Study on Real World Applications of SVM

Kansal, Vipashi; Singhdev, Harsiddhi; Pant, Bhaskar

doi:10.1007/978-981-16-6893-7_60

Cited by 3 publications

(1 citation statement)

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“…The DTW algorithm is suitable for the analysis of the flows and movements that are characteristic of Tai Chi since it is adapted to time series data. The second component, SVM, serves classification functions that help determine the quality of movements by practitioners and set specific goals for improvement [2], [3]. The integration of DTW to provide accurate comparisons with standard signals and SVM to promote learning through classification ensures a superior and more personalized experience than with existing systems.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%