A smartphone-based model for human activity recognition
Author
Source
Ibn al-Haitham Journal for Pure and Applied Science
Issue
Vol. 30, Issue 3 (31 Dec. 2017), pp.243-250, 8 p.
Publisher
University of Baghdad College of Education for Pure Science / Ibn al-Haitham
Publication Date
2017-12-31
Country of Publication
Iraq
No. of Pages
8
Main Subjects
Natural & Life Sciences (Multidisciplinary)
Abstract EN
Activity recognition (AR) is a new interesting and challenging research area with many applications (e.g.
healthcare, security, and event detection).
Basically, activity recognition (e.g.
identifying user's physical activity) is more likely to be considered as a classification problem.
In this paper, a combination of 7 classification methods is employed and experimented on accelerometer data collected via smartphones, and compared for best performance.
The dataset is collected from 59 individuals who performed 6 different activities (i.e.
walk, jog, sit, stand, upstairs, and downstairs).
The total number of dataset instances is 5418 with 46 labeled features.
The results show that the proposed method of ensemble boost-based classifier overperfomis other classifiers that were examined in this research paper.
American Psychological Association (APA)
al-Tai, Ali. 2017. A smartphone-based model for human activity recognition. Ibn al-Haitham Journal for Pure and Applied Science،Vol. 30, no. 3, pp.243-250.
https://search.emarefa.net/detail/BIM-852177
Modern Language Association (MLA)
al-Tai, Ali. A smartphone-based model for human activity recognition. Ibn al-Haitham Journal for Pure and Applied Science Vol. 30, no. 3 (2017), pp.243-250.
https://search.emarefa.net/detail/BIM-852177
American Medical Association (AMA)
al-Tai, Ali. A smartphone-based model for human activity recognition. Ibn al-Haitham Journal for Pure and Applied Science. 2017. Vol. 30, no. 3, pp.243-250.
https://search.emarefa.net/detail/BIM-852177
Data Type
Journal Articles
Language
English
Notes
Includes appendices : p. 248-250
Record ID
BIM-852177