用于中风机器人手部矫形器意图推断中快速适应的元学习
Meta-Learning for Fast Adaptation in Intent Inferral on a Robotic Hand Orthosis for Stroke
AI总结：
提出MetaEMG元学习方法，利用少量数据实现中风机器人手部矫形器意图推断的快速适应，显著提升准确率。
摘要：
我们提出了MetaEMG，一种用于中风机器人手部矫形器意图推断中快速适应的元学习方法。针对残疾人辅助和康复机器人，机器学习面临的一个关键挑战是难以收集带标签的训练数据。中风患者的肌张力和痉挛程度通常差异显著，甚至同一患者在不同使用阶段的手部功能也会发生变化。我们研究了利用元学习来减轻将高容量神经网络适应到新阶段或新患者所需的数据收集负担。我们在从5名中风患者收集的真实临床数据上的实验表明，MetaEMG能够利用少量特定于阶段或患者的数据集以及极少的微调轮次来提高意图推断的准确率。据我们所知，我们是首个将中风患者的意图推断表述为元学习问题的团队，并展示了对新阶段或新患者的快速适应能力，以利用EMG信号控制机器人手部矫形器。
英文摘要：
We propose MetaEMG, a meta-learning approach for fast adaptation in intent inferral on a robotic hand orthosis for stroke. One key challenge in machine learning for assistive and rehabilitative robotics with disabled-bodied subjects is the difficulty of collecting labeled training data. Muscle tone and spasticity often vary significantly among stroke subjects, and hand function can even change across different use sessions of the device for the same subject. We investigate the use of meta-learning to mitigate the burden of data collection needed to adapt high-capacity neural networks to a new session or subject. Our experiments on real clinical data collected from five stroke subjects show that MetaEMG can improve the intent inferral accuracy with a small session- or subject-specific dataset and very few fine-tuning epochs. To the best of our knowledge, we are the first to formulate intent inferral on stroke subjects as a meta-learning problem and demonstrate fast adaptation to a new session or subject for controlling a robotic hand orthosis with EMG signals.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
