Portable, open-source solutions for estimating wrist position during reaching in people with stroke.
Portable, open-source solutions for estimating wrist position during reaching in people with stroke.
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DOI：
10.1038/s41598-021-01805-2
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发表时间：
2021-11-18
影响因子：
4.6
通讯作者：
Slutzky MW
中科院分区：
文献类型：
--
作者：
Nie JZ;Nie JW;Hung NT;Cotton RJ;Slutzky MW
手臂运动学可能提供一种比现有指标更敏感的方法来评估神经康复结果。然而，测量中风患者的手臂运动学对于传统的光学跟踪系统来说可能是具有挑战性的，这是由于非理想的环境、费用和难以执行所需的校准。在这里，我们提出了两种开源方法，一种使用惯性测量单元（伊穆斯），另一种使用虚拟现实（Vive）传感器，用于精确测量中风患者在到达运动期间手腕相对于肩部的位置。我们评估了每种方法在3D到达任务中的准确性。我们还证明了每种方法的能力，跟踪两个指标来自运动学扫描面积和平滑度的人与慢性中风。适当时，我们计算了每种方法估计的运动学之间的相关系数。与传统的光学跟踪系统相比，这两种方法在伸手过程中都能准确地跟踪手腕，伊穆斯和Vive的平均符号误差分别为0.09 ± 1.81 cm和0.48 ± 1.58 cm。此外，两种方法估计的运动学高度相关（p < 0.01）。通过使用相对便宜的可穿戴传感器，这些方法可能有助于开发运动学指标，以评估实验室和临床环境中的中风康复结果。
Arm movement kinematics may provide a more sensitive way to assess neurorehabilitation outcomes than existing metrics. However, measuring arm kinematics in people with stroke can be challenging for traditional optical tracking systems due to non-ideal environments, expense, and difficulty performing required calibration. Here, we present two open-source methods, one using inertial measurement units (IMUs) and another using virtual reality (Vive) sensors, for accurate measurements of wrist position with respect to the shoulder during reaching movements in people with stroke. We assessed the accuracy of each method during a 3D reaching task. We also demonstrated each method’s ability to track two metrics derived from kinematics-sweep area and smoothness-in people with chronic stroke. We computed correlation coefficients between the kinematics estimated by each method when appropriate. Compared to a traditional optical tracking system, both methods accurately tracked the wrist during reaching, with mean signed errors of 0.09 ± 1.81 cm and 0.48 ± 1.58 cm for the IMUs and Vive, respectively. Furthermore, both methods’ estimated kinematics were highly correlated with each other (p < 0.01). By using relatively inexpensive wearable sensors, these methods may be useful for developing kinematic metrics to evaluate stroke rehabilitation outcomes in both laboratory and clinical environments.
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DOI：
10.3390/s18010076
发表时间：
2017-12-28
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Fan B;Li Q;Liu T
通讯作者：
Liu T
DOI：
10.1097/00004356-198112000-00001
发表时间：
1981-01-01
影响因子：
1.7
作者：
LYLE, RC
通讯作者：
LYLE, RC
影响因子：
25
作者：
Mathis, Alexander;Mamidanna, Pranav;Bethge, Matthias
通讯作者：
Bethge, Matthias
DOI：
10.1109/icorr.2011.5975346
发表时间：
2011-01-01
期刊：
IEEE ... International Conference on Rehabilitation Robotics : [proceedings]
影响因子：
--
作者：
Madgwick, Sebastian O H;Harrison, Andrew J L;Vaidyanathan, Andrew
通讯作者：
Vaidyanathan, Andrew
影响因子：
4.8
作者：
Krakauer, JW
通讯作者：
Krakauer, JW