A musculoskeletal model of the hand and wrist: model definition and evaluation

Abstract

To improve our understanding on the neuromechanics of finger movements, a comprehensive musculoskeletal model is needed. The aim of this study was to build a musculoskeletal model of the hand and wrist, based on one consistent data set of the relevant anatomical parameters. We built and tested a model including the hand and wrist segments, as well as the muscles of the forearm and hand in OpenSim. In total, the model comprises 19 segments (with the carpal bones modeled as one segment) with 23 degrees of freedom and 43 muscles. All required anatomical input data, including bone masses and inertias, joint axis positions and orientations as well as muscle morphological parameters (i.e. PCSA, mass, optimal fiber length and tendon length) were obtained from one cadaver of which the data set was recently published. Model validity was investigated by first comparing computed muscle moment arms at the index finger metacarpophalangeal (MCP) joint and wrist joint to published reference values. Secondly, the muscle forces during pinching were computed using static optimization and compared to previously measured intraoperative reference values. Computed and measured moment arms of muscles at both index MCP and wrist showed high correlation coefficients (r ¼ 0.88 averaged across all muscles) and modest root mean square deviation (RMSD ¼ 23% averaged across all muscles). Computed extrinsic flexor forces of the index finger during index pinch task were within one standard deviation of previously measured in-vivo tendon forces. These results provide an indication of model validity for use in estimating muscle forces during static tasks.

Modelling force-length-activation relationships of wrist and finger extensor muscles

“…Since direct measurements of muscle force are ethically and technically difficult and only provide measurement for isolated relatively large tendons [8], musculoskeletal models have continuously been developed to provide estimates of the internal mechanics of the finger and the wrist [9][10][11][12][13][14][15][16][17][18]. Those models require anatomical data describing the trajectories and morphology of muscle-tendon units as well as the bone geometries to quantify how each muscle can contribute to the generation of a movement or a force [19][20][21][22].…”

“…Furthermore, the potential contribution of 8 each muscle also varies with the joint configuration as the maximal force it can produce depends on its current length, as described by the well-known force-length relationship [23]. In the most advanced hand musculoskeletal models, the force-length relationship of each muscle is obtained by scaling a generic curve using cadaver data [11,15,16,24]. Although this is a well-accepted way of modelling, such models often require a scaling of certain muscle-tendon unit parameters so they are more representative of participant abilities, especially the maximal joint torques [25,26].…”

Quantifying Soft Tissue Artefacts and Imaging Variability in Motion Capture of the Fingers

“…Nevertheless, we provide data that could be used to support future researchers in calculating their required sample size to ensure statistical power and evaluating potential clinical effect sizes, and offer insights to support previous work undertaken in cadaveric or single case studies. These results would complement advanced soft tissue MSK models, 3,17,23,26,29,30 now finding use in such diverse applications as studying human-device interactions 22 and optimising tenodesis surgery. 9 These results also quantified the error associated with STA affecting and advocating a single surface marker placement protocol on the fingers as an alternative approach to technical marker sets or marker clusters, which has been an ongoing topic of debate in kinematic analysis studies.…”

“…16 Marker-based kinematic measurement 11,27,28,32,34 and MSK modelling techniques have been reported for the hand joints. 3,19,29,37 The cited studies demonstrate that these methods are capable of reliable analysis of functional hand and wrist joint movements in health and disease, and integration into a MSK model can be used to predict kinetics such as joint moments, 5,31 muscle forces in static tasks, 29 and give insights into hand movements from extrinsic muscles with applications in prosthetic hand control. 3 They are often limited by idealising anatomy and its variability and by available input data, requiring a compromise between the detailed measurements available from cadaver models and the representativeness of living human subjects.…”

A new musculoskeletal AnyBody™ detailed hand model

“…As proposed by Mirakhorlo et al (2018) and Kerkhof et al (2018), the usage of one consitent source for anatomic data is fundamental.…”