On Clustering Validation Techniques
Journal of Intelligent Information Systems
The Journal of Machine Learning Research
eWatch: A Wearable Sensor and Notification Platform
BSN '06 Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks
The Mobile Sensing Platform: An Embedded Activity Recognition System
IEEE Pervasive Computing
Discovery of activity patterns using topic models
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Proceedings of the 6th ACM conference on Embedded network sensor systems
A framework of energy efficient mobile sensing for automatic user state recognition
Proceedings of the 7th international conference on Mobile systems, applications, and services
PERCOM '09 Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications
Predicting human behaviour from selected mobile phone data points
Proceedings of the 12th ACM international conference on Ubiquitous computing
The Jigsaw continuous sensing engine for mobile phone applications
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Activity recognition using cell phone accelerometers
ACM SIGKDD Explorations Newsletter
Accurate, low-energy trajectory mapping for mobile devices
Proceedings of the 8th USENIX conference on Networked systems design and implementation
Mining Discriminative Patterns for Classifying Trajectories on Road Networks
IEEE Transactions on Knowledge and Data Engineering
What Can an Arm Holster Worn Smart Phone Do for Activity Recognition?
ISWC '11 Proceedings of the 2011 15th Annual International Symposium on Wearable Computers
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)
SAMMPLE: Detecting Semantic Indoor Activities in Practical Settings Using Locomotive Signatures
ISWC '12 Proceedings of the 2012 16th Annual International Symposium on Wearable Computers (ISWC)
| Hi-index | 0.00 |
Complex activities are activities that are a combination of many simple ones. Typically, activities of daily living (ADLs) fall in this category. Complex activity recognition is an active area of interest amongst sensing and knowledge mining community today. A majority of investigations along this vein has happened in controlled experimental settings, with multiple wearable and object-interaction sensors. This provides rich observation data for mining. Recently, a new and challenging problem is to investigate recognition accuracy of complex activities in the wild using the smartphone. In this paper, we study the strength of the energy-friendly, cheap, and ubiquitous accelerometer sensor, towards recognizing complex activities in a complete real-life setting. In particular, along the lines of hierarchical feature construction, we investigate multiple higher-order features from the raw sensor stream (x, y, z, t). Further, we propose and evaluate two SVM-based fusion mechanisms (early fusion vs. late fusion) using the higher-order features. Our results show promising performance improvements in recognizing complex activities, w. r.t. prior results in such settings.