Login (DCU Staff Only)
Login (DCU Staff Only)

DORAS | DCU Research Repository

Explore open access research and scholarly works from DCU

Advanced Search

Kinect vs. low-cost inertial sensing For gesture recognition

Gowing, Marc, Ahmadi, Amin, Destelle, Francois, Monaghan, David orcid logoORCID: 0000-0002-5169-9902, O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 and Moran, Kieran orcid logoORCID: 0000-0003-2015-8967 (2014) Kinect vs. low-cost inertial sensing For gesture recognition. In: International Conference on MultiMedia Modeling (MMM 2014), 6-10 Jan 2014, Dublin, ireland.

Abstract
In this paper, we investigate efficient recognition of human gestures / movements from multimedia and multimodal data, including the Microsoft Kinect and translational and rotational acceleration and velocity from wearable inertial sensors. We firstly present a system that automatically classifies a large range of activities (17 different gestures) using a random forest decision tree. Our system can achieve near real time recognition by appropriately selecting the sensors that led to the greatest contributing factor for a particular task. Features extracted from multimodal sensor data were used to train and evaluate a customized classifier. This novel technique is capable of successfully classifying var- ious gestures with up to 91 % overall accuracy on a publicly available data set. Secondly we investigate a wide range of different motion capture modalities and compare their results in terms of gesture recognition accu- racy using our proposed approach. We conclude that gesture recognition can be effectively performed by considering an approach that overcomes many of the limitations associated with the Kinect and potentially paves the way for low-cost gesture recognition in unconstrained environments.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Engineering > Signal processing
Medical Sciences > Sports sciences
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Science and Health > School of Health and Human Performance
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:SFI Grant Number 12/RC/2289
ID Code:20598
Deposited On:27 May 2015 13:10 by Kevin Fraser . Last Modified 19 Oct 2018 13:28
Documents

Full text available as:

[thumbnail of Kinect vs. Low-cost Inertial Sensing For Gesture Recognition]
Preview
PDF (Kinect vs. Low-cost Inertial Sensing For Gesture Recognition) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
650kB
Downloads

Downloads

Downloads per month over past year

Archive Staff Only: edit this record