Visual perception of joint stiffness from multijoint motion
Visual perception of joint stiffness from multijoint motion
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DOI：
10.1152/jn.00514.2018
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发表时间：
2019-07-01
影响因子：
2.5
通讯作者：
Hogan, Neville
中科院分区：
文献类型：
--
作者：
Huber, Meghan E.;Folinus, Charlotte;Hogan, Neville
人类有一种惊人的能力，可以从他人的动作中提取隐藏的信息。例如，即使只有有限的运动学信息，人类也可以区分生物和非生物运动，识别人类演示者的年龄和性别。并识别人类演示者正在执行的动作。然而，目前还不清楚他们是否也可以通过观察他们的运动来估计另一个肢体的隐藏机械特性。严格来说。识别物体的机械特性。例如刚度，需要接触。只有运动信息，明确的刚度测量是根本不可能的。因为可以用无限数量的刚度值来产生相同的肢体运动。然而，我们表明，人类可以很容易地估计从其运动的模拟肢体的刚度。在三个实验中，我们发现，参与者线性增加他们的评级手臂刚度关节刚度参数的手臂控制器增加。这是值得注意的，因为没有与模拟肢体的物理接触。此外，参与者对如何控制模拟手臂没有明确的知识。为了成功地将多关节运动的重要变化映射到手臂刚度的变化，参与者可能利用了人类神经运动控制的先验知识。具有与用于驱动模拟臂的控制器的行为一致的内部表示意味着这种控制策略能够胜任地捕获真实生物控制的关键特征。人类可以从运动学中提取神经运动控制的潜在特征，这一发现也为人类如何解释他人的运动行为提供了新的见解。新&值得注意的是，人类可以通过视觉感知他人的明显运动。但他们是否也能从肢体的运动中感知到另一个肢体隐藏的动态特性，这是未知的。在这里，我们表明，人类可以正确地推断肢体刚度的变化，从非平凡的变化，在多关节肢体运动没有力的信息或明确的知识的基础肢体控制器。我们的研究结果表明，人类认为其他人控制运动行为的方式，肢体僵硬影响运动。
Humans have an astonishing ability to extract hidden information from the movements of others. For example, even with limited kinematic information, humans can distinguish between biological and nonbiological motion, identify the age and gender of a human demonstrator. and recognize what action a human demonstrator is performing. It is unknown, however, whether they can also estimate hidden mechanical properties of another's limbs simply by observing their motions. Strictly speaking. identifying an object's mechanical properties. such as stiffness, requires contact. With only motion information, unambiguous measurements of stiffness are fundamentally impossible. since the same limb motion can be generated with an infinite number of stiffness values. However, we show that humans can readily estimate the stiffness of a simulated limb from its motion. In three experiments, we found that participants linearly increased their rating of arm stiffness as joint stiffness parameters in the arm controller increased. This was remarkable since there was no physical contact with the simulated limb. Moreover, participants had no explicit knowledge of how the simulated arm was controlled. To successfully map nontrivial changes in multijoint motion to changes in arm stiffness, participants likely drew on prior knowledge of human neuromotor control. Having an internal representation consistent with the behavior of the controller used to drive the simulated arm implies that this control policy competently captures key features of veridical biological control. Finding that humans can extract latent features of neuromotor control from kinematics also provides new insight into how humans interpret the motor actions of others.NEW & NOTEWORTHY Humans can visually perceive another's overt motion. but it is unknown whether they can also perceive the hidden dynamic properties of another's limbs from their motions. Here, we show that humans can correctly infer changes in limb stiffness from nontrivial changes in multijoint limb motion without force information or explicit knowledge of the underlying limb controller. Our findings suggest that humans presume others control motor behavior in such a way that limb stiffness influences motion.