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.

• Type: Conference
• Year: 2009
• Where: AMI
• Authors: Liang Wang, Tao Gu, XianPing Tao, Jian Lu
• Keywords: AMI 2009, Artificial Intelligence, Multi-user Activities, Single-user Activities, Smart Home