  Human activity recognition aims to recognize the activities of daily living
by utilizing the sensors on different body parts. However, when the labeled
data from a certain body position (i.e. target domain) is missing, how to
leverage the data from other positions (i.e. source domain) to help learn the
activity labels of this position? When there are several source domains
available, it is often difficult to select the most similar source domain to
the target domain. With the selected source domain, we need to perform accurate
knowledge transfer between domains. Existing methods only learn the global
distance between domains while ignoring the local property. In this paper, we
propose a \textit{Stratified Transfer Learning} (STL) framework to perform both
source domain selection and knowledge transfer. STL is based on our proposed
\textit{Stratified} distance to capture the local property of domains. STL
consists of two components: Stratified Domain Selection (STL-SDS) can select
the most similar source domain to the target domain; Stratified Activity
Transfer (STL-SAT) is able to perform accurate knowledge transfer. Extensive
experiments on three public activity recognition datasets demonstrate the
superiority of STL. Furthermore, we extensively investigate the performance of
transfer learning across different degrees of similarities and activity levels
between domains. We also discuss the potential applications of STL in other
fields of pervasive computing for future research.
