RIS-Aided Kinematic Analysis for Remote Rehabilitation
RIS-Aided Kinematic Analysis for Remote Rehabilitation
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DOI：
10.1109/jsen.2023.3308920
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发表时间：
2023-06
影响因子：
4.3
通讯作者：
Don-Roberts Emenonye;Anik Sarker;A. Asbeck;Harpreet S. Dhillon;R. Buehrer
中科院分区：
文献类型：
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作者：
Don-Roberts Emenonye;Anik Sarker;A. Asbeck;Harpreet S. Dhillon;R. Buehrer
这篇文章是第一篇研究使用可重构智能表面（RIS）作为无源设备的想法，这些设备可以随着时间的推移测量某些人体部位的位置和方向。在这篇文章中，我们调查的可能性，使用现有的几何信息提供的身体上的RISs，反映信号从一个离体发射器到一个离体接收器中风康复。更具体地说，我们调查的可能性，使用对身体的RISs估计的位置信息随着时间的推移，上肢可能已受损，由于中风。该位置信息可以帮助医疗专业人员估计可能随时间变化的姿势并评估上肢康复的进展。我们的分析表明，虽然上肢方向可以估计接收器是在近场的被动RIS，这个方向不能在远场计算。我们还提出了一个下界估计上肢的位置在近场传播制度的可实现的精度。由基于Fisher信息矩阵（Fisher information matrix，HMM）的分析提供的准确度对于上肢的方向和位置分别为0.01 rad和1 cm的量级。该精度可以比从惯性测量单元（伊穆斯）获得的精度更好，并且不会由于漂移而降低。给出的准确度值不特定于任何算法。相反，通过该方法获得的准确度值是任何未来肢体位置估计算法的基准。最后，重要的是要指出，这项工作提供了一个严格的数学框架，以利用已经存在的无线信号来收集有用的家庭健康数据。我们承认，总体而言，RISs仍处于起步阶段，它们在任何环境中的实际使用都取决于硬件的未来发展。
This article is the first to examine the idea of using reconfigurable intelligent surfaces (RISs) as passive devices that measure the position and orientation of certain human body parts over time. In this article, we investigate the possibility of using the available geometric information provided by on-body RISs that reflect signals from an off-body transmitter to an off-body receiver for stroke rehabilitation. More specifically, we investigate the possibility of using on-body RISs to estimate the location information over time of upper limbs that may have been impaired due to stroke. This location information can help medical professionals to estimate the possibly time-varying pose and evaluate progress on the rehabilitation of the upper limbs. Our analysis indicates that while the upper limb orientation can be estimated when the receiver is in the near-field of a passive RIS, this orientation cannot be calculated in the far-field. We also present a lower bound on the achievable accuracy for estimating the upper limbs’ location in the near-field propagation regime. The accuracy provided by the Fisher information matrix (FIM)-based analysis is on the order of 0.01 rad and 1 cm for orientation and position of the upper limbs, respectively. This accuracy can be better than that obtained from inertial measurement units (IMUs), and it does not degrade due to drift. The accuracy values presented are not specific to any algorithm. Instead, the accuracy values obtained through the FIM are a benchmark for any future limb location estimation algorithm. Finally, it is important to state that this work provides a rigorous mathematical framework to take advantage of the wireless signals that are already present to collect useful in-home health data. We acknowledge that RISs, in general, are still in their infancy, and their practical use in any setting depends on future advances in hardware.