I am a Smartwatch and I can Track my User's Arm
I am a Smartwatch and I can Track my User's Arm
复制标题
DOI：
10.1145/2906388.2906407
复制
发表时间：
2016-06
期刊：
影响因子：
--
通讯作者：
He Wang
中科院分区：
文献类型：
--
作者：
He Wang
本文旨在使用智能手表上的运动和磁性传感器来跟踪整个手臂（包括手腕和肘部）的3D姿势。我们不打算使用机器学习来训练系统的特定手势集。相反，我们的目标是跟踪手臂的几何运动，然后可以用作基于手势的应用程序的通用平台。这个问题很有挑战性，因为手臂的姿势是肘部和肩部运动的函数，而手表只是手腕上的一个（有噪声的）测量点。此外，虽然其他跟踪系统（如室内/室外定位）通常受益于地图或地标，偶尔重置其估计，但这种机会几乎不存在。虽然这似乎是一个约束不足的问题，我们发现，前臂的指向方向强烈耦合到手臂的姿势。如果手表上的陀螺仪和指南针可以估计这个方向，3D搜索空间可以变得更小;然后可以应用IMU传感器来减轻剩余的不确定性。我们利用这一观察结果来设计ArmTrak，该系统将IMU传感器和手臂关节的解剖结构融合到修改后的隐马尔可夫模型（HMM）中，以连续估计状态变量。使用Kinect 2.0作为地面实况，我们实现了自由形式姿势的中位误差约为9.2 cm;对于真实的时间版本，误差增加到13.3 cm。我们相信这是姿势跟踪的一个进步，通过一些额外的工作，可以成为各种实际应用的通用工具。
This paper aims to track the 3D posture of the entire arm - both wrist and elbow - using the motion and magnetic sensors on smartwatches. We do not intend to employ machine learning to train the system on a specific set of gestures. Instead, we aim to trace the geometric motion of the arm, which can then be used as a generic platform for gesture-based applications. The problem is challenging because the arm posture is a function of both elbow and shoulder motions, whereas the watch is only a single point of (noisy) measurement from the wrist. Moreover, while other tracking systems (like indoor/outdoor localization) often benefit from maps or landmarks to occasionally reset their estimates, such opportunities are almost absent here. While this appears to be an under-constrained problem, we find that the pointing direction of the forearm is strongly coupled to the arm's posture. If the gyroscope and compass on the watch can be made to estimate this direction, the 3D search space can become smaller; the IMU sensors can then be applied to mitigate the remaining uncertainty. We leverage this observation to design ArmTrak, a system that fuses the IMU sensors and the anatomy of arm joints into a modified hidden Markov model (HMM) to continuously estimate state variables. Using Kinect 2.0 as ground truth, we achieve around 9.2 cm of median error for free-form postures; the errors increase to 13.3 cm for a real time version. We believe this is a step forward in posture tracking, and with some additional work, could become a generic underlay to various practical applications.