Evaluation of Haptic Sensation and Object Recognition using Transcutaneous Nerve Stimulation

摘要 触觉感觉反馈对精细运动控制和物体识别至关重要。这种躯体感觉的丧失 会严重影响我们灵巧的物体交互和操纵能力。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，例如视觉或音频提示。结果, 假手运动缓慢，物体抓取/释放需要持续的认知注意。 最近，通过神经植入对周围神经或躯体感觉皮质的直接刺激显示 在唤起与体感匹配的反馈方面很有希望。然而，几个主要障碍限制了临床的广泛应用。 应用方面，包括缺乏长期稳定性的植入系统，手术程序的配合 具有广泛的术后护理，并有专门的系统维护。因此，迫切需要 开发一种非侵入性的方法，可以为假肢使用者带来体位匹配的感觉。 我所提议的研究的目的是开发非侵入性经皮神经刺激方法。 它可以为手臂截肢者提供稳定的触觉反馈，并允许他们识别不同的对象属性 (例如，僵硬、大小、形状和位置)。拟议的研究将有助于我的 长期目标是开发临床适用的方法，使闭环人-机 互动。具体地说，经皮神经刺激可以通过以下方式在幻手中引起触觉感觉 提供精确控制的电流，可以激活正中和尺骨近端 太紧张了。这种方法允许我们通过皮肤激活感觉神经纤维的不同部分，并 如我们实验室所示，用分级的感觉强度来诱导空间局部化的触觉。建立在 根据已有的研究结果，我提出以下研究目标：1)表征触觉是如何 来自不同位置的并行神经刺激的组合；以及2)评估识别 基于假手的触觉的物体特性的表现。 为了实现我的研究目标，我计划在上臂内侧安装一个高密度栅极电极 在二头肌肌腹下。然后，各种参数的受控电流将同时 输送到不同的电极对上。感知到的感觉将被量化，包括 感觉的空间位置和感知到的感觉的稳定性/重复性。然后我会把电流送到 关节假手相互作用时不同电极对具有编码对象特性的轮廓 带着不同的物体。将对目标识别的性能进行评估。总体而言，结果可以 帮助假手使用者提供准确而稳定的触觉，实现直观和闭环 控制，并加强假体使用期间的体现。我的方法的非侵入性还有一个 易于临床翻译的巨大潜力，具有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.