Special Issue: DataDriven Methods in Biomechanics

Abstract

Machine learning (ML) and artificial intelligence (AI) are impacting all engineering fields and biomechanical engineering is no exception. This special issue aims at showcasing some of the recent methods and applications of ML and AI specific to biomechanics. While ML and AI have had incredible success in their more traditional areas of application such as image processing and classification, new frontiers require the development of new tools or the repurposing of existing tools tailored to the unique types of data encountered in biomechanics problems. Furthermore, blind use of ML and AI techniques is likely to introduce biases, miss the mechanistic connection between physics and data, and ultimately lead to untrustworthy and inaccurate predictions. Integration of datadriven methods with indepth knowledge and experience in biomechanics is paramount. This collection offers a timely snapshot of such integration. For example, solution of partial differential equations (PDEs) for fluid or solid mechanics is central to many biomechanics problems, prompting the development of physicsinformed ML methods. Another class of problems in the field involves the dynamics of human motion, and in particular the role of electrical signaling for muscle activation, leading to specific instantiation of ML tools. Biological systems are inherently noisy, and this variability makes its way across temporal and spatial scales yielding patienttopatient variation and heterogeneities even within the same tissue. Datadriven methods that can capture and propagate these uncertainties are also illustrated in this issue. Lastly, parallel to the development of new ML and AI tools, the creation of standards and benchmarks cannot be emphasized enough. The special issue also includes a paper that deals with this subject.