Research Article
Real time event-based segmentation to classify locomotion activities through a single inertial sensor
@ARTICLE{10.4108/eai.14-10-2015.2261695, author={Benish Fida and Daniele Bibbo and Ivan Bernabucci and Antonino Proto and Silvia Conforto and Maurizio Schmid}, title={Real time event-based segmentation to classify locomotion activities through a single inertial sensor}, journal={EAI Endorsed Transactions on Mobile Communications and Applications}, volume={3}, number={9}, publisher={ACM}, journal_a={MCA}, year={2017}, month={4}, keywords={gait events, dynamic segmentation, locomotion activities, inertial sensors, classification}, doi={10.4108/eai.14-10-2015.2261695} }
- Benish Fida
Daniele Bibbo
Ivan Bernabucci
Antonino Proto
Silvia Conforto
Maurizio Schmid
Year: 2017
Real time event-based segmentation to classify locomotion activities through a single inertial sensor
MCA
EAI
DOI: 10.4108/eai.14-10-2015.2261695
Abstract
We propose an event-based dynamic segmentation technique for the classification of locomotion activities, able to detect the mid-swing, initial contact and end contact events. This technique is based on the use of a shank-mounted inertial sensor incorporating a tri-axial accelerometer and a tri-axial gyroscope, and it is tested on four different locomotion activities: walking, stair ascent, stair descent and running. Gyroscope data along one component are used to dynamically determine the window size for segmentation, and a number of features are then extracted from these segments. The event-based segmentation technique has been compared against three different fixed window size segmentations, in terms of classification accuracy on two different datasets, and with two different feature sets. The dynamic event-based segmentation showed an improvement in terms of accuracy of around 5% (97% vs. 92% and 92% vs. 87%) and 1-2% (89% vs. 87% and 97% vs. 96%) for the two dataset, respectively, thus confirming the need to incorporate an event-based criterion to increase performance in the classification of motion activities.
Copyright © 2015 B. Fida et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.