Title :
Multi-sensor fusion for human daily activity recognition in robot-assisted living
Author :
Zhu, Chun ; Sheng, Weihua
Author_Institution :
Sch. of Electr. & Comput. Eng., Oklahoma State Univ., Stillwater, OK, USA
Abstract :
In this paper, we propose a human activity recognition method by fusing the data from two wearable inertial sensors attached to one foot and the waist of a human subject, respectively. Our multi-sensor fusion based method combines neural networks and hidden Markov models (HMMs), and can reduce the computation load. We conducted experiments using a prototype wearable sensor system and the obtained results prove the effectiveness and the accuracy of our algorithm.
Keywords :
body sensor networks; geriatrics; hidden Markov models; medical control systems; mobile robots; neural nets; object recognition; sensor fusion; wearable computers; HMM; computation load reduction; data fusion; hidden Markov models; human activity recognition method; multisensor fusion based method; neural networks; robot-assisted living; wearable inertial sensors; Foot; Hidden Markov models; Humans; Legged locomotion; Robot sensing systems; Sensor fusion; Activity Recognition; Assisted Living; Sensor Fusion; Wearable Sensor;
Conference_Titel :
Human-Robot Interaction (HRI), 2009 4th ACM/IEEE International Conference on
Conference_Location :
La Jolla, CA
Print_ISBN :
978-1-60558-404-1
