  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.
