A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Using Gravity to Estimate Accelerometer Orientation
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
Gait analyzer based on a cell phone with a single three-axis accelerometer
Proceedings of the 8th conference on Human-computer interaction with mobile devices and services
Activity sensing in the wild: a field trial of ubifit garden
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Understanding mobility based on GPS data
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Proceedings of the 6th ACM conference on Embedded network sensor systems
UbiGreen: investigating a mobile tool for tracking and supporting green transportation habits
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Activity Recognition from Accelerometer Data on a Mobile Phone
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Activity Recognition for Everyday Life on Mobile Phones
UAHCI '09 Proceedings of the 5th International on ConferenceUniversal Access in Human-Computer Interaction. Part II: Intelligent and Ubiquitous Interaction Environments
IMCE '09 Proceedings of the 1st international workshop on Interactive multimedia for consumer electronics
Understanding transportation modes based on GPS data for web applications
ACM Transactions on the Web (TWEB)
Using mobile phones to determine transportation modes
ACM Transactions on Sensor Networks (TOSN)
Sensing motion using spectral and spatial analysis of WLAN RSSI
EuroSSC'07 Proceedings of the 2nd European conference on Smart sensing and context
Accelerometer Based Transportation Mode Recognition on Mobile Phones
APWCS '10 Proceedings of the 2010 Asia-Pacific Conference on Wearable Computing Systems
A grid-based algorithm for on-device GSM positioning
Proceedings of the 12th ACM international conference on Ubiquitous computing
A survey of mobile phone sensing
IEEE Communications Magazine
SensLoc: sensing everyday places and paths using less energy
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
The Jigsaw continuous sensing engine for mobile phone applications
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Preprocessing techniques for context recognition from accelerometer data
Personal and Ubiquitous Computing
Performance metrics for activity recognition
ACM Transactions on Intelligent Systems and Technology (TIST)
Energy-efficient trajectory tracking for mobile devices
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
Proceedings of the 13th international conference on Ubiquitous computing
Transportation mode detection using mobile phones and GIS information
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Evaluating performance in continuous context recognition using event-driven error characterisation
LoCA'06 Proceedings of the Second international conference on Location- and Context-Awareness
Is human mobility tracking a good idea?
Communications of the ACM
Mobility detection using everyday GSM traces
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Energy-Efficient Continuous Activity Recognition on Mobile Phones: An Activity-Adaptive Approach
ISWC '12 Proceedings of the 2012 16th Annual International Symposium on Wearable Computers (ISWC)
MatkaHupi: a persuasive mobile application for sustainable mobility
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
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We present novel accelerometer-based techniques for accurate and fine-grained detection of transportation modes on smartphones. The primary contributions of our work are an improved algorithm for estimating the gravity component of accelerometer measurements, a novel set of accelerometer features that are able to capture key characteristics of vehicular movement patterns, and a hierarchical decomposition of the detection task. We evaluate our approach using over 150 hours of transportation data, which has been collected from 4 different countries and 16 individuals. Results of the evaluation demonstrate that our approach is able to improve transportation mode detection by over 20% compared to current accelerometer-based systems, while at the same time improving generalization and robustness of the detection. The main performance improvements are obtained for motorised transportation modalities, which currently represent the main challenge for smartphone-based transportation mode detection.