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

fYear

2009

fDate

11-13 March 2009

Firstpage

303

Lastpage

304

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Human-Robot Interaction (HRI), 2009 4th ACM/IEEE International Conference on

Conference_Location

La Jolla, CA

ISSN

2167-2121

Print_ISBN

978-1-60558-404-1

Type

conf

Filename

6256075