Activity Recognition from Sparsely Labeled Data Using Multi-Instance Learning
LoCA '09 Proceedings of the 4th International Symposium on Location and Context Awareness
Proceedings of the 2nd Augmented Human International Conference
Proceedings of the 13th international conference on Ubiquitous computing
Using active learning to allow activity recognition on a large scale
AmI'11 Proceedings of the Second international conference on Ambient Intelligence
Cross-people mobile-phone based activity recognition
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Personal and Ubiquitous Computing
NuActiv: recognizing unseen new activities using semantic attribute-based learning
Proceeding of the 11th annual international conference on Mobile systems, applications, and services
Towards zero-shot learning for human activity recognition using semantic attribute sequence model
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
Reducing user intervention in incremental activityrecognition for assistive technologies
Proceedings of the 2013 International Symposium on Wearable Computers
Labeling method for acceleration data using an execution sequence of activities
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Sharing training data among different activity classes
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Towards scalable activity recognition: adapting zero-effort crowdsourced acoustic models
Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia
Personal and Ubiquitous Computing
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In recent years research on human activity recognition using wearable sensors has enabled to achieve impressive results on real-world data. However, the most successful activity recognition algorithms require substantial amounts of labeled training data. The generation of this data is not only tedious and error prone but also limits the applicability and scalability of today's approaches. This paper explores and systematically analyzes two different techniques to significantly reduce the required amount of labeled training data. The first technique is based on semi-supervised learning and uses self-training and co-training. The second technique is inspired by active learning. In this approach the system actively asks which data the user should label. With both techniques, the required amount of training data can be reduced significantly while obtaining similar and sometimes even better performance than standard supervised techniques. The experiments are conducted using one of the largest and richest currently available datasets.