A significant challenge for a supervised learning approach to inertial human activity recognition is the heterogeneity of data between individual users, resulting in very poor performance of impersonal algorithms for some subjects. We present an approach to personalized activity recognition based on deep embeddings derived from a fully convolutional neural network. We experiment with both categorical cross entropy loss and triplet loss for training the embedding, and describe a novel triplet loss function based on subject triplets. We evaluate these methods on three publicly available inertial human activity recognition data sets (MHEALTH, WISDM, and SPAR) comparing classification accuracy, out-of-distribution activity detection, and embedding generalization to new activities. The novel subject triplet loss provides the best performance overall, and all personalized deep embeddings out-perform our baseline personalized engineered feature embedding and an impersonal fully convolutional neural network classifier.

中文翻译：

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具有深度三元组嵌入的个性化活动识别

用于惯性人类活动识别的监督学习方法的一个重大挑战是个人用户之间数据的异质性，导致某些主题的非个人算法性能非常差。我们提出了一种基于源自完全卷积神经网络的深度嵌入的个性化活动识别方法。我们对分类交叉熵损失和三元组损失进行实验以训练嵌入，并描述了一种基于主题三元组的新型三元组损失函数。我们在三个公开可用的惯性人类活动识别数据集（MHEALTH、WISDM 和 SPAR）上评估这些方法，比较分类准确性、分布外活动检测和对新活动的嵌入泛化。新的主题三元组损失总体上提供了最佳性能，