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Sensors 2016, 16(4), 426; doi:10.3390/s16040426

Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors

Pervasive Systems Group, Department of Computer Science, Zilverling Building, PO-Box 217, 7500 AE Enschede, The Netherlands
Department of Computer Engineering, Galatasaray University, Ortakoy, 34349 Istanbul, Turkey
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Academic Editor: Kamiar Aminian
Received: 22 January 2016 / Revised: 23 February 2016 / Accepted: 17 March 2016 / Published: 24 March 2016
(This article belongs to the Special Issue Body Worn Behavior Sensing)
View Full-Text   |   Download PDF [2213 KB, uploaded 24 March 2016]   |  


The position of on-body motion sensors plays an important role in human activity recognition. Most often, mobile phone sensors at the trouser pocket or an equivalent position are used for this purpose. However, this position is not suitable for recognizing activities that involve hand gestures, such as smoking, eating, drinking coffee and giving a talk. To recognize such activities, wrist-worn motion sensors are used. However, these two positions are mainly used in isolation. To use richer context information, we evaluate three motion sensors (accelerometer, gyroscope and linear acceleration sensor) at both wrist and pocket positions. Using three classifiers, we show that the combination of these two positions outperforms the wrist position alone, mainly at smaller segmentation windows. Another problem is that less-repetitive activities, such as smoking, eating, giving a talk and drinking coffee, cannot be recognized easily at smaller segmentation windows unlike repetitive activities, like walking, jogging and biking. For this purpose, we evaluate the effect of seven window sizes (2–30 s) on thirteen activities and show how increasing window size affects these various activities in different ways. We also propose various optimizations to further improve the recognition of these activities. For reproducibility, we make our dataset publicly available. View Full-Text
Keywords: body-worn sensing; behavior analysis; sensor fusion; gesture recognition; smartwatch sensors; smoking recognition body-worn sensing; behavior analysis; sensor fusion; gesture recognition; smartwatch sensors; smoking recognition

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Shoaib, M.; Bosch, S.; Incel, O.D.; Scholten, H.; Havinga, P.J.M. Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors. Sensors 2016, 16, 426.

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