"Elbows Out"-Predictive Tracking of Partially Occluded Pose for Robot-Assisted Dressing
"Elbows Out"-Predictive Tracking of Partially Occluded Pose for Robot-Assisted Dressing
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DOI：
10.1109/lra.2018.2854926
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发表时间：
2018-10-01
影响因子：
5.2
通讯作者：
Dogramadzi, Sanja
中科院分区：
文献类型：
--
作者：
Chance, Greg;Jevtic, Aleksandar;Dogramadzi, Sanja
可以帮助日常生活活动的机器人，如穿衣，可以支持老年人，解决人口老龄化在护理专业人员日益短缺的情况下的需求。在机器人辅助敷料期间使用深度相机可能导致遮挡和用户跟踪丢失，这可能导致不安全的轨迹规划或完全阻止规划任务的进行。对于本信中所述的穿上夹克的穿衣任务，当用户的手进入夹克时，手臂的跟踪丢失，这可能导致用户的不安全情况和较差的交互体验。使用运动跟踪数据，从辅助穿衣任务的人机交互研究中收集的无遮挡的数据，建立递归神经网络模型，以根据用户姿势的其他特征预测单臂手肘位置。预测手肘位置的最佳特征是通过使用回归树来探索的，回归树指示髋关节和肩关节作为可能的预测因子。还根据对真实的敷料场景的观察创建了工程特征，并对其有效性进行了探索。本研究还包括基于位置和方向的数据集之间的比较。对每个特征集进行12倍交叉验证，并重复20次以提高统计功效。使用基于位置的数据，可以预测手肘位置，误差为4.1 cm，但添加工程特征将误差降低到2.4 cm。向数据中添加方向信息并没有提高准确性，并且汇总单变量响应模型未能取得显着改善。该模型进行了评估Kinect数据的机器人穿衣任务，虽然不是没有问题，证明了这种应用的潜力。虽然这已经被证明是用于护套敷料，但该技术可以应用于闭塞跟踪期间的许多不同情况。
Robots that can assist in the activities of daily living, such as dressing, may support older adults, addressing the needs of an aging population in the face of a growing shortage of care professionals. Using depth cameras during robot-assisted dressing can lead to occlusions and loss of user tracking, which may result in unsafe trajectory planning or prevent the planning task proceeding altogether. For the dressing task of putting on a jacket, which is addressed in this letter, tracking of the arm is lost when the user's hand enters the jacket, which may lead to unsafe situations for the user and a poor interaction experience. Using motion tracking data, free from occlusions, gathered from a human human interaction study on an assisted dressing task, recurrent neural network models were built to predict the elbow position of a single arm based on other features of the user pose. The best features for predicting the elbow position were explored by using regression trees indicating the hips and shoulder as possible predictors. Engineered features were also created based on observations of real dressing scenarios and their effectiveness explored. Comparison between position and orientation-based datasets was also included in this study. A 12-fold cross-validation was performed for each feature set and repeated 20 times to improve statistical power. Using position-based data, the elbow position could be predicted with a 4.1 cm error but adding engineered features reduced the error to 2.4 cm. Adding orientation information to the data did not improve the accuracy and aggregating univariate response models failed to make significant improvements. The model was evaluated on Kinect data for a robot dressing task and although not without issues, demonstrates potential for this application. Although this has been demonstrated for jacket dressing, the technique could be applied to a number of different situations during occluded tracking.