I-Corps: Dexterous Robotic Prosthetic Control Using Deep Learning Pattern Prediction from Ultrasound Signal

I-Corps:利用超声波信号的深度学习模式预测灵巧的机器人假肢控制

基本信息

  • 批准号:
    1744192
  • 负责人:
  • 金额:
    $ 5万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2017
  • 资助国家:
    美国
  • 起止时间:
    2017-07-01 至 2018-07-31
  • 项目状态:
    已结题

项目摘要

The broader impact/commercial potential of this I-Corps project lies in the development a novel system that would allow people with transradial and partial hand amputations to gain unparalleled precise individuation of prosthetic digit motion including continuous and simultaneous movement for individual digits without requiring long and complicated training process. To allow for such functionality a novel set of deep learning algorithms are designed to model muscle movement patterns from ultrasound images. The network is pre-trained with a large amount of data in an effort to minimize later individual training. In addition to power prosthetics, the proposed technology can provide broad impacts in other markets where easy-to-use and accurate gestural control of robotics and/or digital environment are required. These include tele-robotics, exoskeleton operation, virtual reality, gaming, glove boxes as well as work related Personal Protective Equipment, and Performance Augmentation and Amplification Devices.This I-Corps project will develop and utilize a novel ultrasound sensor and novel deep learning algorithms to recognize continuous muscle activity patterns that can predict accurate and dexterous finger motion. Current myoelectric powered prostheses use discrete classifiers that can only predict a limited number of discrete gestures from noisy electromyography (EMG) signal. Feeding deep learning architectures, such as Convolutional Neural Networks, with rich and detailed ultrasound signal promises to allow for the modeling and prediction of detailed continuous and simultaneous muscle movements patterns, which can be mapped to control continuous and simultaneous movements of individual prosthetic fingers. An additional intellectual of this project merit is the pre-training of these deep neural network with a large amount of data, which would allow for short fine tuning training for individual users, allowing for wide and easy adoption of the technology. The proposed project could therefore allow amputees and people with upper body disabilities to perform finger-by-finger movement activities such as fine object manipulation, typing or playing a musical instrument.
该 I-Corps 项目更广泛的影响/商业潜力在于开发一种新颖的系统,该系统将允许经桡动脉和部分手截肢的患者获得无与伦比的精确的假肢手指运动个性化,包括单个手指的连续和同时运动,而无需长时间和长时间的操作。复杂的训练过程。为了实现此类功能,设计了一组新颖的深度学习算法来根据超声图像对肌肉运动模式进行建模。该网络使用大量数据进行了预训练,以尽量减少后期的单独训练。除了动力假肢之外,所提出的技术还可以在需要对机器人和/或数字环境进行易于使用和准确的手势控制的其他市场产生广泛的影响。其中包括远程机器人、外骨骼操作、虚拟现实、游戏、手套箱以及与工作相关的个人防护设备以及性能增强和放大设备。该 I-Corps 项目将开发和利用新型超声波传感器和新型深度学习算法识别连续的肌肉活动模式,可以预测准确而灵巧的手指运动。目前的肌电假肢使用离散分类器,只能从嘈杂的肌电图 (EMG) 信号中预测有限数量的离散手势。为深度学习架构(例如卷积神经网络)提供丰富而详细的超声信号,有望对详细的连续和同时的肌肉运动模式进行建模和预测,这些模式可以映射到控制单个假肢手指的连续和同时的运动。该项目的另一个优点是使用大量数据对这些深度神经网络进行预训练,这将允许对个人用户进行短期微调训练,从而允许广泛而轻松地采用该技术。因此,拟议的项目可以允许截肢者和上半身残疾的人进行手指对手指的运动活动,例如精细的物体操作、打字或演奏乐器。

项目成果

期刊论文数量(0)
专著数量(0)
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会议论文数量(0)
专利数量(0)

