基于回归的肌电控制的高效多位置训练:通过转移学习探索变压器模型
概括
这项研究探讨了减少基于回归的肌电控制训练时间的方法,使得手腕和手的同时运动更自然. 通过微调几次射击进行转移学习,显示了改善假肢控制的有希望的结果.
科学领域:
- 生物医学工程 生物医学工程
- 康复机器人 康复机器人
- 神经修复品是一种神经修复品.
背景情况:
- 目前的上肢肌电假肢经常使用分类模型,限制了对多个自由度 (DOF) 的同时控制.
- 基于回归的模型为更自然的运动提供了同时的DOF控制,但通常需要广泛的训练.
- 减少基于回归的肌电控制的培训负担对于临床采用至关重要.
研究的目的:
- 调查和比较不同的方法来减少基于回归的肌电控制的培训负担.
- 在此背景下,评估转移学习和少量学习技术的有效性.
- 为了能够更直观和自然地控制上肢假肢.
主要方法:
- 从10名身体健康的参与者收集了电肌图 (EMG) 数据.
- 测试了五种训练策略:传统训练 (单肢/多肢姿势) 和少数射击学习.
- 应用了转移学习,对其他用户的数据进行预训练模型,并与最终用户数据进行微调.
- 模型使用线性回归器,卷积神经网络和变压器架构进行了评估.
主要成果:
- 转移学习与几次精确调整相结合,在所有参与者中实现了第二高的R2中位数0.76.
- 这种方法表明了改善基于回归的肌电控制的巨大潜力.
- 该研究提供了多DOF假肢控制有效培训的概念证明.
结论:
- 对于基于回归的肌电控制来说,可以减少训练例程.
- 转移学习和少量学习是最小化培训数据要求的有效策略.
- 开发的方法为更加自然和直观的假肢功能铺平了道路.
相关概念视频
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