A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
An API for Integrating Spatial Context Models with Spatial Reasoning Algorithms
PERCOMW '06 Proceedings of the 4th annual IEEE international conference on Pervasive Computing and Communications Workshops
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
Toward accurate dynamic time warping in linear time and space
Intelligent Data Analysis
Sensing and using social context
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
High accuracy context recovery using clustering mechanisms
PERCOM '09 Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications
Towards a taxonomy of movement patterns
Information Visualization
DECODE: Exploiting Shadow Fading to DEtect COMoving Wireless DEvices
IEEE Transactions on Mobile Computing
SensLoc: sensing everyday places and paths using less energy
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Tracking vehicular speed variations by warping mobile phone signal strengths
PERCOM '11 Proceedings of the 2011 IEEE International Conference on Pervasive Computing and Communications
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
Contextual Grouping: Discovering Real-Life Interaction Types from Longitudinal Bluetooth Data
MDM '11 Proceedings of the 2011 IEEE 12th International Conference on Mobile Data Management - Volume 01
Correlation analysis of discrete motions
GIScience'06 Proceedings of the 4th international conference on Geographic Information Science
Sense and sensibility in a pervasive world
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
Detecting pedestrian flocks by fusion of multi-modal sensors in mobile phones
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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The vast availability of mobile phones with built-in movement and location sensors enables the collection of detailed information about human movement even indoors. As mobility is a key element of many processes and activities, an interesting class of information to extract is movement patterns that quantify how humans move, interact and group. In this paper we propose methods for detecting two common pedestrian movement patterns, namely individual following relations and group leadership. The proposed methods for identifying following patterns employ machine learning on features derived using similarity analysis on time-lagged sequences of WiFi measurements containing either raw signal strength values or derived locations. To detect leadership we combine the individual following relations into directed graphs and detect leadership within groups by graph link analysis. Methods for detecting these movement patterns open up new possibilities in-amongst others-computational social science, reality mining, marketing research and location-based gaming. We provide evaluation results that show error rates down to 7%, improving over state-of-the-art methods with up to eleven percentage points for following patterns and up to twenty percentage points for leadership patterns. Furthermore, we provide an analysis of the computational efficiency of the proposed methods and present visualizations for the analysis of detected patterns. Our methods are, contrary to state of the art, also applicable in challenging indoor environments, e.g., multi-story buildings. This implies that even quite small samples allow us to detect information such as how events and campaigns in multi-story shopping malls may trigger following in small groups, or which group members typically take the lead when triggered by e.g. commercials, or how rescue or police forces act during training exercises.