ActiLabel: A Combinatorial Transfer Learning Framework for Activity Recognition
This addresses the challenge of limited adoption of activity recognition models across different sensor domains, which is incremental as it builds on transfer learning approaches.
The paper tackles the problem of sensor-based human activity recognition across diverse devices by proposing ActiLabel, a combinatorial transfer learning framework that learns structural similarities between domains using dependency graphs and finds optimal mappings, achieving superior performance over state-of-the-art methods in experiments on three public datasets.
Sensor-based human activity recognition has become a critical component of many emerging applications ranging from behavioral medicine to gaming. However, an unprecedented increase in the diversity of sensor devices in the Internet-of-Things era has limited the adoption of activity recognition models for use across different domains. We propose ActiLabel a combinatorial framework that learns structural similarities among the events in an arbitrary domain and those of a different domain. The structural similarities are captured through a graph model, referred to as the it dependency graph, which abstracts details of activity patterns in low-level signal and feature space. The activity labels are then autonomously learned by finding an optimal tiered mapping between the dependency graphs. Extensive experiments based on three public datasets demonstrate the superiority of ActiLabel over state-of-the-art transfer learning and deep learning methods.