Shape Description by Time Series
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamentals of speech recognition
Fundamentals of speech recognition
Knowledge-based trend detection and diagnosis
Knowledge-based trend detection and diagnosis
A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
Spotting recognition of human gestures from time-varying images
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Exact indexing of dynamic time warping
Knowledge and Information Systems
Temporal reasoning for decision support in medicine
Artificial Intelligence in Medicine
A segmentation-free biometric writer verification method based on continuous dynamic programming
Pattern Recognition Letters
Early Recognition and Prediction of Gestures
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
SAXually Explicit Images: Finding Unusual Shapes
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
On-line signature recognition based on VQ-DTW
Pattern Recognition
Temporal abstraction in intelligent clinical data analysis: A survey
Artificial Intelligence in Medicine
Learning recurrent behaviors from heterogeneous multivariate time-series
Artificial Intelligence in Medicine
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
An elastic partial shape matching technique
Pattern Recognition
A Leaf Image Retrieval Scheme Based on Partial Dynamic Time Warping and Two-Level Filtering
CIT '07 Proceedings of the 7th IEEE International Conference on Computer and Information Technology
Recognizing Upper Body Postures using Textile Strain Sensors
ISWC '07 Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers
An on-line time warping algorithm for tracking musical performances
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Clustering of time series data-a survey
Pattern Recognition
Guest Editorial New Generation of Smart Wearable Health Systems and Applications
IEEE Transactions on Information Technology in Biomedicine
Electroactive polymer-based devices for e-textiles in biomedicine
IEEE Transactions on Information Technology in Biomedicine
Strain sensing fabric for hand posture and gesture monitoring
IEEE Transactions on Information Technology in Biomedicine
Wireless support to poststroke rehabilitation: myheart's neurological rehabilitation concept
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
Automated recognition of sequential patterns in captured motion streams
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Computers in Biology and Medicine
Artificial Intelligence in Medicine
Correlation based dynamic time warping of multivariate time series
Expert Systems with Applications: An International Journal
Continuous time Bayesian network classifiers
Journal of Biomedical Informatics
Exercise repetition detection for resistance training based on smartphones
Personal and Ubiquitous Computing
Expert Systems with Applications: An International Journal
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Objective: The purpose of this study was to assess the performance of a real-time (''open-end'') version of the dynamic time warping (DTW) algorithm for the recognition of motor exercises. Given a possibly incomplete input stream of data and a reference time series, the open-end DTW algorithm computes both the size of the prefix of reference which is best matched by the input, and the dissimilarity between the matched portions. The algorithm was used to provide real-time feedback to neurological patients undergoing motor rehabilitation. Methods and materials: We acquired a dataset of multivariate time series from a sensorized long-sleeve shirt which contains 29 strain sensors distributed on the upper limb. Seven typical rehabilitation exercises were recorded in several variations, both correctly and incorrectly executed, and at various speeds, totaling a data set of 840 time series. Nearest-neighbour classifiers were built according to the outputs of open-end DTW alignments and their global counterparts on exercise pairs. The classifiers were also tested on well-known public datasets from heterogeneous domains. Results: Nonparametric tests show that (1) on full time series the two algorithms achieve the same classification accuracy (p-value =0.32); (2) on partial time series, classifiers based on open-end DTW have a far higher accuracy (@k=0.898 versus @k=0.447;p