A musculoskeletal model driven by dual Microsoft Kinect Sensor data
A musculoskeletal model driven by dual Microsoft Kinect Sensor data
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DOI：
10.1007/s11044-017-9573-8
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发表时间：
2017-12-01
影响因子：
3.4
通讯作者：
Andersen, Michael S.
中科院分区：
文献类型：
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作者：
Skals, Sebastian;Rasmussen, Kasper P.;Andersen, Michael S.
肌肉骨骼建模正在成为一种无创估计肌肉、韧带和关节力量的标准方法。作为输入，这些模型通常使用使用基于标记的运动捕捉获得的运动学数据，然而，这与一些限制相关，例如软组织伪影和附加标记的耗时任务。这些问题可能可以通过应用无标记运动捕捉来解决。因此，我们开发了一个由无标记运动捕捉数据驱动的肌肉骨骼模型，该模型基于两个Microsoft Kinect传感器和IPI运动捕捉软件，其中包含了预测地面反作用力和力矩的方法。为了验证，选定的模型输出(例如，地面反作用力、关节反作用力、关节角度和关节活动范围)与肌肉骨骼模型进行了比较，该模型由10名男性在有和没有外部负荷的情况下进行步态和肩部外展的同时记录的基于标记的运动捕捉数据驱动。初步研究结果表明，步态中垂直地面反作用力与两种外展任务中肩关节的外展/内收角、关节反作用力和三角肌力具有可比性。此外，肩关节外展/内收活动范围在两个系统之间没有显著差异。然而，在步态过程中，下肢关节角度、力矩和反作用力表现出从弱到强的相关性，并且对于大多数变量，无标记系统显示出较大的标准偏差。尽管系统之间的差异被识别出来，但无标记系统显示出潜力，特别是在跟踪简单的上半身运动方面。
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.