# Validation and calibration processes of a musculoskeletal model of the hand and forearm

Nicolas Vignais, Frédéric Marin

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Nicolas Vignais, Frédéric Marin. Validation and calibration processes of a musculoskeletal model of the hand and forearm. Computer Methods in Biomechanics and Biomedical Engineering, 2012, Berlin, Germany. hal-04863417

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# VALIDATION AND CALIBRATION PROCESSES OF A MUSCULOSKELETAL MODEL OF THE HAND AND FOREARM

N. Vignais<fnref n="1" /> and F. Marin<fnref n="2" />

## 1. ABSTRACT

Musculoskeletal disorders linked to professional manual tasks can now be considered as a public health issue because of their associated costs. In order to prevent these work-related disorders, knowledge of internal biomechanical parameters of the hand and forearm such as muscle forces and joint loads is essential. To this aim, a biomechanical model of the hand and forearm representing the whole complexity of this musculoskeletal system (21 segments, 21 joints and 46 musculotendon units) has been developed. In order to confirm results obtained by the musculoskeletal model of the hand and forearm, a validation process is performed based on EMG signals and external forces (from a manual dynamometer). Two validation parameters have been defined to compare real measures and estimations from the model: muscle activation and the resulting external force. These parameters are compared for different industrial manual tasks (grasping, pinching, holding...). Although amplitude differences are observed, first results show qualitative correspondences between experimental and simulation data. In order to refine the musculoskeletal model of the hand and forearm, a calibration process based on the coupled measure of EMG and manual dynamometer is proposed. Both processes are discussed and results provided by the musculoskeletal model are detailed. This work is a preliminary step to improve the robustness of musculoskeletal modelling for biomechanical and ergonomic analyses.

## 2. INTRODUCTION

Manual tasks can be considered as any task involving lifting, pushing, pulling, carrying, moving manipulating, holding, or restraining a person, animal, or item. When performed in a hazardous way, these manual activities lead to musculoskeletal disorders (MSDs) (Burgess-Limerick 2007). For example, the prevalence of musculoskeletal pain in the wrist/hand anatomical area was equal to 23.2% in the Dutch population aged 25 years and over (Picaven and Schouten 2003). In Australia, manual tasks at work represented 42% of all claims in six years to June 2003 and had associated costs of $12 billion (Burgess-Limerick 2007). In France in 2007, 44.5% of occupational diseases consisted of MSDs located in elbow, wrist, hand and digits (Euzenat, 2010).
In order to detect hazardous manual tasks and prevent from MSDs appearance, knowledge of the internal biomechanical parameters of the musculoskeletal system of the hand and forearm is fundamental. As such in vivo measurements are difficult to obtain (Rasmussen et Dendorfer, 2009), some computer simulations and mathematical

<footnote n="1">¹Postdoctoral Research Assistant, BioMechanics and BioEngineering (UMR7338), University of Technology of Compiègne, Research Centre, rue du dct Schweitzer, 60200 Compiègne, France</footnote>

<footnote n="2">² Professor of Biomedical Engineering, BioMechanics and BioEngineering (UMR7338), University of Technology of Compiègne, Research Centre, rue du dct Schweitzer, 60200 Compiègne, France</footnote>
models have been developed. Because of the complexity and the redundancy of
anatomical structures of the hand and forearm, several sophisticated musculoskeletal
models of substructures have been suggested (Sancho-Bru et al., 2001; Valero-Cuevas
et al., 2003; Vigouroux et al., 2009; Wu et al., 2009). However, some researchers have
also designed biomechanical models that take into account the whole complexity of the
musculoskeletal system of the hand and forearm (Albrecht et al., 2003; Tsang et al.,
2005; Chalfoun et al., 2005; Li et Zhang, 2009; Johnson et al., 2010). More recently a
complete musculoskeletal model of the hand and forearm composed of 21 segments, 20
articulations (providing 24 degrees of freedom) and 46 muscle units has been developed
and used to estimate muscle forces and joint loads during a cylinder manual task
(Vignais et Marin, 2011).

However, one the main problem with musculoskeletal modeling is the lack of studies reporting successful validation of internal parameters estimates. Musculoskeletal validation processes depend on the biomechanical modeling approach. Firstly, the inverse approach estimates muscle force by using external force data combined with inverse dynamics and static optimization (Tsirakos et al., 1997). Muscle forces estimated are typically compared to EMG activity patterns to validate results of this approach. More precisely, the optimization technique used for the investigation of muscle behaviour of the upper extremity is based on minimizing sum of squared muscle forces. Secondly, in the forward approach, an initial set of muscle activations are fed into a forward dynamics model of the musculoskeletal system to estimate the produced movement. Then the solution is compared against experimental data and the process is iterated by updating the muscle activations that best reproduce the experimental kinematics (Erdemir et al., 2007). Thus the validation process of models based on the forward approach is commonly assessed by its performance while tracking experimental data (kinematics, kinetics, EMG). Concerning estimation of muscle forces, the validation is solely based on comparing EMG data with estimated muscle activation patterns.

