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Motion reconstruction provides essential inputs for analyzing human movement through musculoskeletal simulations. To reconstruct joint angles from motion capture data, several multibody kinematic optimization methods have been developed. However, a computationally efficient method yet simple to implement while ensuring consistent kinematics at all levels is lacking. Here, we propose a potential field method generated by virtual spring-dampers connecting measured-derived skin markers to segment-fixed model points to reconstruct motion in a forward dynamic manner by solving the equations of motion. The virtual spring-damper forces move the mechanical system to minimize the elastic potential and the distance between markers during the motion. Several evaluation strategies are performed which demonstrate that the potential field method is computationally fast (2.5ms per frame) with comparable accuracy to the well-established least squares method in terms of reconstructed marker trajectories and joint angles (RMSE < 0.37 mm, 1.87°) and with low marker residuals (< 18.7 ± 12.6 mm) in line with reported ranges. Furthermore, soft tissue artifacts are compensated well compared to the simulated true values (RMSE < 1.66 mm, 3.69°). Sternoclavicular, scapulothoracic and glenohumeral rotations were reconstructed well the major trends and magnitudes of experimental measurements. We anticipate our method will pave the way for complex applications that demand reliable and rapid large-scale biomechanical analysis of human movement.

Digital twins are virtual models of physical artefacts that may or may not be synchronously connected, and that can be used to simulate their behavior. They are widely used in several domains such as manufacturing and automotive to enable achieving specific quality goals. In the health domain, so-called digital patient twins have been understood as virtual models of patients generated from population data and/or patient data, including, for example, real-time feedback from wearables. Along with the growing impact of data science technologies like artificial intelligence, novel health data ecosystems centered around digital patient twins could be developed. This paves the way for improved health monitoring and facilitation of personalized therapeutics based on management, analysis, and interpretation of medical data via digital patient twins. The utility and feasibility of digital patient twins in routine medical processes are still limited, despite practical endeavors to create digital twins of physiological functions, single organs, or holistic models. Moreover, reliable simulations for the prediction of individual drug responses are still missing. However, these simulations would be one important milestone for truly personalized therapeutics. Another prerequisite for this would be individualized pharmaceutical manufacturing with subsequent obstacles, such as low automation, scalability, and therefore high costs. Additionally, regulatory challenges must be met thus calling for more digitalization in this area. Therefore, this narrative mini-review provides a discussion on the potentials and limitations of digital patient twins, focusing on their potential bridging function for personalized therapeutics and an individualized pharmaceutical manufacturing while also looking at the regulatory impacts.