Improving IMU-Based Prediction of Lower Limb Kinematics in Natural Environments Using Egocentric Optical Flow
Improving IMU-Based Prediction of Lower Limb Kinematics in Natural Environments Using Egocentric Optical Flow
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DOI：
10.1109/tnsre.2022.3156884
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发表时间：
2022-01-01
影响因子：
4.9
通讯作者：
Rombokas, Eric
中科院分区：
文献类型：
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作者：
Sharma, Abhishek;Rombokas, Eric
我们试图使用可穿戴传感器来预测膝盖和脚踝的运动。这些预测可以作为下肢假体的目标轨迹。在这篇手稿中，我们研究了使用自我中心视觉来提高运动学可穿戴运动捕捉的性能。我们提供了一个实验室外的数据集，其中包括23名健康受试者，他们在公共教室、大中庭和楼梯上航行，总共记录了近12个小时。预测任务很困难，因为动作包括躲避障碍物、其他人、特殊动作，如穿越门，以及在选择未来道路时的个人选择。我们证明，使用视觉改善了预测的膝盖和脚踝轨迹的质量，特别是在拥挤的空间和视觉环境提供的信息不是简单地出现在身体运动中的时候。总体而言，包括视力在内，膝关节和脚踝角度预测的均方根误差分别改善了7.9%和7.0%。皮尔逊相关系数对膝关节和脚踝预测的改善分别为1.5%和12.3%。我们讨论了视觉极大地改善或未能改善预测性能的特定时刻。我们还发现，更多的数据可以增强VISION的好处。最后，我们讨论了在自然的、非实验室的数据集中连续估计步态的挑战。
We seek to predict knee and ankle motion using wearable sensors. These predictions could serve as target trajectories for a lower limb prosthesis. In this manuscript, we investigate the use of egocentric vision for improving performance over kinematic wearable motion capture. We present an out-of-the-lab dataset of 23 healthy subjects navigating public classrooms, a large atrium, and stairs for a total of almost 12 hours of recording. The prediction task is difficult because the movements include avoiding obstacles, other people, idiosyncratic movements such as traversing doors, and individual choices in selecting the future path. We demonstrate that using vision improves the quality of the predicted knee and ankle trajectories, especially in congested spaces and when the visual environment provides information that does not appear simply in the movements of the body. Overall, including vision results in 7.9% and 7.0% improvement in root mean squared error of knee and ankle angle predictions respectively. The improvement in Pearson Correlation Coefficient for knee and ankle predictions is 1.5% and 12.3% respectively. We discuss particular moments where vision greatly improved, or failed to improve, the prediction performance. We also find that the benefits of vision can be enhanced with more data. Lastly, we discuss challenges of continuous estimation of gait in natural, out-of-the-lab datasets.