The human hand is a highly developed and sophisticated grasping organ containing 27 bones with 36 articulations and 39 active muscles . This contributes to a wide range of motion (ROM) (31 degrees of freedom – DOF) while possessing sensitive haptic properties. For controlling this complex system, a high level of interaction between the human brain and the musculoskeletal structure is required. To address various malfunctions because of to disorders of the musculoskeletal system, the inverse dynamics modelling approach is an increasingly applied method.
With this method, the complex dynamic force distribution in all hand structures can be analyzed in numerous kinds of tasks for physiological as well as for pathological simulations. Research questions regarding the prevention or rehabilitation of the biomechanics of the hand can be explained without the requirement for in vivo or in vitro experiments. Mechanical loads within the hand do not only affect muscle activities and forces in the surrounding joints but also lead to balancing forces in the entire body.
Therefore, a diversified field of problems does not rely on the biomechanics of an isolated hand model alone, but an embedment into a holistic human body model. Numerous research groups conducted musculoskeletal simulations of the human hand over recent decades.
Holzbaur et a.  implemented an entire upper limb model, including the human hand within the OpenSim  framework. This model is based on the experimental and anatomical data of , , ,  and . The model copes with 26 muscles crossing the wrist and finger joints, but lacks the intrinsic muscles.
Lee et al.  solved this limitation by implementing intrinsic muscles for the fingers. On the basis of the experimental data of , the muscle pathing was optimized to achieve an improved alignment with the moment arm behavior of each joint [10,11]. Further enhancements regarding the length-dependent passive properties of the extrinsic index finger muscles was done by Binder-Markey and Murray .
The model from Ma’touq et al.  also included the biomechanics of the thumb and its intrinsic muscles based on the same literature data  and . In contrast to the previous models, this one implements the human forearm and hand as a standalone framework in Simulink® (The MathWorks, Inc., USA).
As proposed by  and , the usage of one consitent source for anatomic data is fundamental.
Goislard de Monsabert et al.  showed that using multiple sources instead of a single one can lead to errors of up to 180% in the calculated muscle forces. Therefore,  implemented an OpenSim hand/wrist model, based on an anatomical study of a single cadaver specimen .
Nevertheless, it is a standalone model of the upper extremity based on one cadaver and can thus not be used in a broader scope.
The AnyBodyTM Modelling System (AMS) (Anybody, Aalborg, Denmark) is a musculoskeletal modeling platform containing body scaling functions that incorporate body mass and percentage of fat and influence the muscle and bone dimensions accordingly, which features a patient-specific scaling of the hand model. The AMS is a widely applied simulation platform for musculoskeletal modeling using an inverse dynamics approach. Furthermore, it contains sophisticated algorithms to optimize complex motion capture data, like the movements of thumb and fingers.
The AMS also provides the AnyBody Managed Model Repository (AMMR), which includes a generic human body model and a collection of human body parts. The AMMR contained only single fingers in detail [19,20], which are not implemented in the full-body model. Therefore, a complete comprehensive model of the hand was still lacking.
Therefore, the aim of this study was the development and validation of a detailed human hand model within an existing, commonly used framework for inverse dynamics simulation, including:
- Development of a hand model with all intrinsic and extrinsic muscles of the entire hand based on an anatomical data set of one consistent source 
- Comparison of experimental and numerical gained muscle moment arms 
- Experimental validation by comparing numerical determined muscle activities and measure electromyographical data 
- Investigating the impact of anatomical uncertainties on the model 
- First application of the model in the scope of obstetrics 
This work was supported by the project no. 182 “Obstetrics 2.0 – virtual models for injury prevention during childbirth” realised within the frame of the Program INTERREG V-A: Cross-border cooperation between the Czech Republic and the Federal State of Germany, Bavaria, Aim European Cross-border Cooperation 2014–2020.