Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Mining models of human activities from the web
Proceedings of the 13th international conference on World Wide Web
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
International Journal of Robotics Research
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
On using existing time-use study data for ubiquitous computing applications
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Daily Routine Recognition through Activity Spotting
LoCA '09 Proceedings of the 4th International Symposium on Location and Context Awareness
Using rhythm awareness in long-term activity recognition
ISWC '08 Proceedings of the 2008 12th IEEE International Symposium on Wearable Computers
Using situation lattices in sensor analysis
PERCOM '09 Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications
Is it really about me?: message content in social awareness streams
Proceedings of the 2010 ACM conference on Computer supported cooperative work
Making the ordinary visible in microblogs
Personal and Ubiquitous Computing
Scalable recognition of daily activities with wearable sensors
LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
Identifying the activities supported by locations with community-authored content
Proceedings of the 12th ACM international conference on Ubiquitous computing
On using temporal features to create more accurate human-activity classifiers
AICS'09 Proceedings of the 20th Irish conference on Artificial intelligence and cognitive science
Proceedings of the 2nd Augmented Human International Conference
Opportunities exist: continuous discovery of places to perform activities
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Scikit-learn: Machine Learning in Python
The Journal of Machine Learning Research
Improving activity recognition without sensor data: a comparison study of time use surveys
Proceedings of the 4th Augmented Human International Conference
Prior knowledge of human activities from social data
Proceedings of the 2013 International Symposium on Wearable Computers
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Human activity recognition is a core component of context-aware, ubiquitous computing systems. Traditionally, this task is accomplished by analyzing signals of wearable motion sensors. While such signals can effectively distinguish various low-level activities (e.g. walking or standing), two issues exist: First, high-level activities (e.g. watching movies or attending lectures) are difficult to distinguish from motion data alone. Second, instrumentation of complex body sensor network at population scale is impractical. In this work, we take an alternative approach of leveraging rich, dynamic, and crowd-generated self-report data as the basis for in-situ activity recognition. By treating the user as the "sensor", we make use of implicit signals emitted from natural use of mobile smart-phones. Applying an L1-regularized Linear SVM on features derived from textual content, semantic location, and time, we are able to infer 10 meaningful classes of daily life activities with a mean accuracy of up to 83.9%. Our work illustrates a promising first step towards comprehensive, high-level activity recognition using free, crowd-generated, social media data.