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Gil Weinberg其他文献

Synchronization in human-robot Musicianship
人机音乐同步
Robotic Musicianship - Musical Interactions Between Humans and Machines
机器人音乐——人与机器之间的音乐互动
  • DOI:
    10.5772/5206
  • 发表时间:
    2007
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Gil Weinberg
  • 通讯作者:
    Gil Weinberg
Visual cues-based anticipation for percussionist-robot interaction
基于视觉线索的打击乐手与机器人交互的预期
The embroidered musical ball: a squeezable instrument for expressive performance
刺绣音乐球:一种可挤压的表现力乐器
  • DOI:
  • 发表时间:
    2000
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Gil Weinberg;Maggie Orth;Peter Russo
  • 通讯作者:
    Peter Russo
Emotional musical prosody for the enhancement of trust: Audio design for robotic arm communication
增强信任的情感音乐韵律:机械臂通信的音频设计

Gil Weinberg的其他文献

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{{ truncateString('Gil Weinberg', 18)}}的其他基金

Data Driven Predictive Auditory Cues for Safety and Fluency in Human-Robot Interaction
数据驱动的预测听觉线索可确保人机交互的安全性和流畅性
  • 批准号:
    2240525
  • 财政年份:
    2023
  • 资助金额:
    $ 5万
  • 项目类别:
    Standard Grant
NRI: FND: Creating Trust Between Groups of Humans and Robots Using a Novel Music Driven Robotic Emotion Generator
NRI:FND:使用新颖的音乐驱动机器人情感发生器在人类和机器人群体之间建立信任
  • 批准号:
    1925178
  • 财政年份:
    2019
  • 资助金额:
    $ 5万
  • 项目类别:
    Standard Grant
EAGER: Volition Based Anticipatory Control for Time-Critical Brain-Prosthetic Interaction
EAGER:基于意志的预期控制,用于时间关键的大脑-假体交互
  • 批准号:
    1550397
  • 财政年份:
    2015
  • 资助金额:
    $ 5万
  • 项目类别:
    Standard Grant
EAGER: Sub-second human-robot synchronization
EAGER:亚秒级人机同步
  • 批准号:
    1345006
  • 财政年份:
    2013
  • 资助金额:
    $ 5万
  • 项目类别:
    Standard Grant
HCC: Small: Multi Modal Music Intelligence for Robotic Musicianship
HCC:小型:机器人音乐的多模式音乐智能
  • 批准号:
    1017169
  • 财政年份:
    2010
  • 资助金额:
    $ 5万
  • 项目类别:
    Standard Grant
HRI: The Robotic Musician - Facilitating Novel Musical Experiences and Outcomes through Human Robot Interaction
HRI:机器人音乐家 - 通过人机交互促进新颖的音乐体验和成果
  • 批准号:
    0713269
  • 财政年份:
    2007
  • 资助金额:
    $ 5万
  • 项目类别:
    Standard Grant

相似海外基金

CAREER: Context-Aware Task-Oriented Dexterous Robotic Manipulation
职业:上下文感知、任务导向的灵巧机器人操作
  • 批准号:
    2239540
  • 财政年份:
    2023
  • 资助金额:
    $ 5万
  • 项目类别:
    Continuing Grant
CAREER: Context-Aware Task-Oriented Dexterous Robotic Manipulation
职业:上下文感知、任务导向的灵巧机器人操作
  • 批准号:
    2420355
  • 财政年份:
    2023
  • 资助金额:
    $ 5万
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CAREER: Soft Robotic Fingertips with High-Resolution, Calibrated Shape and Force Sensing for Dexterous Manipulation
职业:具有高分辨率、经过校准的形状和力感应的软机器人指尖,可实现灵巧的操作
  • 批准号:
    2142773
  • 财政年份:
    2022
  • 资助金额:
    $ 5万
  • 项目类别:
    Standard Grant
Robot In-hand Dexterous manipulation by extracting data from human manipulation of objects to improve robotic autonomy and dexterity - InDex
机器人手动灵巧操纵,通过从人类操纵物体中提取数据来提高机器人的自主性和灵活性 - InDex
  • 批准号:
    EP/S032355/1
  • 财政年份:
    2019
  • 资助金额:
    $ 5万
  • 项目类别:
    Research Grant
NRI: FND: Scalable, Customizable Sensory Solutions for Dexterous Robotic Hands
NRI:FND:适用于灵巧机械手的可扩展、可定制的感官解决方案
  • 批准号:
    1849417
  • 财政年份:
    2018
  • 资助金额:
    $ 5万
  • 项目类别:
    Standard Grant
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