Inertial measurement unit-based pose estimation: Analyzing and reducing sensitivity to sensor placement and body measures

Introduction Inertial measurement units have been proposed for automated pose estimation and exercise monitoring in clinical settings. However, many existing methods assume an extensive calibration procedure, which may not be realizable in clinical practice. In this study, an inertial measurement unit-based pose estimation method using extended Kalman filter and kinematic chain modeling is adapted for lower body pose estimation during clinical mobility tests such as the single leg squat, and the sensitivity to parameter calibration is investigated. Methods The sensitivity of pose estimation accuracy to each of the kinematic model and sensor placement parameters was analyzed. Sensitivity analysis results suggested that accurate extraction of inertial measurement unit orientation on the body is a key factor in improving the accuracy. Hence, a simple calibration protocol was proposed to reach a better approximation for inertial measurement unit orientation. Results After applying the protocol, the ankle, knee, and hip joint angle errors improved to 4.2 circle,6.3 circle, and 8.3 circle, without the need for any other calibration. Conclusions Only a small subset of kinematic and sensor parameters contribute significantly to pose estimation accuracy when using body worn inertial sensors. A simple calibration procedure identifying the inertial measurement unit orientation on the body can provide good pose estimation performance.

引言 惯性测量单元已被提议用于临床环境中的自动姿态估计和运动监测。然而，许多现有方法假定了一个广泛的校准程序，这在临床实践中可能无法实现。在本研究中，一种基于惯性测量单元的姿态估计方法，使用扩展卡尔曼滤波器和运动链建模，被应用于临床移动性测试（如单腿深蹲）期间的下肢姿态估计，并且对参数校准的敏感性进行了研究。 方法 分析了姿态估计精度对每个运动学模型和传感器放置参数的敏感性。敏感性分析结果表明，准确提取身体上惯性测量单元的方向是提高精度的关键因素。因此，提出了一种简单的校准协议，以更好地近似惯性测量单元的方向。 结果 应用该协议后，踝关节、膝关节和髋关节角度误差分别改善至4.2°、6.3°和8.3°，且无需任何其他校准。 结论 当使用穿戴在身体上的惯性传感器时，只有一小部分运动学和传感器参数对姿态估计精度有显著贡献。一种确定身体上惯性测量单元方向的简单校准程序可以提供良好的姿态估计性能。