Improving the Performance, Robustness and Reliability of Myoelectric Control

提高肌电控制的性能、鲁棒性和可靠性

基本信息

  • 批准号:
    RGPIN-2014-04920
  • 负责人:
  • 金额:
    $ 1.82万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Discovery Grants Program - Individual
  • 财政年份:
    2018
  • 资助国家:
    加拿大
  • 起止时间:
    2018-01-01 至 2019-12-31
  • 项目状态:
    已结题

项目摘要

The loss or congenital absence of a limb is a major disability that can have considerable physical and psychological impact on the life of an amputee. It is estimated that there are more than 3 million upper limb amputees globally, with the rate of incidence growing steadily. The loss of an upper limb, in particular, can greatly affect an individual's level of independence and self-esteem, as well as lead to overuse injuries of the neck, shoulders and back. Although upper limb prostheses have evolved considerably, the method of controlling them has changed little since the 1960s. These devices use the electromyogram (electrical signals generated while contracting muscles of the residual limb) to determine the intent of the user. Termed myoelectric control, this approach is functional and non-invasive, but commercially available systems provide limited control and can be unintuitive. **Recently, significant advances in myoelectric control have been shown in the literature. Pattern-based approaches have shown the ability to control more motions, more intuitively, but robustness remains a concern. Our recent work has highlighted possibilities for greatly improving the reliability of these systems through algorithmic improvements and better understanding of confounding factors. Furthermore, we have identified flaws in the assumptions that form the basis of the standard design approach. The objective of this research is to apply these findings, using novel approaches in signal processing and system design, and novel pressure sensing technology to improve the control, performance and reliability of upper limb prosthetics, and human-machine interfaces in general.
肢体丧失或先天性缺失是一种重大残疾,可能对截肢者的生活产生相当大的身体和心理影响。 据估计,全球有超过300万上肢截肢者,且发病率稳步增长。尤其是失去上肢,会极大地影响个人的独立和自尊水平,并导致颈部、肩部和背部的过度使用损伤。尽管上肢假肢已经有了很大的发展,但自 20 世纪 60 年代以来,控制它们的方法几乎没有变化。这些设备使用肌电图(收缩残肢肌肉时产生的电信号)来确定用户的意图。这种方法被称为肌电控制,是功能性且非侵入性的,但商用系统提供的控制有限并且可能不直观。 **最近,文献显示了肌电控制方面的重大进展。基于模式的方法已经显示出能够更直观地控制更多运动,但鲁棒性仍然是一个问题。我们最近的工作强调了通过算法改进和更好地理解混杂因素来大大提高这些系统可靠性的可能性。此外,我们还发现了构成标准设计方法基础的假设中的缺陷。本研究的目的是应用这些发现,使用新的信号处理和系统设计方法以及新颖的压力传感技术来改善上肢假肢和人机界面的控制、性能和可靠性。

项目成果

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Scheme, Erik其他文献

Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use
Fractal Analysis of Human Gait Variability via Stride Interval Time Series
  • DOI:
    10.3389/fphys.2020.00333
  • 发表时间:
    2020-04-15
  • 期刊:
  • 影响因子:
    4
  • 作者:
    Phinyomark, Angkoon;Larracy, Robyn;Scheme, Erik
  • 通讯作者:
    Scheme, Erik
A long short-term recurrent spatial-temporal fusion for myoelectric pattern recognition
  • DOI:
    10.1016/j.eswa.2021.114977
  • 发表时间:
    2021-04-21
  • 期刊:
  • 影响因子:
    8.5
  • 作者:
    Khushaba, Rami N.;Scheme, Erik;Al-Jumaily, Adel
  • 通讯作者:
    Al-Jumaily, Adel
Motion Normalized Proportional Control for Improved Pattern Recognition-Based Myoelectric Control
Resolving the Limb Position Effect in Myoelectric Pattern Recognition

Scheme, Erik的其他文献

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{{ truncateString('Scheme, Erik', 18)}}的其他基金

Pattern Recognition Based Myoelectric Control for Emerging Human-Machine Interfaces
基于模式识别的新兴人机界面肌电控制
  • 批准号:
    RGPIN-2020-04776
  • 财政年份:
    2022
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Pattern Recognition Based Myoelectric Control for Emerging Human-Machine Interfaces
基于模式识别的新兴人机界面肌电控制
  • 批准号:
    RGPAS-2020-00109
  • 财政年份:
    2022
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Accelerator Supplements
Development of a Pressure-Based Gait Biometric for User Access Control
开发用于用户访问控制的基于压力的步态生物识别
  • 批准号:
    558340-2020
  • 财政年份:
    2021
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Alliance Grants
Pattern Recognition Based Myoelectric Control for Emerging Human-Machine Interfaces
基于模式识别的新兴人机界面肌电控制
  • 批准号:
    RGPAS-2020-00109
  • 财政年份:
    2021
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Accelerator Supplements
Pattern Recognition Based Myoelectric Control for Emerging Human-Machine Interfaces
基于模式识别的新兴人机界面肌电控制
  • 批准号:
    RGPIN-2020-04776
  • 财政年份:
    2021
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Development of a Pressure-Based Gait Biometric for User Access Control
开发用于用户访问控制的基于压力的步态生物识别
  • 批准号:
    558340-2020
  • 财政年份:
    2020
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Alliance Grants
Pattern Recognition Based Myoelectric Control for Emerging Human-Machine Interfaces
基于模式识别的新兴人机界面肌电控制
  • 批准号:
    RGPAS-2020-00109
  • 财政年份:
    2020
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Accelerator Supplements
Pattern Recognition Based Myoelectric Control for Emerging Human-Machine Interfaces
基于模式识别的新兴人机界面肌电控制
  • 批准号:
    RGPIN-2020-04776
  • 财政年份:
    2020
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Improving the Performance, Robustness and Reliability of Myoelectric Control
提高肌电控制的性能、鲁棒性和可靠性
  • 批准号:
    RGPIN-2014-04920
  • 财政年份:
    2019
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Concurrent EMG and EEG Analysis for Quantitative Motor Assessment
用于定量运动评估的同步肌电图和脑电图分析
  • 批准号:
    538323-2019
  • 财政年份:
    2019
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Engage Grants Program

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