Neuromorphic encoding of tactile stimuli to provide naturalistic sensory feedback in upper limb prostheses

触觉刺激的神经形态编码为上肢假肢提供自然的感觉反馈

• 批准号: 10537606
• 负责人: Mark Iskarous
• 金额: $ 4.68万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10537606
• 关键词: Affect; Algorithms; Amputees; Biological; Biology; Breathing; Classification; Communication; Computer Models; Data Set; Environment; Esthesia; Feedback; Fingers; Goals; Intention; Language; Limb Prosthesis; Literature; Measurement; Mechanoreceptors; Methods; Modality; Modeling; Nerve; Nervous system structure; Neurons; Output; Palpation; Pattern; Peripheral Nerve Stimulation; Population; Prosthesis; Reading; Research; Research Project Grants; Research Project Summaries; Scanning; Scheme; Sensory; Signal Transduction; Skin; Speed; Stimulus; Structure; System; Tactile; Techniques; Testing; Texture; Training; Upper Extremity; Work; analog; functional outcomes; improved; neural prosthesis; neurodevelopment; relating to nervous system; response; robotic device; sensor; sensory feedback; sensory system; signal processing; spatiotemporal

PROJECT SUMMARY This research project is motivated by the goal of improving the lives of amputees through naturalistic sensory feedback of tactile stimuli. Today, prostheses rely on decoding user intention through measurement of neural or electromyographic (EMG) signals. The full potential of these sophisticated robotic devices cannot be realized without the incorporation of sensors that evaluate the environment and a way to seamlessly communicate with the user. Neural prostheses can enable this seamless communication by interfacing directly with the nervous system of amputees and stimulating the nerves in order to elicit sensations corresponding to the interaction between the prosthesis and the environment. To do this naturalistically, the analog readings from sensors incorporated into the prosthesis must be encoded into the language of the nervous system: patterns of spiking activity. My goal is to improve sensory feedback for amputees by exploring how information from tactile sensors can be transformed into neuron-like (neuromorphic) spikes to be used for stimulation feedback. I will examine how tactile sensing is encoded in biology and then phenomenologically recreate the signal processing chain using a computational model that will be tested with a real-world texture dataset. The output of these models will be classified to verify and quantify the successful encoding of texture information as neuromorphic spiking activity. Texture serves as a good test case to develop these models because of its rich spatiotemporal structure. Specific Aim 1 – Neuromorphic Encoding and Processing of Tactile Stimuli – I will use the Izhikevich neuron model to recreate the spiking activity of SA and RA mechanoreceptors in response to texture stimuli applied to a tactile sensing array. I will develop new algorithms to transform the spiking patterns to account for scanning speed and applied force. This will result in a speed- and force-invariant representation of texture. Specific Aim 2 – Neuromorphic Compression of Tactile Information – Initially, a naïve channel selection algorithm that uses spike train distance to evaluate mutual information between different input channels will compress tactile information to select an optimal set of sensing channels to pass through to stimulation. A more advanced scheme will combine inputs together for more efficient information encoding and to enrich the information content of the final output spiking patterns. Artificial texture classification will be used to evaluate the capability of these methods to efficiently retain relevant texture information. Fundamentally, Aim 1 focuses on robust representations of tactile stimuli independent of exploratory conditions, while Aim 2 focuses on efficient representations of those stimuli. When completed, this work will provide the basis for more naturalistic sensory feedback to amputees through peripheral nerve stimulation which will result in better functional outcomes when using prostheses in their daily lives.

项目摘要 该研究项目的激励是通过通过改善截肢者生活的目标 触觉刺激的自然主义感觉反馈。今天，假肢依靠解码用户意图 神经或肌电图（EMG）信号的测量。这些复杂的全部潜力 没有评估环境的传感器保险，机器人设备将无法实现 一种与用户无缝通信的方法。神经假体可以实现这种无缝的沟通 通过直接与截肢者的神经系统接口并刺激神经以引起神经 对应于假体与环境之间相互作用的感觉。为此 自然地，必须将包含在假体中的传感器的模拟读数编码为 神经系统的语言：峰值活动的模式。我的目标是提高感官反馈 通过探索如何将来自触觉传感器的信息转换为神经元状的信息，截肢者 （神经形态）用于刺激反馈的尖峰。我将检查触觉灵敏度如何编码 在生物学上，然后在现象学上使用计算模型重新创建信号处理链 这将使用现实世界的纹理数据集进行测试。这些模型的输出将分类以验证 并将成功编码纹理信息量化为神经形态尖峰活动。质地 由于其具有丰富的时空结构，因此是开发这些模型的良好测试用例。 特定目标1 - 触觉刺激的神经形态编码和处理 - 我将使用Izhikevich 神经元模型以响应质地刺激来重现SA和RA机制受体的尖峰活动 应用于触觉灵敏度阵列。我将开发新算法来转换尖峰模式以说明 用于扫描速度和施加力。这将导致速度和力不变的表示 质地。 特定目标2 - 触觉信息的神经形态压缩 - 最初是幼稚的渠道 选择使用尖峰火车距离来评估不同输入之间的相互信息的选择算法 频道将压缩触觉信息，以选择一组最佳的感应频道，以通过 刺激。一个更高级的方案将将输入结合在一起，以进行更有效的信息编码 并丰富最终输出峰值模式的信息内容。人造纹理分类 将用于评估这些方法有效保留相关纹理信息的能力。 从根本上讲，目标1专注于独立于触觉刺激的强大表示 探索性条件，而目标2则集中于这些刺激的有效表示。完成后， 这项工作将为通过周围神经向截肢者提供更自然的感觉反馈提供基础 刺激在日常生活中使用假体时会产生更好的功能结果。