas of use.

Hard tissues are fascinating materials that are found in very different contexts. Bone tissue, for example, was amongst the first investigated tissues in biomechanics. However, there remain a number of clinical challenges due to skeletal diseases that affect millions of patients worldwide. Cold-water corals on the other hand are vitally important to sustain marine biodiversity but are threatened by ocean acidification, desoxygenation, and rising water temperatures.

We use these seemingly disparate example tissues to illustrate how constitutive models across several length scales can be developed and used to address emerging research questions. We will also give an overview of computational and experimental methods that are helpful in developing such models.