Sensor-based human activity recognition (HAR) is a common application in the fields of mobile computing and pattern recognition. Existing approaches and models of HAR can present ideal recognition performance only in well-designed, specific, and deterministic scenarios. However, in real scenes, new types of activities, new human bodies that performs activities, and other new situations are encountered. When new situations arise, it is difficult to collect sufficient and high-quality data in time. Thus, the existing approaches and models suffer from a lack of interoperability and scalability. To address these challenges, this study proposes a Model-agnostic Meta Learnings(MAML)-Coarse-Fine Convolutional Neural Networks(CFCNN) mixed task strategy to achieve fast adaptation to the human activity recognition under new situations. This is a novel method that incorporates shot learning to recognize tasks in situations where several kinds of new scenarios exist. First, some modifications were carried out on traditional MAML for multi-scale feature extraction, and a mixed task strategy was adopted during training. The proposed method improves the generalization ability of the model, and is capable of learning quickly when dealing with diffirent new tasks. Finally, the model was compared with other models when facing two new scenes of a new activity category and a new human body. The results showed that the accuracy of the proposed MAML-CFCNN based on the mixed task strategy is higher than that of the state-of-the-art methods, including MAML, First-Order MAML (FOMAML), REPTILE, and so on.

基于传感器的人类活动识别（HAR）是移动计算和模式识别领域的常见应用。现有的 HAR 方法和模型只能在设计良好、特定且确定性的场景中呈现理想的识别性能。然而，在现实场景中，会遇到新的活动类型、进行活动的新人体等新情况。当出现新情况时，很难及时收集到足够的高质量数据。因此，现有的方法和模型缺乏互操作性和可扩展性。为了应对这些挑战，本研究提出了一种与模型无关的元学习（MAML）-粗细卷积神经网络（CFCNN）混合任务策略，以实现在新情况下快速适应人类活动识别。这是一种新颖的方法，它结合了镜头学习来识别存在多种新场景的情况下的任务。首先，对传统的 MAML 进行了一些修改以进行多尺度特征提取，并在训练时采用混合任务策略。该方法提高了模型的泛化能力，在处理不同的新任务时能够快速学习。最后，该模型在面对新的活动类别和新的人体两个新场景时与其他模型进行了比较。结果表明，所提出的基于混合任务策略的 MAML-CFCNN 的准确性高于最先进的方法，包括 MAML、First-Order MAML (FOMAML)、REPTILE 等。