About this Research Topic
The current trend towards digitalization of human-centred engineering processes in conjunction with advances in (bio-)mechanistic modelling, high-performance computing, artificial intelligence (AI) and sensor technology leads to unprecedented transformation potentials in medical, product and human factors engineering for the enhancement of human-technology interaction as well as medical treatment outcomes. Biomechanical simulations hold high potential by revealing the processes and inner strain conditions of the human body. For reliable simulation results, a model suitable for the application and a way to measure/estimate/predict the human motion behaviour and the interaction with the environment and/or interacting technology are necessary. In this context we refer to a human digital twin as an extension and connection of participant/person-specific biomechanical human models with data streams from clinical observation, operational use of technology or daily life. Each human digital twin is an instance digitally representing a specific person in healthy or pathological state suitable for the specified application.
If interactions between humans and technology are considered, the concept of “digital twin couples” is applicable. It facilitates the use of human digital twins and digital product twins (within its environment) in conjunction with different data streams, which can be measured on the human, the product or at their interface. The combined use of different data streams may enable a more accurate estimation of the states within the overall system “human-technology” in terms of a model-based systems engineering approach, allowing for the optimization of physical human-technology interactions based on simulations, estimations, or predictions with the digital twin couples as well as data transfer between virtual and physical instances of human and technology.
Goal of this Research Topic is to explore human digital twins as personalized biomechanical/musculoskeletal models for person-/patient-specific simulations and their use in human-centred engineering.
These person/patient-specific simulations could allow for calculating biomechanical variables from wearable or unobtrusive sensors, such as inertial sensors and pressure insoles, instead of requiring expensive gold standard lab-based equipment, such as optical motion capture. This would make biomechanical analysis much more accessible to clinical applications, permanent monitoring of usage scenarios or for direct utilization of simulation-based information throughout the interaction. Therefore, alternative measurement, feature extraction, state estimation and prediction methods are necessary, to uncover past, current and prospective states through human digital twin simulation. Digital product twins may be included, if observation/simulation/prediction of product behaviour is necessary in the prospective human-technology interaction use case. For the greatest possible benefit, direct feedback to the user, patient or physician as well as adaption of product behaviour directly during product usage is desirable.
Furthermore, often only anthropometric scaling is performed based on marker data to adapt a generic model to a specific person. Other crucial factors such as muscle strength or mobility are regularly disregarded. More in-depth patient-specific modelling from medical image data is often implemented for very restricted body regions, for which such data is available. Medical imaging is usually indicated for conditions that cause larger deformities of the skeletal system, such as in cerebral palsy or for orthopaedic surgery planning. However, when medical imaging and standard motion capture data are unavailable, alternative methods for person-/patient-specific modelling are needed.
This Research Topic aims to encompass theoretical, computational and experimental studies dealing with novel techniques and methodologies for personalized simulation/optimization of physical human-technology interaction in the fields of medical, product and human factors engineering. Application areas of interest range from medical/rehabilitation technology and orthopaedics through exoskeletons/support systems to mobility and sports products.
The scope of this Topic includes, but is not limited to personalized biomechanical/musculoskeletal modelling and simulation with regard to:
• Modelling, simulation, prediction and optimization of physical interactions
• Sensor technology for digital twin assessment/continuous monitoring
• Novel assessment, feature extraction and state estimation approaches as well as predictive simulations supporting bioengineering processes
• Integration of biomechanistic models with machine learning and AI
If interactions between humans and technology are considered, the concept of “digital twin couples” is applicable. It facilitates the use of human digital twins and digital product twins (within its environment) in conjunction with different data streams, which can be measured on the human, the product or at their interface. The combined use of different data streams may enable a more accurate estimation of the states within the overall system “human-technology” in terms of a model-based systems engineering approach, allowing for the optimization of physical human-technology interactions based on simulations, estimations, or predictions with the digital twin couples as well as data transfer between virtual and physical instances of human and technology.
Goal of this Research Topic is to explore human digital twins as personalized biomechanical/musculoskeletal models for person-/patient-specific simulations and their use in human-centred engineering.
These person/patient-specific simulations could allow for calculating biomechanical variables from wearable or unobtrusive sensors, such as inertial sensors and pressure insoles, instead of requiring expensive gold standard lab-based equipment, such as optical motion capture. This would make biomechanical analysis much more accessible to clinical applications, permanent monitoring of usage scenarios or for direct utilization of simulation-based information throughout the interaction. Therefore, alternative measurement, feature extraction, state estimation and prediction methods are necessary, to uncover past, current and prospective states through human digital twin simulation. Digital product twins may be included, if observation/simulation/prediction of product behaviour is necessary in the prospective human-technology interaction use case. For the greatest possible benefit, direct feedback to the user, patient or physician as well as adaption of product behaviour directly during product usage is desirable.
Furthermore, often only anthropometric scaling is performed based on marker data to adapt a generic model to a specific person. Other crucial factors such as muscle strength or mobility are regularly disregarded. More in-depth patient-specific modelling from medical image data is often implemented for very restricted body regions, for which such data is available. Medical imaging is usually indicated for conditions that cause larger deformities of the skeletal system, such as in cerebral palsy or for orthopaedic surgery planning. However, when medical imaging and standard motion capture data are unavailable, alternative methods for person-/patient-specific modelling are needed.
This Research Topic aims to encompass theoretical, computational and experimental studies dealing with novel techniques and methodologies for personalized simulation/optimization of physical human-technology interaction in the fields of medical, product and human factors engineering. Application areas of interest range from medical/rehabilitation technology and orthopaedics through exoskeletons/support systems to mobility and sports products.
The scope of this Topic includes, but is not limited to personalized biomechanical/musculoskeletal modelling and simulation with regard to:
• Modelling, simulation, prediction and optimization of physical interactions
• Sensor technology for digital twin assessment/continuous monitoring
• Novel assessment, feature extraction and state estimation approaches as well as predictive simulations supporting bioengineering processes
• Integration of biomechanistic models with machine learning and AI
Keywords: Biomechanics, Personalized Musculoskeletal Modelling and Simulation, Human-Technology Interaction, Sensorization, State Estimation Description
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