The purpose of this study is therefore to design a validation process for a
musculoskeletal model of the hand and forearm based on experimental data. This
process will be performed at two levels: the first will deal with muscle activation
patterns comparison and the second will concern external forces comparison. A
calibration process will be proposed based on results from the validation process.

3. MATERIALS AND METHODS

3.1 Musculoskeletal modeling of the hand and forearm

In order to model the musculoskeletal model of the hand and forearm, a three-step
process has been developed (see Fig. 1):
<figure><img src="image_2.png" /><figcaption>Figure 1: A three-step process simulation to compute joint loads and muscle forces from a musculoskeletal model of the hand and forearm.</figcaption></figure>

- Firstly, an accurate musculoskeletal model of the hand and forearm has been designed under the LifeMOD simulation software (MSC Software Inc., Santa Ana, CA, USA). The model is composed of 21 segments, 21 joints providing 26 degrees of freedom, and 46 muscle units.

- Secondly, motion capture data are imported in the modeling software. Kinematical data have been recorded during three manual tasks: a maximal grasping task, a maximal pressing task for digit 3 and a disk gripping task. An optoelectronic system (VICON, Oxford Metrics Ltd, Oxford, UK) was used to track the position of passive markers in three dimensions. This system is composed of 7 infrared cameras with a resolution of 16 megapixels which recorded the position of 43 reflective markers of 3 mm-size attached to different bony landmarks on hand and forearm segments. During these experiments, EMG acquisitions of flexor digitorum superficialis (FDS) and extensor digitorum communis (EDC) have been performed. Moreover, a manual dynamometer enabled to record external forces during maximal grasping and pressing tasks (see Fig. 2).

<figure><img src="image_3.png" /><figcaption>Figure 2: Manual tasks recorded during motion capture experiments: maximal grasping (a), disk gripping (b) and maximal pressing (c).</figcaption></figure>

- Thirdly, an inverse-to-forward dynamics simulation is run. Based on real kinematical data, the inverse dynamics simulation is firstly done to drive the model whereas the trainable elements in joints and muscles record joint angulations and muscle-contraction histories. Then these recordable elements are replaced with trained active elements in a subsequent forward dynamics simulation based on proportional derivative servo controllers. During the simulation, contact forces are also computed using an ellipsoid-plate contact
elements algorithm from Adams solver embedded in LifeMOD plugin.

## 3.2 Validation process

In order to validate results obtained by the musculoskeletal model of the hand and forearm, a two-step validation process is conducted. In the first step, we will compare muscle activation derived from EMG data recording during motion capture experiments, and muscle activation assessed by the musculoskeletal model of the hand and forearm. In the second step, experimental data of external force (obtained with the manual dynamometer) will be compared with resulting external force computed by the model during the simulation.

# 4. RESULTS

## 4.1 Muscle activation

For experimental EMG data, muscle activation is derived from the method of Buchanan and colleagues (2004). The musculoskeletal model of the hand and forearm also provided simulated muscle activation (see Fig.3). Comparative graphics have been plotted for the maximal grasping task.

<figure><img src="image_4.png" /><figcaption>Figure 3: Validation method for EMG data. Experimental and estimated data have been obtained during the maximal grasping task.</figcaption></figure>

## 4.2 External forces

The manual dynamometer used during motion capture experiments provided resulting external forces on the longitudinal axis. By integrating a contact force algorithm in the environment of the musculoskeletal model, we were able to obtain resulting external forces applied on the manual dynamometer during the maximal grasping task. Both values, experimental and evaluated, have been compared and showed qualitative correspondences.

# 5. DISCUSSION

## 5.1 Validation process
The validation process employed on this study demonstrated a qualitative correlation between experimental data and biomechanical values estimated by the musculoskeletal model. Up to know, EMG data from the literature have been largely used to validate musculoskeletal models. However, it appears that only few authors have captured experimental EMG data to compare it with their own model (Erdemir et al., 2007). Moreover, due to the complexity of modeling contact forces in a musculoskeletal model of the upper limb, it has been difficult in the past to compare external force values. Nevertheless, our results show that it can be used in future works on musculoskeletal modeling. Besides, using MRI acquisition to model the external volume of the musculoskeletal model, i.e. skin volume, would be an interesting idea to improve contact forces estimations.

## 5.2 A calibration process proposal

More than a validation process, comparing experimental values and simulated values can be used to calibrate the musculoskeletal model of the hand and forearm. We propose to employ a methodology based on 'landmark points' to process the calibration (Perng et al., 2000). This process is applied to data recorded and estimated during the disk gripping task. However, more EMG data are needed to accurately calibrate the model.

## 6. CONCLUSION

This study proposes a specific validation process to support musculoskeletal modeling of the hand and forearm. This validation is based on EMG data and external force comparisons. Results demonstrated qualitative correlations between experimental and simulated data. A calibration process can then be proposed to reach towards qualitative correspondences. Further works considering contact forces algorithm or EMG acquisitions appear necessary to complete the validation-to-calibration process of the musculoskeletal model of the hand and forearm.

## 7. REFERENCES

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3. Euzenat, D. L'exposition des salariés aux maladies professionnelles en 2007, 2010.

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