Authors:
Sumeyye Konak
1
;
Fulya Turan
1
;
Muhammad Shoaib
2
and
Ozlem Durmaz Incel
1
Affiliations:
1
Galatasaray University, Turkey
;
2
University of Twente, Netherlands
Keyword(s):
Activity Recognition, Motion Sensing, Wrist-worn Devices, Mobile Sensing.
Related
Ontology
Subjects/Areas/Topics:
Ambient Intelligence
;
Applications and Services
;
Artificial Intelligence
;
Computer Vision, Visualization and Computer Graphics
;
Context
;
Context-Aware Applications
;
Enterprise Information Systems
;
Human and Computer Interaction
;
Human-Computer Interaction
;
Mobile and Pervasive Computing
;
Mobile Computing
;
Paradigm Trends
;
Pervasive Health
;
Software Engineering
;
Symbolic Systems
;
Telecommunications
Abstract:
With their integrated sensors, wrist-worn devices, such as smart watches, provide an ideal platform for human
activity recognition. Particularly, the inertial sensors, such as accelerometer and gyroscope can efficiently capture
the wrist and arm movements of the users. In this paper, we investigate the use of accelerometer sensor for
recognizing thirteen different activities. Particularly, we analyse how different sets of features extracted from
acceleration readings perform in activity recognition. We categorize the set of features into three classes: motion
related features, orientation-related features and rotation-related features and we analyse the recognition
performance using motion, orientation and rotation information both alone and in combination. We utilize a
dataset collected from 10 participants and use different classification algorithms in the analysis. The results
show that using orientation features achieve the highest accuracies when used alone and in combination wi
th
other sensors. Moreover, using only raw acceleration performs slightly better than using linear acceleration
and similar compared with gyroscope.
(More)