Towards Digital Twins of the Human Body for Personalized Safety

Research Project, 2025 – 2026

Models of the human body (crash test dummy, finite element models) are used in design and safety assessment of vehicles. However, these are limited to a few design points resulting in systems that are biased against sections of the population. A lack of experimental data and efficient tools historically restricted the scope of safety evaluations to a subset of road users. The current advances in digital resources, AI technology, and physics-aware computational methods open up new opportunities to design tools and workflows that can consider the diversity of road users in safety evaluations.
In this study, we propose to develop a workflow to extract information from
photographs to build individualized models and digital twins that can be used to assess injuries for wider sections of society (females, obese, elderly). The workflow consists of (a) deep learning model to recognize anthropometry parameters (b) mass distribution estimator tuned using convex geometric optimization and (c) a digital twin generator that generates anatomical landmarks and inertial properties required for a human body models. This workflow will enable users to combine existing empirical data from literature with person-specific information. The end users include industry that uses human body models for development of safe vehicles (Volvo, Autoliv) and
researchers (experimental and computational biomechanics).