Next Article in Journal
Convergent Validity of a Wearable Sensor System for Measuring Sub-Task Performance during the Timed Up-and-Go Test
Next Article in Special Issue
A Smartphone Indoor Localization Algorithm Based on WLAN Location Fingerprinting with Feature Extraction and Clustering
Previous Article in Journal
A Miniature Aerosol Sensor for Detecting Polydisperse Airborne Ultrafine Particles
Previous Article in Special Issue
A Robust Crowdsourcing-Based Indoor Localization System
Article

Smartphone Location-Independent Physical Activity Recognition Based on Transportation Natural Vibration Analysis

1
Department of Computer Science and Engineering, Kyung Hee University, (Global Campus), 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104, Korea
2
Telemedicine Cluster of the Biomedical Signals and Systems Group, University of Twente, Enschede 7500AE, The Netherlands
*
Author to whom correspondence should be addressed.
Academic Editor: Antonio R. Jiménez
Sensors 2017, 17(4), 931; https://doi.org/10.3390/s17040931
Received: 1 January 2017 / Revised: 18 April 2017 / Accepted: 19 April 2017 / Published: 23 April 2017
(This article belongs to the Special Issue Smartphone-based Pedestrian Localization and Navigation)
Activity recognition through smartphones has been proposed for a variety of applications. The orientation of the smartphone has a significant effect on the recognition accuracy; thus, researchers generally propose using features invariant to orientation or displacement to achieve this goal. However, those features reduce the capability of the recognition system to differentiate among some specific commuting activities (e.g., bus and subway) that normally involve similar postures. In this work, we recognize those activities by analyzing the vibrations of the vehicle in which the user is traveling. We extract natural vibration features of buses and subways to distinguish between them and address the confusion that can arise because the activities are both static in terms of user movement. We use the gyroscope to fix the accelerometer to the direction of gravity to achieve an orientation-free use of the sensor. We also propose a correction algorithm to increase the accuracy when used in free living conditions and a battery saving algorithm to consume less power without reducing performance. Our experimental results show that the proposed system can adequately recognize each activity, yielding better accuracy in the detection of bus and subway activities than existing methods. View Full-Text
Keywords: movement activity recognition; vehicle natural vibration; natural vibration feature extraction; correction algorithm; smartphone movement activity recognition; vehicle natural vibration; natural vibration feature extraction; correction algorithm; smartphone
Show Figures

Figure 1

MDPI and ACS Style

Hur, T.; Bang, J.; Kim, D.; Banos, O.; Lee, S. Smartphone Location-Independent Physical Activity Recognition Based on Transportation Natural Vibration Analysis. Sensors 2017, 17, 931. https://doi.org/10.3390/s17040931

AMA Style

Hur T, Bang J, Kim D, Banos O, Lee S. Smartphone Location-Independent Physical Activity Recognition Based on Transportation Natural Vibration Analysis. Sensors. 2017; 17(4):931. https://doi.org/10.3390/s17040931

Chicago/Turabian Style

Hur, Taeho, Jaehun Bang, Dohyeong Kim, Oresti Banos, and Sungyoung Lee. 2017. "Smartphone Location-Independent Physical Activity Recognition Based on Transportation Natural Vibration Analysis" Sensors 17, no. 4: 931. https://doi.org/10.3390/s17040931

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop