An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Constructing Suffix Trees On-Line in Linear Time
Proceedings of the IFIP 12th World Computer Congress on Algorithms, Software, Architecture - Information Processing '92, Volume 1 - Volume I
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Probabilistic discovery of time series motifs
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
Fine-Grained Activity Recognition by Aggregating Abstract Object Usage
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Detection of eating and drinking arm gestures using inertial body-worn sensors
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Analyzing features for activity recognition
Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies
Long-Term Activity Monitoring with a Wearable Sensor Node
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
Wearable Activity Tracking in Car Manufacturing
IEEE Pervasive Computing
Fusion of String-Matched Templates for Continuous Activity Recognition
ISWC '07 Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers
An Analysis of Sensor-Oriented vs. Model-Based Activity Recognition
ISWC '09 Proceedings of the 2009 International Symposium on Wearable Computers
A hybrid discriminative/generative approach for modeling human activities
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Toward unsupervised activity discovery using multi-dimensional motif detection in time series
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Enabling Efficient Time Series Analysis for Wearable Activity Data
ICMLA '09 Proceedings of the 2009 International Conference on Machine Learning and Applications
Tracking free-weight exercises
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
Using a live-in laboratory for ubiquitous computing research
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
A comparison of HMMs and dynamic bayesian networks for recognizing office activities
UM'05 Proceedings of the 10th international conference on User Modeling
NuActiv: recognizing unseen new activities using semantic attribute-based learning
Proceeding of the 11th annual international conference on Mobile systems, applications, and services
Towards never-ending learning from time series streams
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Fine-grained sharing of sensed physical activity: a value sensitive approach
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
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This paper proposes an activity inference system that has been designed for deployment in mood disorder research, which aims at accurately and efficiently recognizing selected leisure activities in week-long continuous data. The approach to achieve this relies on an unobtrusive and wrist-worn data logger, in combination with a custom data mining tool that performs early data abstraction and dense motif discovery to collect evidence for activities. After presenting the system design, a feasibility study on weeks of continuous inertial data from 6 participants investigates both accuracy and execution speed of each of the abstraction and detection steps. Results show that our method is able to detect target activities in a large data set with a comparable precision and recall to more conventional approaches, in approximately the time it takes to download and visualize the logs from the sensor.