Recognition of activities of daily living (ADL) is an essential component of assisted living systems based on actigraphy. This task can nowadays be performed by machine learning models which are able to automatically extract and learn relevant features but, most of time, need to be trained with large amounts of data collected on several users. In this paper, we propose an approach to learn personalized ADL recognition models from few raw data based on a specific type of neural network called matching network. The interest of this few-shot learning approach is three-fold. Firstly, people perform activities their own way and general models may average out important individual characteristics unlike personalized models that could thus achieve better performance. Secondly, gathering large quantities of annotated data from one user is time-consuming and threatens privacy in a medical context. Thirdly, matching networks are by nature weakly dependent on the classes they are trained on and can generalize easily to new activities without needing extra training, thus making them very versatile for real applications. Our results show the effectiveness of the proposed approach compared to general neural network models, even in situations with few training data.

日常生活活动的识别 (ADL) 是基于活动记录的辅助生活系统的重要组成部分。如今，这项任务可以通过机器学习模型来执行，这些模型能够自动提取和学习相关特征，但在大多数情况下，需要使用在多个用户上收集的大量数据进行训练。在本文中，我们提出了一种基于特定类型的神经网络（称为匹配网络）从少量原始数据中学习个性化 ADL 识别模型的方法. 这种少量学习方法的兴趣是三倍的。首先，人们以自己的方式执行活动，而通用模型可能会平均化重要的个人特征，而个性化模型可以因此获得更好的性能。其次，从一个用户那里收集大量带注释的数据非常耗时，并且会威胁到医疗环境中的隐私。第三，匹配网络本质上弱依赖于它们所训练的类，并且可以轻松地推广到新的活动而无需额外的训练，从而使它们在实际应用中非常通用。我们的结果表明，即使在训练数据很少的情况下，与一般神经网络模型相比，所提出的方法也是有效的。