Gesture recognition with inertial sensors and optimized DTW prototypes

Hartmann, Bastian; Link, Norbert

doi:10.1109/icsmc.2010.5641703

Cited by 71 publications

(39 citation statements)

References 16 publications

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“…This was extended by the research presented in [13], where they describe a form of oblique/multi-variate decision tree that uses a branching condition based on conjunctions or disjunctions of shapelets. Shapelets have also been used in futher applications, such as early classification [18] and gesture recognition [8].…”

Section: Introductionmentioning

confidence: 99%

A shapelet transform for time series classification

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Davis

Hills

et al. 2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

278

233

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The problem of time series classification (TSC), where we consider any real-valued ordered data a time series, presents a specific machine learning challenge as the ordering of variables is often crucial in finding the best discriminating features. One of the most promising recent approaches is to find shapelets within a data set. A shapelet is a time series subsequence that is identified as being representative of class membership. The original research in this field embedded the procedure of finding shapelets within a decision tree. We propose disconnecting the process of finding shapelets from the classification algorithm by proposing a shapelet transformation. We describe a means of extracting the k best shapelets from a data set in a single pass, and then use these shapelets to transform data by calculating the distances from a series to each shapelet. We demonstrate that transformation into this new data space can improve classification accuracy, whilst retaining the explanatory power provided by shapelets.

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Section: Introductionmentioning

confidence: 99%

A shapelet transform for time series classification

Lines

Davis

Hills

et al. 2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

278

233

View full text Add to dashboard Cite

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“…It was firstly used in speech recognition [14] although its use has widely extended to other fields [15], [16]. Let x = (x 1 , x 2 , .…”

Section: A Online Techniquesmentioning

confidence: 99%

Recognition of Pen-Based Music Notation: The HOMUS Dataset

Calvo-Zaragoza

Oncina

2014

2014 22nd International Conference on Pattern Recognition

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Abstract-A profitable way of digitizing a new musical composition is by using a pen-based (online) system, in which the score is created with the sole effort of the composition itself. However, the development of such systems is still largely unexplored. Some studies have been carried out but the use of particular little datasets has led to avoid objective comparisons between different approaches. To solve this situation, this work presents the Handwritten Online Musical Symbols (HOMUS) dataset, which consists of 15200 samples of 32 types of musical symbols from 100 different musicians. Several alternatives of recognition for the two modalities -online, using the strokes drawn by the pen, and offline, using the image generated after drawing the symbol-are also presented. Some experiments are included aimed to draw main conclusions about the recognition of these data. It is expected that this work can establish a binding point in the field of recognition of online handwritten music notation and serve as a baseline for future developments.

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“…Because shapelets are small in size compared to original data, algorithms that use shapelets for classification results in less time and space complexity. Shapelets have also been used successfully in many other applications, such as early classification [4], gesture recognition [5] and as a filter transformation for TSC [6].…”

Section: Introductionmentioning

confidence: 99%

Performance of Mahalanobis Distance in Time Series Classification Using Shapelets

Arathi¹,

Govardhan²

2014

IJMLC

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Abstract-Time series data are sequences of values measured over time. One of the most recent approaches to classification of time series data is to find shapelets within a data set. Time series shapelets are time series subsequences which represent a class. In order to compare two time series sequences, existing work uses Euclidean distance measure. The problem with Euclidean distance is that it requires data to be standardized if scales differ. In this paper, we perform classification of time series data using time series shapelets and used Mahalanobis distance measure. The Mahalanobis distance is a descriptive statistic that provides a relative measure of a data point's distance (residual) from a common point. The Mahalanobis distance is used to identify and gauge similarity of an unknown sample set to a known one. It differs from Euclidean distance in that it takes into account the correlations of the data set and is scale-invariant. We show that use of Mahalanobis distance measure instead of Euclidean distance measure in time series dataset classification using shapelets leads to increase in accuracy.

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Gesture recognition with inertial sensors and optimized DTW prototypes

Cited by 71 publications

References 16 publications

A shapelet transform for time series classification

A shapelet transform for time series classification

Recognition of Pen-Based Music Notation: The HOMUS Dataset

Performance of Mahalanobis Distance in Time Series Classification Using Shapelets

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