Authors:
Abdulrahman Alruban
1
;
Hind Alobaidi
2
;
Nathan Clarke
3
and
Fudong Li
4
Affiliations:
1
Centre for Security, Communications and Network Research, Plymouth University, Plymouth, U.K., Computer Sciences and Information Technology College, Majmaah University, Majmaah and Saudi Arabia
;
2
Centre for Security, Communications and Network Research, Plymouth University, Plymouth, U.K., College of Education for Pure Science, University of Baghdad, Baghdad and Iraq
;
3
Centre for Security, Communications and Network Research, Plymouth University, Plymouth, U.K., Security Research Institute, Edith Cowan University Perth Western Australia and Australia
;
4
Centre for Security, Communications and Network Research, Plymouth University, Plymouth, U.K., School of Computing, University of Portsmouth, Portsmouth and U.K.
Keyword(s):
Human Activity Recognition, Smartphone Sensors, Gait Activity, Gyroscope, Accelerometer.
Related
Ontology
Subjects/Areas/Topics:
Ensemble Methods
;
Feature Selection and Extraction
;
Pattern Recognition
;
Theory and Methods
Abstract:
Human physical motion activity identification has many potential applications in various fields, such as medical diagnosis, military sensing, sports analysis, and human-computer security interaction. With the recent advances in smartphones and wearable technologies, it has become common for such devices to have embedded motion sensors that are able to sense even small body movements. This study collected human activity data from 60 participants across two different days for a total of six activities recorded by gyroscope and accelerometer sensors in a modern smartphone. The paper investigates to what extent different activities can be identified by utilising machine learning algorithms using approaches such as majority algorithmic voting. More analyses are also provided that reveal which time and frequency domain-based features were best able to identify individuals’ motion activity types. Overall, the proposed approach achieved a classification accuracy of 98% in identifying four di
fferent activities: walking, walking upstairs, walking downstairs, and sitting (on a chair) while the subject is calm and doing a typical desk-based activity.
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