Activity classification has become a vital feature of wearable health tracking devices. As innovation in this field grows, wearable devices worn on different parts of the body are emerging. To perform activity classification on a new body location, labeled data corresponding to the new locations are generally required, but this is expensive to acquire. In this work, we present an innovative method to leverage an existing activity classifier, trained on Inertial Measurement Unit (IMU) data from a reference body location (the source domain), in order to perform activity classification on a new body location (the target domain) in an unsupervised way, i.e. without the need for classification labels at the new location. Specifically, given an IMU embedding model trained to perform activity classification at the source domain, we train an embedding model to perform activity classification at the target domain by replicating the embeddings at the source domain. This is achieved using simultaneous IMU measurements at the source and target domains. The replicated embeddings at the target domain are used by a classification model that has previously been trained on the source domain to perform activity classification at the target domain. We have evaluated the proposed methods on three activity classification datasets PAMAP2, MHealth, and Opportunity, yielding high F1 scores of 67.19%, 70.40% and 68.34%, respectively when the source domain is the wrist and the target domain is the torso.

翻译：活动分类已成为可穿戴健康追踪设备的核心功能。随着该领域创新不断发展，佩戴于身体不同部位的可穿戴设备正陆续涌现。要在新的身体部位进行活动分类，通常需要对应新部位的标注数据，但此类数据获取成本高昂。本文提出一种创新方法，利用在参考身体部位（源域）惯性测量单元数据上训练好的现有活动分类器，以无监督方式（即无需新部位的分类标签）实现新身体部位（目标域）的活动分类。具体而言，基于在源域训练完成活动分类的IMU嵌入模型，我们通过复制源域嵌入来训练目标域的嵌入模型，使其具备活动分类能力。该过程借助源域与目标域的同时IMU测量实现。目标域中复制的嵌入随后由先前在源域训练的分类模型使用，从而在目标域执行活动分类。我们在PAMAP2、MHealth和Opportunity三个活动分类数据集上评估了所提方法，当源域为手腕、目标域为躯干时，分别取得了67.19%、70.40%和68.34%的高F1分数。