Efficient greedy learning of Gaussian mixture models
Neural Computation
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
Wearable Activity Tracking in Car Manufacturing
IEEE Pervasive Computing
iSAX: indexing and mining terabyte sized time series
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Preprocessing techniques for context recognition from accelerometer data
Personal and Ubiquitous Computing
Wearable assistant for Parkinson's disease patients with the freezing of gait symptom
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
Feature selection and activity recognition from wearable sensors
UCS'06 Proceedings of the Third international conference on Ubiquitous Computing Systems
Discovering deformable motifs in continuous time series data
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Feature learning for activity recognition in ubiquitous computing
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Introducing a New Benchmarked Dataset for Activity Monitoring
ISWC '12 Proceedings of the 2012 16th Annual International Symposium on Wearable Computers (ISWC)
USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Dog's life: wearable activity recognition for dogs
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
Automatic correction of annotation boundaries in activity datasets by class separation maximization
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Automatic assessment of problem behaviour in developmental disabilities
ACM SIGACCESS Accessibility and Computing
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The majority of activity recognition systems in wearable computing rely on a set of statistical measures, such as means and moments, extracted from short frames of continuous sensor measurements to perform recognition. These features implicitly quantify the distribution of data observed in each frame. However, feature selection remains challenging and labour intensive, rendering a more generic method to quantify distributions in accelerometer data much desired. In this paper we present the ECDF representation, a novel approach to preserve characteristics of arbitrary distributions for feature extraction, which is particularly suitable for embedded applications. In extensive experiments on six publicly available datasets we demonstrate that it outperforms common approaches to feature extraction across a wide variety of tasks.