Modality translation grants diagnostic value to wearable devices by translating signals collected from low-power sensors to their highly-interpretable counterparts that are more familiar to healthcare providers. For instance, bio-impedance (Bio-Z) is a conveniently collected modality for measuring physiological parameters but is not highly interpretable. Thus, translating it to a well-known modality such as electrocardiogram (ECG) improves the usability of Bio-Z in wearables. Deep learning solutions are well-suited for this task given complex relationships between modalities generated by distinct processes. However, current algorithms usually train a single model for all users that results in ignoring cross-user variations. Retraining for new users usually requires collecting abundant labeled data, which is challenging in healthcare applications. In this paper, we build a modality translation framework to translate Bio-Z to ECG by learning personalized user information without training several independent architectures. Furthermore, our framework is able to adapt to new users in testing using very few samples. We design a meta-learning framework that contains shared and user-specific parameters to account for user differences while learning from the similarity amongst user signals. In this model, a meta-learner approximated by a neural network learns how to learn user-specific parameters and can efficiently update them in testing. Our experiments show that the proposed model reduces the percentage root mean square difference (PRD) by 41% compared to training a single model for all users and by 36% compared to training independent models for each user. When adapting the model to new users, our model outperforms fine-tuning a pre-trained model through back-propagation by 40% using as few as two new samples in testing.

中文翻译：

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可穿戴生理传感中模态转换模型快速个性化的元学习方法

模态转换通过将从低功耗传感器收集的信号转换为医疗保健提供者更熟悉的高度可解释的对应信号，为可穿戴设备赋予诊断价值。例如，生物阻抗 (Bio-Z) 是一种方便收集的测量生理参数的模式，但不是高度可解释的。因此，将其转化为心电图 (ECG) 等众所周知的模式可以提高 Bio-Z 在可穿戴设备中的可用性。鉴于不同过程生成的模式之间的复杂关系，深度学习解决方案非常适合这项任务。然而，当前的算法通常为所有用户训练一个模型，这会导致忽略跨用户的变化。对新用户进行再培训通常需要收集大量标记数据，这在医疗保健应用中具有挑战性。在本文中，我们构建了一个模态转换框架，通过学习个性化用户信息将 Bio-Z 转换为 ECG，而无需训练多个独立的架构。此外，我们的框架能够适应新用户使用很少的样本进行测试。我们设计了一个元学习框架，其中包含共享参数和用户特定参数，以在从用户信号之间的相似性中学习的同时考虑用户差异。在这个模型中，由神经网络近似的元学习器学习如何学习用户特定的参数，并可以在测试中有效地更新它们。我们的实验表明，与为所有用户训练单个模型相比，所提出的模型将均方根差 (PRD) 百分比降低了 41%，与为每个用户训练独立模型相比降低了 36%。在使模型适应新用户时，