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

DORAS | DCU Research Repository

Explore open access research and scholarly works from DCU

Advanced Search

Smart lifelogging: recognizing human activities using PHASOR

Dao, Minh-Son, Dang-Nguyen, Duc-Tien orcid logoORCID: 0000-0002-2761-2213, Riegler, Michael and Gurrin, Cathal orcid logoORCID: 0000-0003-2903-3968 (2017) Smart lifelogging: recognizing human activities using PHASOR. In: 6th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2017), 24-26 Feb 2017, Porto, Portugal. ISBN 978-989-758-222-6

Abstract
Lifelog, Human Activity Recognition, Smartphones, Embedded Sensors, Smart-City, Heterogeneous Sensory Data Analytics. This paper introduces a new idea for sensor data analytics, named PHASOR, that can recognize and stream individual human activities online. The proposed sensor concept can be utilized to solve some emerging problems in smartcity domain such as health care, urban mobility, or security by creating a lifelog of human activities. PHASOR is created from three ‘components’: ID, model, and Sensor. The first component is to identify which sensor is used to monitor which object (e.g., group of users, individual users, type of smart- phone). The second component decides suitable classifiers for human activities recognition. The last one includes two types: (1) physical sensors that utilize embedded sensors in smartphones to recognize human activities, (2) human factors that uses human interaction to personally increase the accuracy of the detection. The advantage of PHASOR is the error signal is inversely proportional to its lifetime, which is well-suited for lifelogging applications. The proposed concept is evaluated and compared to de-facto datasets as well as state-of-the-art of Human Activity Recognition (HAR) using smartphones, confirming that applying PHASOR can improves the accuracy of HAR.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Lifelog; Human Activity Recognition; Smartphones; Embedded Sensors; Smart-City; Heterogeneous Sensory Data Analytics
Subjects:Computer Science > Lifelog
Computer Science > Information storage and retrieval systems
DCU Faculties and Centres:Research Initiatives and Centres > INSIGHT Centre for Data Analytics
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in: De Marsico, Maria, Sanniti di Baja, Gabriella and Fred, Ana, (eds.) Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2017). 1. Scitepress – Science and Technology Publications. ISBN 978-989-758-222-6
Publisher:Scitepress – Science and Technology Publications
Official URL:https://dx.doi.org/10.5220/0006320907610768
Copyright Information:© 2017 Scitepress
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:21818
Deposited On:29 May 2017 10:02 by Duc-Tien Dang-Nguyen . Last Modified 08 Nov 2021 15:08
Documents

Full text available as:

[thumbnail of Dao_et_al._2017.pdf]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
997kB
Downloads

Downloads

Downloads per month over past year

Archive Staff Only: edit this record