增强和塑造闭环协同适应的肌电接口与场景导向的自适应增量学习
概括
本研究介绍了使用增强现实 (AR) 和多模进步域对抗神经网络 (MPDANN) 的适应性学习策略,以改善截肢者的肌电假肢培训. MPDANN在新的环境中增强了表面电肌图 (sEMG) 识别,促进了康复参与.
科学领域:
- 生物医学工程 生物医学工程
- 康复技术 康复技术 康复技术
- 医疗保健中的人工智能
背景情况:
- 虚拟环境提高了肌电假肢培训的动力.
- 对于截肢者来说,长期坚持和坚持康复仍然是重大挑战.
- 当前的方法很难适应新的环境,并保持用户参与度.
研究的目的:
- 提出一个以场景为导向的自适应增量学习策略,以提高在未知的环境中伪标签预测的准确性.
- 整合增强现实 (AR) 以实现现实的假肢控制培训和多模进步域对抗神经网络 (MPDANN) 以实现强大的适应.
- 增强截肢者的神经肌肉康复参与和长期训练坚持.
主要方法:
- 开发了一个AR环境,用于虚拟假体控制和全息物体操纵任务.
- 实施MPDANN使用表面电肌图 (sEMG) 和惯性测量单元 (IMU) 数据进行域对抗训练.
- 在5天的时间里,用10个任务和8个肢体位置评估了16名身体健康和2名截肢受试者的策略,将MPDANN与CNN基线进行了比较.
主要成果:
- 在有能力的受试者中,MPDANN取得了超过80%的熟练程度,这与CNN基线相比是显著的改善.
- 虽然截肢患者的完成率较低,但两组都在MPDANN时表现出一致的绩效增长.
- 该战略显示了强大的适应未见的环境,提高了sEMG识别性能.
结论:
- 将实时视觉反与闭环域适应算法的整合有效地改善了未经训练的环境中的sEMG识别.
- 拟议的AR和MPDANN战略显示了增强肌电假肢培训和康复的前景.
- 这种方法可以带来更有效和持续的参与截肢者的神经肌肉康复.
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