A DTW Approach for Complex Data A Case Study with Network Data Streams

Silva, Paula R. C.; Vinagre, João; Gama, João

doi:10.1145/3555776.3577638

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Two technological solutions stand out in solving the above challenges in the field of motion recognition and classification -Dynamic Time Warping and Support Vector Machine [5], [6]. DTW has been used to recognize complex human movements as it allows the alignment of time series data that can vary in speed and duration [7]. In the context of a Tai Chi learning system, this solution would enable the system to better understand the movements and nuances of the human practice form, as well as recognize and correct poor movement on an individual-based scale.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhanced Design of a Tai Chi Teaching Assistance System Integrating DTW Algorithm and SVM

Guo

2024

ICST Transactions on Scalable Information Systems

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Physical education using technology has enabled traditional practices like Tai Chi, a martial art known for its multiple health benefits and meditative aspects, to set coordinated goals. This research presents an intelligent Tai Chi Teaching Assistance System supported by the integration of the Dynamic Time Warping algorithm and Support Vector Machine, in which can practitioners providing real-time feedback to improve Tai Chi learning and quality. In the system, the DTWA Dynamic Time Warping Algorithm was used to accurately compare a practitioner’s complex body movements with the Tai Chi standard movements dataset, taking into account execution speed deviations and others. Meanwhile, the SVM was employed to classify the movement as to quality and correctness, thereby being able to provide precise, individual feedback. This hybrid approach ensures a high-motion recognition accuracy rate while also adhering to nuanced Tai Chi requirements. The system was evaluated through detailed testing with various levels of Tai Chi experience. Evaluation showed that the students’ performance and understanding of most Taijiquan movements and related physical exercises improved significantly. It indicates the system has a practical application value for also beginners and intermediate and last expert, respectively. It also shows the effectiveness of combining DTW and SVM to support learners ‘body movement trajectory in a physical learning environment, opening them up to additional technology-assisted physical training applications. This provides implications for a more promising generation of future physical education involving the incorporation of complex AI technology.

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Section: Literature Reviewmentioning

confidence: 99%

Enhanced Design of a Tai Chi Teaching Assistance System Integrating DTW Algorithm and SVM

Guo

2024

ICST Transactions on Scalable Information Systems

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Exploring Dynamic Time Warping for Network Traffic Analysis

Raghavendra,

Gautam,

Jain

2024

2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)

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A Link-Quality Anomaly Detection Framework for Software-Defined Wireless Mesh Networks

Skaperas,

Mamatas,

Tsaoussidis

2024

Trans. Mach. Learn. Comm. Netw.

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Software-defined wireless mesh networks are being increasingly deployed in diverse settings, such as smart cities and public Wi-Fi access infrastructures. The signal propagation and interference issues that typically characterize these environments can be handled by employing SDN controller mechanisms, effectively monitoring link quality and triggering appropriate mitigation strategies, such as adjusting link and/or routing protocols. In this paper, we propose an unsupervised machine learning (ML) online -for link quality detection consisting of: (i) an improved preprocessing clustering algorithm, based on elastic similarity measures, to efficiently characterize wireless links in terms of reliability, and, (ii) a novel change point (CP) detector for the real-time identification of anomalies in the quality of selected links, which minimizes the overestimation error through the incorporation of a rank-based test and a recursive max-type procedure. In this sense, considering the communication constraints of such environments, our approach minimizes the detection overhead and the inaccurate decisions caused by overestimation. The proposed detector is validated, both on its individual components and as an overall mechanism, against synthetic but also real data traces; the latter being extracted from real wireless mesh network deployments.

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A DTW Approach for Complex Data A Case Study with Network Data Streams

Cited by 3 publications

References 9 publications

Enhanced Design of a Tai Chi Teaching Assistance System Integrating DTW Algorithm and SVM

Enhanced Design of a Tai Chi Teaching Assistance System Integrating DTW Algorithm and SVM

Exploring Dynamic Time Warping for Network Traffic Analysis

A Link-Quality Anomaly Detection Framework for Software-Defined Wireless Mesh Networks

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