A musculoskeletal model driven by dual Microsoft Kinect Sensor data

Abstract

Musculoskeletal modeling is becoming a standard method to estimate muscle, ligament and joint forces non-invasively. As input, these models often use kinematic data obtained using marker-based motion capture, which, however, is associated with several limitations, such as soft tissue artefacts and the time-consuming task of attaching markers. These issues can potentially be addressed by applying marker-less motion capture. Therefore, we developed a musculoskeletal model driven by marker-less motion capture data, based on two Microsoft Kinect Sensors and iPi Motion Capture software, which incorporated a method for predicting ground reaction forces and moments. For validation, selected model outputs (e.g. ground reaction forces, joint reaction forces, joint angles and joint range-of-motion) were compared to musculoskeletal models driven by simultaneously recorded marker-based motion capture data from 10 males performing gait and shoulder abduction with and without external load. The primary findings were that the vertical ground reaction force during gait and the shoulder abduction/adduction angles, glenohumeral joint reaction forces and deltoideus forces during both shoulder abduction tasks showed comparable results. In addition, shoulder abduction/adduction range-of-motions were not significantly different between the two systems. However, the lower extremity joint angles, moments and reaction forces showed discrepancies during gait with correlations ranging from weak to strong, and for the majority of the variables, the marker-less system showed larger standard deviations. Although discrepancies between the systems were identified, the marker-less system shows potential, especially for tracking simple upper-body movements.

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Abbreviations

MBS:

Marker-based system

MKS:

Microsoft Kinect Sensor

ROM:

Range-of-motion

MGRF:

Measured ground reaction force

PGRF:

Predicted ground reaction force

MLS:

Marker-less system

PCSA:

Physiological cross-sectional area

SA:

Unloaded shoulder abduction

LSA:

Loaded shoulder abduction

GRF:

Ground reaction force

AMS:

AnyBody Modeling System

JRF:

Joint reaction force

\(r\) :

Pearson’s correlation coefficient

RMSD:

Root-mean-square deviation

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Acknowledgements

This work was supported by the Danish Agency for Science, Technology and Innovation under the Patient@Home project to M. S. Andersen and J. Yang and the Danish Council for Independent Research under grant number DFF-4184-00018 to M. S. Andersen.

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Correspondence to Michael S. Andersen.

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Skals, S., Rasmussen, K.P., Bendtsen, K.M. et al. A musculoskeletal model driven by dual Microsoft Kinect Sensor data. Multibody Syst Dyn 41, 297–316 (2017). https://doi.org/10.1007/s11044-017-9573-8

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Keywords

  • Marker-less motion capture
  • Microsoft Kinect Sensor
  • iPi Motion Capture
  • Ground reaction force prediction
  • Musculoskeletal modeling
  • AnyBody Modeling System