The compliant nature of soft fingers allows for safe and dexterous manipulation of objects by humans in an unstructured environment. A soft prosthetic finger design with tactile sensing capabilities for texture discrimination and subsequent sensory stimulation has the potential to create a more natural experience for an amputee. In this work, a pneumatically actuated soft biomimetic finger is integrated with a textile neuromorphic tactile sensor array for a texture discrimination task. The tactile sensor outputs were converted into neuromorphic spike trains, which emulate the firing pattern of biological mechanoreceptors. Spike-based features from each taxel compressed the information and were then used as inputs for the support vector machine classifier to differentiate the textures. Our soft biomimetic finger with neuromorphic encoding was able to achieve an average overall classification accuracy of 99.57% over 16 independent parameters when tested on 13 standardized textured surfaces. The 16 parameters were the combination of 4 angles of flexion of the soft finger and 4 speeds of palpation. To aid in the perception of more natural objects and their manipulation, subjects were provided with transcutaneous electrical nerve stimulation to convey a subset of four textures with varied textural information. Three able-bodied subjects successfully distinguished two or three textures with the applied stimuli. This work paves the way for a more human-like prosthesis through a soft biomimetic finger with texture discrimination capabilities using neuromorphic techniques that provide sensory feedback; furthermore, texture feedback has the potential to enhance user experience when interacting with their surroundings.

中文翻译：

---

使用可提供感官反馈的灵活神经形态触觉传感器阵列通过软仿生手指进行纹理识别

柔软手指的顺从特性允许人类在非结构化环境中安全和灵巧地操纵物体。具有触觉感知能力的柔软假肢手指设计可用于纹理辨别和随后的感官刺激，有可能为截肢者创造更自然的体验。在这项工作中，气动驱动的软仿生手指与纺织神经形态触觉传感器阵列集成，用于纹理识别任务。触觉传感器的输出被转换成神经形态的脉冲序列，模拟生物机械感受器的发射模式。来自每个紫杉的基于尖峰的特征压缩信息，然后用作支持向量机分类器的输入以区分纹理。当在 13 个标准化纹理表面上进行测试时，我们的具有神经形态编码的软仿生手指能够在 16 个独立参数上实现 99.57% 的平均整体分类准确率。16 个参数是软手指的 4 个屈曲角度和 4 个触诊速度的组合。为了帮助感知更自然的物体及其操作，向受试者提供经皮神经电刺激，以传达具有不同纹理信息的四种纹理的子集。三个身体健全的受试者成功区分了两个或三个纹理与应用的刺激。这项工作通过具有纹理辨别能力的柔软仿生手指，使用提供感觉反馈的神经形态技术，为更类似于人类的假肢铺平了道路；此外，