Decoding of human hand actions to handle missing limbs in neuroprosthetics.
Decoding of human hand actions to handle missing limbs in neuroprosthetics.
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DOI：
10.3389/fncom.2015.00027
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发表时间：
2015
影响因子：
3.2
通讯作者：
Faisal AA
中科院分区：
文献类型：
--
作者：
Belić JJ;Faisal AA
我们与世界互动的唯一方式是通过运动，而我们的主要互动是通过手，因此任何手部功能的丧失都会直接影响我们的生活质量。然而，迄今为止，还没有系统地评估手部关节的协调如何影响日常行动。这一点很重要，有两个基本原因。首先，要了解的表示和计算基础的电机控制“在野外”的情况下，其次是开发更智能的控制器的假肢，具有相同的功能，作为自然的肢体。在这项工作中，我们利用我们的手和手指运动在日常生活中的相关结构。我们的想法的新奇在于，而不是平均的变化，我们认为，结构的变化可能包含有价值的信息，正在执行的任务。我们要求7名受试者在17种日常生活情况下进行互动，并使用CyberGlove身体传感器网络以原则性的方式量化行为，该网络在精确校准后跟踪手部的所有主要关节。我们的主要发现是：（1）我们证实，日常生活任务中的手部控制是非常低维的，4到5个维度足以解释自然运动数据中80-90%的变异性。(2)我们建立了一个普遍适用的操作复杂性的测量方法，使我们能够测量和比较不同任务中的肢体运动。我们使用贝叶斯潜变量模型来模拟自然动作中手指关节角度的低维结构。(3)这使我们能够构建一个朴素的分类器，在动作启动的前1000 ms内（从平面手开始配置）预测17个动作中的哪一个将被执行，使我们能够从非常短的时间尺度的初始数据可靠地预测动作意图，进一步揭示了用于神经假肢控制和远程操作目的的手部运动的可预见性。(4)在我们的潜变量模型上使用期望最大化算法，使我们能够通过简单地跟踪剩余的手指来高精度（<5-6° MAE）重建丢失手指的运动轨迹。总的来说，我们的研究结果表明，特定的手部动作是由大脑以这样一种方式精心策划的，即在日常生活的自然任务中，有足够的冗余和可预测性可以直接用于神经修复术。
The only way we can interact with the world is through movements, and our primary interactions are via the hands, thus any loss of hand function has immediate impact on our quality of life. However, to date it has not been systematically assessed how coordination in the hand's joints affects every day actions. This is important for two fundamental reasons. Firstly, to understand the representations and computations underlying motor control “in-the-wild” situations, and secondly to develop smarter controllers for prosthetic hands that have the same functionality as natural limbs. In this work we exploit the correlation structure of our hand and finger movements in daily-life. The novelty of our idea is that instead of averaging variability out, we take the view that the structure of variability may contain valuable information about the task being performed. We asked seven subjects to interact in 17 daily-life situations, and quantified behavior in a principled manner using CyberGlove body sensor networks that, after accurate calibration, track all major joints of the hand. Our key findings are: (1) We confirmed that hand control in daily-life tasks is very low-dimensional, with four to five dimensions being sufficient to explain 80–90% of the variability in the natural movement data. (2) We established a universally applicable measure of manipulative complexity that allowed us to measure and compare limb movements across tasks. We used Bayesian latent variable models to model the low-dimensional structure of finger joint angles in natural actions. (3) This allowed us to build a naïve classifier that within the first 1000 ms of action initiation (from a flat hand start configuration) predicted which of the 17 actions was going to be executed—enabling us to reliably predict the action intention from very short-time-scale initial data, further revealing the foreseeable nature of hand movements for control of neuroprosthetics and tele operation purposes. (4) Using the Expectation-Maximization algorithm on our latent variable model permitted us to reconstruct with high accuracy (<5–6° MAE) the movement trajectory of missing fingers by simply tracking the remaining fingers. Overall, our results suggest the hypothesis that specific hand actions are orchestrated by the brain in such a way that in the natural tasks of daily-life there is sufficient redundancy and predictability to be directly exploitable for neuroprosthetics.
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影响因子：
5.3
作者：
d'Avella, Andrea;Portone, Alessandro;Lacquaniti, Francesco
通讯作者：
Lacquaniti, Francesco
影响因子：
2
作者：
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影响因子：
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影响因子：
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作者：
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通讯作者：
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影响因子：
120.7
作者：
Kuiken, Todd A.;Li, Guanglin;Lock, Blair A.;Lipschutz, Robert D.;Miller, Laura A.;Stubblefield, Kathy A.;Englehart, Kevin B.
通讯作者：
Englehart, Kevin B.