在线无监督适应潜伏表示用于肌电控制在用户解码器共适应期间
概括
本研究引入了一种无监督的方法来调整肌电控制接口,显著减少调整时间并提高假肢手控制的可靠性. 这种方法增强了用户-解码器的共同适应,使假肢操作更加稳定和可访问.
科学领域:
- 生物医学工程 生物医学工程
- 神经科学是一个神经科学.
- 机器人技术 机器人技术 机器人技术
背景情况:
- 肌电控制接口将电肌图 (EMG) 信号映射到控制外部设备的位置,这对于积极的假肢控制至关重要.
- 信号特征随着时间的推移而漂移,破坏静态接口的稳定性,并需要在线用户解码器协同适应.
- 现有的适应方法面临着诸如昂贵的数据标签和缓慢的融合等挑战.
研究的目的:
- 为肌电控制接口开发一种快速,无监督的解码器适应方法.
- 解决当前在线适应技术的局限性,专注于效率和减少数据要求.
主要方法:
- 使用自动编码器提取潜伏多重空间中的运动意图表示.
- 实施了在线无监督的适应方案,使用摩尔-罗斯逆向进行快速的网络再培训.
- 追踪不断演变的信号分组,以保持稳定的控制,尽管信号变化.
主要成果:
- 与最先进的方法相比,拟议的适应方案显示了约50%的更快趋同.
- 在线实验显示,在光标和假肢手控制任务中,监督方法的稳定性与监督方法相美.
- 假肢手杯搬迁任务完成时间 后适应与电极转移匹配基线性能.
结论:
- 无监督的适应方法显著提高了肌电控制接口的可访问性和可靠性.
- 这种方法有效地弥合了翻译差距,通过在实时操作中实现了强大的协同适应.
- 该方法为先进的假肢设备的稳定和直观控制提供了一个有前途的解决方案.
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