通过分析地面反作用力，提升人体质心未来位置预测精度。
Predicting center of mass position in non-cyclic activities: The influence of acceleration, prediction horizon, and ground reaction forces
- 用零、恒定或三次收敛加速度假设预测质心位置。
- 125-250毫秒内使用地面反作用力可显著降低误差和方向错误。
- 250毫秒是实用预测时长的阈值，适合运动意图推断场景。
人体整体质心（CoM）在量化运动中至关重要。通过当前质心位置与速度，向前积分预测未来轨迹需对预测时段内的加速度进行预估。然而，加速度假设方式、预测时长及地面反作用力（GRFs）信息如何影响预测尚不明确。本研究分析了10名健康青年在14种非循环动作中的数据，假设加速度在预测期内为零、恒定或按三次函数趋近于零，并在125至625毫秒的时长下进行预测。通过与全身体标记系统获取的真实轨迹对比，评估位置误差与位移主方向识别准确率。所有加速度假设下，位置误差随时长呈二次增长（R² > 0.930），方向准确率线性下降（R² > 0.615）。事后检验显示，使用GRFs的恒定与三次加速度假设在125和250毫秒时，位置误差（p<0.001，Cohen's d>3.23）与方向准确率（p<0.034，Cohen's d>1.44）均显著优于零加速度假设。结果表明，融合GRFs可提升预测性能，并建议250毫秒为预测应用的时长阈值。
原文摘要 · Abstract (English)
The whole-body center of mass (CoM) plays an important role in quantifying human movement. Prediction of future CoM trajectory, modeled as a point mass under influence of external forces, can be a surrogate for inferring intent. Given the current CoM position and velocity, predicting the future CoM position by forward integration requires a forecast of CoM accelerations during the prediction horizon. However, it is unclear how assumptions about the acceleration, prediction horizon length, and information from ground reaction forces (GRFs), which provide the instantaneous acceleration, affect the prediction. We study these factors by analyzing data of 10 healthy young adults performing 14 non-cyclic activities. We assume that the acceleration during a horizon will be 1) zero, 2) remain constant, or 3) converge to zero as a cubic trajectory, and perform predictions for horizons of 125 to 625 milliseconds. We quantify the prediction performance by comparing the position error and accuracy of identifying the main direction of displacement against trajectories obtained from a whole-body marker set. For all the assumed accelerations profiles, position errors grow quadratically with horizon length ($R^2 > 0.930$) while the accuracy of the predicted direction decreases linearly ($R^2>0.615$). Post-hoc tests reveal that the constant and cubic profiles, which utilize the GRFs, outperform the zero-acceleration assumption in position error ($p<0.001$, Cohen's $d>3.23$) and accuracy ($p<0.034$, Cohen's $d>1.44)$ at horizons of 125 and 250$\,ms$. The results provide evidence for benefits of incorporating GRFs into predictions and point to 250$\,ms$ as a threshold for horizon length in predictive applications.
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