Accurate Subsequence Matching on Data Stream under Time Warping Distance

Niennattrakul, Vit; Wanichsan, Dechawut; Ratanamahatana, Chotirat Ann

doi:10.1007/978-3-642-14640-4_12

Cited by 12 publications

(7 citation statements)

References 12 publications

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“…The automatic trial walk segmentation is evaluated by computing the Rand index [ 73 ] and the accuracy on detection (AoD) [ 74 ], which here is defined as follows. Let indicate the support of a subsequence , and let indicate the corresponding ground-truth support.…”

Section: Resultsmentioning

confidence: 99%

Automated extraction and validation of children’s gait parameters with the Kinect

et al. 2015

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BackgroundGait analysis for therapy regimen prescription and monitoring requires patients to physically access clinics with specialized equipment. The timely availability of such infrastructure at the right frequency is especially important for small children. Besides being very costly, this is a challenge for many children living in rural areas. This is why this work develops a low-cost, portable, and automated approach for in-home gait analysis, based on the Microsoft Kinect.MethodsA robust and efficient method for extracting gait parameters is introduced, which copes with the high variability of noisy Kinect skeleton tracking data experienced across the population of young children. This is achieved by temporally segmenting the data with an approach based on coupling a probabilistic matching of stride template models, learned offline, with the estimation of their global and local temporal scaling. A preliminary study conducted on healthy children between 2 and 4 years of age is performed to analyze the accuracy, precision, repeatability, and concurrent validity of the proposed method against the GAITRite when measuring several spatial and temporal children’s gait parameters.ResultsThe method has excellent accuracy and good precision, with segmenting temporal sequences of body joint locations into stride and step cycles. Also, the spatial and temporal gait parameters, estimated automatically, exhibit good concurrent validity with those provided by the GAITRite, as well as very good repeatability. In particular, on a range of nine gait parameters, the relative and absolute agreements were found to be good and excellent, and the overall agreements were found to be good and moderate.ConclusionThis work enables and validates the automated use of the Kinect for children’s gait analysis in healthy subjects. In particular, the approach makes a step forward towards developing a low-cost, portable, parent-operated in-home tool for clinicians assisting young children.

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Section: Resultsmentioning

confidence: 99%

Automated extraction and validation of children’s gait parameters with the Kinect

et al. 2015

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“…The details of all planted patterns are shown in Table II, and the resulting datasets are shown in Figure 4. To evaluate our algorithm, we utilize the previously proposed Accuracy-on-Detection (AoD) [14], which measures overlapping percentage of any subsequence pair in subsequence matching problem. We calculate the overlap of the output motifs and the planted patterns to measure the accuracy.…”

Section: Methodsmentioning

confidence: 99%

Parameter-free motif discovery for time series data

Nunthanid

Niennattrakul

Ratanamahatana

2012

2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technolog

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Time series motif discovery is an increasingly popular research area in time series mining whose main objective is to search for interesting patterns or motifs. A motif is a pair of time series subsequences, or two subsequences whose shapes are very similar to each other.Typical motif discovery algorithm requires a predefined motif length as its parameter.Discovering motif with arbitrary lengths introduces another problem, where selecting a suitable length for the motif is nontrivial since domain knowledge is often required. Thus, this work proposes a parameter-free motif discovery algorithm called k -Best Motif Discovery (k BMD) which requires no parameter as input, and as a result returns a set of all "Best Motif " that are ranked by our proposed scoring function which is based on similarity of motif locations and similarity of motif shapes.

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“…The algorithm is evaluated based on 5 measurements: AoD (accuracy-on-detection) [ 72 ], sensitivity, specificity, positive predictive value, and false alarm rate. Before stating how to evaluate the detected anomaly subsequence, the overlap criteria must be explained.…”

Section: Methodsmentioning

confidence: 99%

“…( 2) Accuracy on Detection (AoD). AoD [ 72 ] is used to indicate how well an algorithm can recognize anomalous ECG beats. AoD refers to an average percentage of detected subsequences that cover real anomaly beats.…”

Section: Methodsmentioning

confidence: 99%

Robust and Accurate Anomaly Detection in ECG Artifacts Using Time Series Motif Discovery

Sivaraks

Ratanamahatana

2015

Computational and Mathematical Methods in Medicine

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Electrocardiogram (ECG) anomaly detection is an important technique for detecting dissimilar heartbeats which helps identify abnormal ECGs before the diagnosis process. Currently available ECG anomaly detection methods, ranging from academic research to commercial ECG machines, still suffer from a high false alarm rate because these methods are not able to differentiate ECG artifacts from real ECG signal, especially, in ECG artifacts that are similar to ECG signals in terms of shape and/or frequency. The problem leads to high vigilance for physicians and misinterpretation risk for nonspecialists. Therefore, this work proposes a novel anomaly detection technique that is highly robust and accurate in the presence of ECG artifacts which can effectively reduce the false alarm rate. Expert knowledge from cardiologists and motif discovery technique is utilized in our design. In addition, every step of the algorithm conforms to the interpretation of cardiologists. Our method can be utilized to both single-lead ECGs and multilead ECGs. Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists. Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate. The results demonstrate that our proposed method is highly accurate and robust to artifacts, compared with competitive anomaly detection methods.

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Accurate Subsequence Matching on Data Stream under Time Warping Distance

Cited by 12 publications

References 12 publications

Automated extraction and validation of children’s gait parameters with the Kinect

Automated extraction and validation of children’s gait parameters with the Kinect

Parameter-free motif discovery for time series data

Robust and Accurate Anomaly Detection in ECG Artifacts Using Time Series Motif Discovery

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