Evaluation of Haptic Sensation and Object Recognition using Transcutaneous Nerve Stimulation

摘要 触觉感觉反馈对于精细运动控制和物体识别是至关重要的。躯体感觉的丧失 会严重影响我们灵巧的物体交互和操作能力。大多数假手 用户通常依赖于来自其他来源的替代感觉信息，例如视觉或听觉提示。因此，在本发明中， 假手运动缓慢，并且物体抓取/释放需要持续的认知注意力。 最近，通过神经植入物直接刺激外周神经或躯体感觉皮层已经表明， 在唤起躯体匹配反馈方面有很大的潜力。然而，几个主要障碍限制了广泛的临床应用。 应用，包括植入系统缺乏长期稳定性，手术过程耦合 有广泛的术后护理和专业的系统维护。因此，迫切需要 开发非侵入性的方法，可以引起假肢使用者的躯体位置匹配的感觉。 我所提出的研究的目的是开发非侵入性经皮神经刺激方法 它可以为截肢者提供稳定触觉反馈，并允许他们识别不同的物体属性 (e.g.,硬度、大小、形状和位置）。建议的研究将有助于我 长期目标是开发临床适用的方法，使闭环人机 交互.具体地，经皮神经刺激可以通过以下方式在幻手中引起触觉感觉： 输送精确控制的电流，可以激活正中和尺骨的近端节段， 神经这种方法允许我们激活感觉神经纤维的不同部分， 如我们实验室所示，用分级的感觉强度引出空间定位的触觉。基础上 基于现有的研究结果，我提出以下研究目标：1）描述触觉感觉是如何 从不同位置的并发神经刺激组合;和2）评估识别 基于用假手引起的触觉感觉的对象属性的性能。 为了实现我的研究目标，我计划在上臂内侧附着一个高密度的网格电极 在肱二头肌的肌腹下面具有各种参数的受控电流然后将同时被 输送到不同的电极对。感知的感觉将被量化，包括 感觉的空间位置和感知感觉的稳定性/可重复性。然后我会把电流 当关节式假手交互时， 用不同的物体。将评估对象识别的性能。总的来说，结果可以 有助于为假手使用者提供准确稳定的触觉， 控制和增强假体使用期间的实施例。我的方法的非侵入性性质也有一个 具有很大的潜力，易于临床翻译与潜在的高用户接受率。

ABSTRACT Haptic sensory feedback is critical to fine motor control and object recognitions. A loss of this somatosensation can severely affect our ability of dexterous object interaction and manipulation. A majority of prosthetic hand users often rely on substituted sensory information from other sources, such as visual or audio cues. As a result, the prosthetic hand movement is slow, and object grasp/release requires continuous cognitive attention. Recently, direct stimulations of the peripheral nerve or somatosensory cortex via neural implants have shown great promise in evoking somatotopically-matched feedback. However, several major hurdles limit wide clinical applications, including the lack of long-term stability of the implanted system, the surgery procedures coupled with the extensive post-surgery care, and specialized system maintenance. Therefore, there is an urgent need to develop non-invasive approaches that can elicit somatotopically-matched sensations to prosthetic arm users. The objective of my proposed research is to develop non-invasive transcutaneous nerve stimulation approaches that can provide stable haptic feedback to arm amputees and allow them to recognize different object properties (e.g., stiffness, size, shape, and location) from the evoked sensations. The proposed research will facilitate my long-term goal of developing clinically applicable approaches that can enable closed-loop human-machine interactions. Specifically, transcutaneous nerve stimulation can elicit haptic sensations in the phantom hand, by delivering precisely controlled electrical current that can activate proximal segments of the median and ulnar nerves. This approach allows us to activate different portions of the sensory nerve fibers transcutaneously and elicit spatially localized haptic sensations with graded intensity of sensation as shown in our lab. Building on the existing findings, I propose the following research aims: 1) to characterize how the haptic sensations are combined from concurrent nerve stimulations at different locations; and 2) to evaluate the recognition performance of object properties based on elicited haptic sensations with a prosthetic hand. To execute my research aims, I plan to attach a high-density grid electrodes on the medial side of the upper arm beneath the biceps muscle belly. Controlled electrical current with various parameters will then be simultaneously delivered to different pairs of electrodes. The perceived sensations will be quantified, including the changes in spatial location of the sensation and the stability/repeatability of the perceived sensation. I will then deliver current profiles with encoded object properties to different electrode pairs when an articulated prosthetic hand interacts with different objects. The performance of the object recognition will be evaluated. Overall, the outcomes can help deliver accurate and stable haptic sensations to prosthetic hand users, allowing intuitive and closed-loop control, and enhancing embodiment during prosthetic use. The non-invasive nature of my approach also has a great potential for readily clinical translations with a potentially high user-acceptance rate.