TSSEARCH: Time Series Subsequence Search Library

Folgado, Duarte; Barandas, Marília; Antunes, Margarida; Nunes, Maria Lua; Liu, Hui; Hartmann, Yale; Schultz, Tanja; Gambôa, Hugo

doi:10.1016/j.softx.2022.101049

Cited by 26 publications

(21 citation statements)

References 13 publications

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“…Hence, we want to approach more types of input features (such as from the time series feature extraction library [ 36 ]) to compare the performances of alternatives. In addition, the Time Series Subsequence Search Library (TSSEARCH) also can be implemented to extract features [ 46 ]. Thirdly, we intend to implement the whole system in different types of upper-limb-assistive robots (exoskeleton or assistive robotic arm extender) for online testing.…”

Section: Future Workmentioning

confidence: 99%

Learning-Based Motion-Intention Prediction for End-Point Control of Upper-Limb-Assistive Robots

Yang

Garg

Gao

et al. 2023

Sensors

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The lack of intuitive and active human–robot interaction makes it difficult to use upper-limb-assistive devices. In this paper, we propose a novel learning-based controller that intuitively uses onset motion to predict the desired end-point position for an assistive robot. A multi-modal sensing system comprising inertial measurement units (IMUs), electromyographic (EMG) sensors, and mechanomyography (MMG) sensors was implemented. This system was used to acquire kinematic and physiological signals during reaching and placing tasks performed by five healthy subjects. The onset motion data of each motion trial were extracted to input into traditional regression models and deep learning models for training and testing. The models can predict the position of the hand in planar space, which is the reference position for low-level position controllers. The results show that using IMU sensor with the proposed prediction model is sufficient for motion intention detection, which can provide almost the same prediction performance compared with adding EMG or MMG. Additionally, recurrent neural network (RNN)-based models can predict target positions over a short onset time window for reaching motions and are suitable for predicting targets over a longer horizon for placing tasks. This study’s detailed analysis can improve the usability of the assistive/rehabilitation robots.

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Section: Future Workmentioning

confidence: 99%

Learning-Based Motion-Intention Prediction for End-Point Control of Upper-Limb-Assistive Robots

Yang

Garg

Gao

et al. 2023

Sensors

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“…Last but not least, Viktor Moskalenko et al used a UNet-like full convolutional NN for the ECG signal segmentation of P and T waves, as well as the QRS complex [ 64 ]. Without model training, the ECG segmentation can also be solved through a subsequence search in the context of a carefully selected query pattern [ 65 ].…”

Section: Related Literaturementioning

confidence: 99%

Feature-Based Information Retrieval of Multimodal Biosignals with a Self-Similarity Matrix: Focus on Automatic Segmentation

et al. 2022

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Biosignal-based technology has been increasingly available in our daily life, being a critical information source. Wearable biosensors have been widely applied in, among others, biometrics, sports, health care, rehabilitation assistance, and edutainment. Continuous data collection from biodevices provides a valuable volume of information, which needs to be curated and prepared before serving machine learning applications. One of the universal preparation steps is data segmentation and labelling/annotation. This work proposes a practical and manageable way to automatically segment and label single-channel or multimodal biosignal data using a self-similarity matrix (SSM) computed with signals’ feature-based representation. Applied to public biosignal datasets and a benchmark for change point detection, the proposed approach delivered lucid visual support in interpreting the biosignals with the SSM while performing accurate automatic segmentation of biosignals with the help of the novelty function and associating the segments grounded on their similarity measures with the similarity profiles. The proposed method performed superior to other algorithms in most cases of a series of automatic biosignal segmentation tasks; of equal appeal is that it provides an intuitive visualization for information retrieval of multimodal biosignals.

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“…Outliers/anomalies/novelties contained in time series, such as biosignals, can be detected by pattern analysis based on training or pure statistical models, for which state-of-the-art techniques include the search for novelty [14] and subsequence [16] for biomedical corpora. Outliers in datasets composed of discrete points, which is the research object of this article, can be studied using graph theory algorithms or model training.…”

Section: Introductionmentioning

confidence: 99%

MS2OD: outlier detection using minimum spanning tree and medoid selection

Li,

Wang

et al. 2024

Mach. Learn.: Sci. Technol.

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As an essential task in data mining, outlier detection identifies abnormal patterns in numerous applications, among which clustering-based outlier detection is one of the most popular methods for its effectiveness in detecting cluster-related outliers, especially in medical applications. This article presents an advanced method to extract cluster-based outliers by employing a scaled minimum spanning tree (MST) data structure and a new medoid selection method: 1. We compute a scaled MST and iteratively cut the current longest edge to obtain clusters; 2. We apply a new medoid selection method, considering the noise effect to improve the quality of cluster-based outlier identification. The experimental results on real-world data, including extensive medical corpora and other semantically meaningful datasets, demonstrate the wide applicability and outperforming metrics of the proposed method.

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TSSEARCH: Time Series Subsequence Search Library

Cited by 26 publications

References 13 publications

Learning-Based Motion-Intention Prediction for End-Point Control of Upper-Limb-Assistive Robots

Learning-Based Motion-Intention Prediction for End-Point Control of Upper-Limb-Assistive Robots

Feature-Based Information Retrieval of Multimodal Biosignals with a Self-Similarity Matrix: Focus on Automatic Segmentation

MS2OD: outlier detection using minimum spanning tree and medoid selection

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