The Digital Human Model

All human beings desire physical and mental well-being. Traditional, empirically oriented approaches to achieving and maintaining personal health are reaching their limits. An individualized, biophysics-based Digital Human Model with predictive power is emerging as a revolutionary concept in personalized healthcare. This model is set to transform the understanding, diagnosis, and treatment of diseases and injuries. Additionally, it has the potential to identify and cure diseases with significant socioeconomic impacts, such as cardiovascular diseases, neuromusculoskeletal disabilities, cancer, and rare diseases affecting smaller populations. A Digital Human Model could also be crucial in developing customized technologies to support an aging population, create ergonomically optimized work and living spaces, and introduce a new generation of safety concepts.

To comprehensively understand, for example, a neuromuscular disease and its effects on movement, observing motion data alone is insufficient. The entire sensory feedback system, which controls the interaction of molecular, cellular, and organ system processes, must also be identified. This requires new techniques capable of integrating multiscale models with data-rich phenomena (such as omics data, motion tracks, and electrical activity) and data-poor phenomena (such as in vivo muscle fiber type distributions). Simulations will significantly enrich data-poor scales and domains, a prerequisite for employing advanced machine learning techniques on complex data sets in HPC environments. Additionally, there is a pressing need to incorporate patient-specific data into simulation processes through novel homogenization techniques, paving the way for computer-assisted diagnosis and clinical decision-making.

The quality of simulations in a real-time clinical environment is currently constrained by time limitations, low computational resources, and restricted data access due to data protection laws. Thus, it is crucial to innovate model reduction techniques and self-adaptive numerical schemes to optimally exploit available hardware resources. By integrating cognitive models, utilizing mobile devices, and running simulations in real time, pervasive simulation will open new horizons. These include training on actively controlling and naturally operating neuro-prostheses or preventing lower back pain by subtly influencing the person’s environment. Moreover, this technology facilitates interactions with bio-inspired robotics, such as humanoid robots, personalized exoskeletons in the workplace, or biorobotic devices for guiding natural movements in rehabilitation settings. Research in the Cluster aims to provide the methodological foundation for a Digital Human Model, enabling the delivery of personalized healthcare as envisioned here.