Information Retrieval
Detection of early morning daily activities with static home and wearable wireless sensors
EURASIP Journal on Advances in Signal Processing
Accurate activity recognition in a home setting
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Review: Ambient intelligence: Technologies, applications, and opportunities
Pervasive and Mobile Computing
Exploring semi-supervised and active learning for activity recognition
ISWC '08 Proceedings of the 2008 12th IEEE International Symposium on Wearable Computers
An analysis of active learning strategies for sequence labeling tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Recognizing daily activities with RFID-based sensors
Proceedings of the 11th international conference on Ubiquitous computing
Active-learning assisted self-reconfigurable activity recognition in a dynamic environment
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Real-time activity classification using ambient and wearable sensors
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
HMM machine learning and inference for Activities of Daily Living recognition
The Journal of Supercomputing
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
Activity recognition using cell phone accelerometers
ACM SIGKDD Explorations Newsletter
Transferring knowledge of activity recognition across sensor networks
Pervasive'10 Proceedings of the 8th international conference on Pervasive Computing
Hi-index | 0.00 |
Automated activity recognition systems that use probabilistic models require labeled data sets in training phase for learning the model parameters. The parameters are different for every person and every environment. Therefore, for every person or environment, training is needed to be performed from scratch. Obtaining labeled data requires much effort therefore poses challenges on the large scale deployment of activity recognition systems. Active learning can be a solution to this problem. It is a machine learning technique that allows the algorithm to choose the most informative data points to be annotated. Because the algorithm selects the most informative data points, the amount of the labeled data needed for training the model is reduced. In this study, we propose using active learning methods for activity recognition. We use three different informativeness measures for selecting the most informative data points and evaluate their performances using three real world data sets recorded in a home setting. We show through experiments that the required number of data points is reduced by 80% in House A, 73% in House B, and 66% in House C with active learning.