Multi-sensor fusion for human daily activity recognition in robot-assisted living

Authors:
Chun Zhu;Weihua Sheng
Affiliations:
Oklahoma State University, Stillwater, OK, USA;Oklahoma State University, Stillwater, OK, USA
Venue:
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
Year:
2009

Citing 4
Cited 2

A tutorial on hidden Markov models and selected applications in speech recognition

Readings in speech recognition
Naive Bayes models for probability estimation

ICML '05 Proceedings of the 22nd international conference on Machine learning
Body Sensor Networks

Body Sensor Networks
A survey of advances in vision-based human motion capture and analysis

Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour

Motion- and location-based online human daily activity recognition

Pervasive and Mobile Computing
Physical activity recognition using a single triaxial accelerometer and a barometric sensor for baby and child care in a home environment

Journal of Ambient Intelligence and Smart Environments

Quantified Score

Hi-index	0.00

Visualization

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.