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相关概念视频

Design Example: Resistive Touchscreen01:14

Design Example: Resistive Touchscreen

353
A device engineer plays a crucial role in designing user interfaces for mobile devices. One such interface is the resistive touchscreen, which fundamentally consists of two metallic layers: a flexible upper layer and a rigid lower layer, separated by a narrow gap. The high resistance between these two layers is a key characteristic of this design.
When a user touches the screen, the two layers make contact at a specific point known as the touchpoint. This contact reduces the resistance between...
353
Design Example01:23

Design Example

348
The innovation of touch-tone telephony revolutionized the telecommunications industry by replacing the traditional rotary dial with a dual-tone multi-frequency (DTMF) signaling system. This system uses a matrix-style keypad with buttons arranged in four rows and three columns, creating 12 distinct signals each assigned to a pair of frequencies. Each button press results in a simultaneous generation of two sinusoidal tones – one from a low-frequency group (697 to 941 Hz) and one from a...
348

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相关实验视频

Updated: Jul 23, 2025

An Assessment Method and Toolkit to Evaluate Keyboard Design on Smartphones
05:42

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Published on: October 5, 2020

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一个大脑-计算机打字界面,使用手指运动来打字.

Nishal P Shah1, Matthew S Willsey1, Nick Hahn1

  • 1Stanford University.

International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering
|July 19, 2023
PubMed
概括
此摘要是机器生成的。

这项研究为患者引入了一种新的皮质内脑计算机接口 (iBCI) 键盘. 3D iBCI 键盘实现了 90% 的打字精度,大大提高了通信假肢的功能.

关键词:
信息传播 信息传播 信息传播在手指上,手指是指的.皮层内大脑计算机接口运动皮层 运动皮层发动机解码的解码方法打字方式 打字方式

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科学领域:

  • 神经科学是一个神经科学.
  • 生物医学工程 生物医学工程
  • 康复技术 康复技术 康复技术

背景情况:

  • 皮层内脑-计算机接口 (iBCI) 对于为患者开发辅助技术至关重要.
  • 现有的iBCI能够实现像光标控制,手写和语音这样的功能,但需要先进的通信方法.
  • 从运动皮层解码神经活动,允许控制外部设备.

研究的目的:

  • 引入和评估一种新的3D皮质内脑计算机接口 (iBCI) 键盘,以提高打字能力.
  • 为了比较个人手指运动与集体手指运动的解码精度,用于iBCI控制.
  • 使用自然语言统计数据优化虚拟键盘布局,以实现高效的符号获取.

主要方法:

  • 为BrainGate2临床试验参与者开发了一个参与者特定的iBCI键盘原型 (T5).
  • 从左前运动皮层记录神经活动来解码手指运动 (5个单独的DOF与3个分组的DOF).
  • 设计了一个基于自然语言统计数据优化符号配置的虚拟键盘,并使用实时闭环解码器进行测试.

主要成果:

  • 使用3个独立的自由度 (DOF) 进行手指组的神经解码,与5个单独的DOF (76%) 相比,获得了更高的准确性 (95%).
  • 优化的虚拟键盘布局与随机布局相比,平均指的距离减少了大约2倍.
  • 参与者成功地输入了31个符号,准确率为90%,平均速度为每个符号2.3秒.

结论:

  • 使用分组的手指运动的3D iBCI键盘为患者提供了更准确,更有效的通信假肢.
  • 使用自然语言统计数据优化键盘布局可显著提高iBCI系统的打字性能.
  • 这种新的iBCI方法在恢复严重残疾人的沟通方面取得了有希望的进展.