论文标题
深层嵌入的个性化活动识别
Personalized Activity Recognition with Deep Triplet Embeddings
论文作者
论文摘要
对惯性人类活动识别的监督学习方法的一个重大挑战是单个用户之间数据的异质性，导致某些受试者的非人格算法的性能非常差。我们提出了一种基于完全卷积神经网络的深层嵌入的个性化活动识别方法。我们试验训练嵌入的分类跨熵损失和三重损失，并根据主题三重态描述了新型的三重态损失函数。我们将这些方法评估了三个公开可用的惯性人类活动识别数据集（MHealth，Wisdm和SPAR），以比较分类精度，分布外活动检测以及将概括嵌入到新活动中。新颖的主题三胞胎损失总体上提供了最佳性能，并且所有个性化的深层嵌入方式都超过了我们的基线个性化工程功能嵌入和非人格化的完全卷积神经网络分类器。
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.
