Sensor-Based Human Activity Recognition in a Multi-user Scenario

Existing work on sensor-based activity recognition focuses mainly on single-user activities However, in real life, activities are often performed by multiple users involving interactions between them In this paper. we propose Coupled Hidden Markov Models (CHMMs) to recognize multi-user activities from sensor readings in a smart home environment We develop a multimodal sensing platform and present a theoretical framework to recognize both single-user and multi-user activities We conduct our trace collection done in a smart home, and evaluate our framework through experimental studies Our experimental result shows that we achieve an average accuracy of 85 46% with CHMMs