ICRA'09 Paper Abstract


Paper FrB7.3

Zhu, Chun (Oklahoma State University), Sheng, Weihua (Oklahoma State University)

Human Daily Activity Recognition in Robot-Assisted Living Using Multi-Sensor Fusion

Scheduled for presentation during the Regular Sessions "Robot Companions and Social Robots in Home Environments" (FrB7), Friday, May 15, 2009, 11:10−11:30, Room: 405

2009 IEEE International Conference on Robotics and Automation, May 12 - 17, 2009, Kobe, Japan

This information is tentative and subject to change. Compiled on January 21, 2022

Keywords Domestic Robots, Personal Robots, Cognitive Human-Robot Interaction


In this paper, we propose a human daily activity recognition method by fusing the data from two wearable inertial sensors attached on one foot and the waist of the subject, respectively. We developed a multi-sensor fusion scheme for activity recognition. First, data from these two sensors are fused for coarse-grained classification in order to determine the type of the activity: zero displacement activity, transitional activity, and strong displacement activity. Second, a fine-grained classification module based on heuristic discrimination or hidden Markov models (HMMs) is applied to further distinguish the activities. We conducted experiments using a prototype wearable sensor system and the obtained results prove the effectiveness and accuracy of our algorithm.



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