A shuttlecock for the visually impaired

Dally, Claire; Haddow, Sara; Kershaw, Elizabeth

doi:10.1049/ee.1996.0011

Cited by 2 publications

(1 citation statement)

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“…It has a broad collective foundation, which is not only pleasing to the eyes but also popular with the audience. Through practice, the shuttlecock can improve a great level and has a unique charm [8]. We should dig, organize, inherit, and develop.…”

Section: Using Machine Learning Algorithms Tomentioning

confidence: 99%

Using Machine Learning Algorithms to Recognize Shuttlecock Movements

Wang

2021

Wireless Communications and Mobile Computing

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Shuttlecock is an excellent traditional national sport in China. Because of its simplicity, convenience, and fun, it is loved by the broad masses of people, especially teenagers and children. The development of shuttlecock sports into a confrontational event is not long, and it takes a period of research to master the tactics and strategies of shuttlecock sports. Based on this, this article proposes the use of machine learning algorithms to recognize the movement of shuttlecock movements, aiming to provide more theoretical and technical support for shuttlecock competitions by identifying features through actions with the assistance of technical algorithms. This paper uses literature research methods, model methods, comparative analysis methods, and other methods to deeply study the motion characteristics of shuttlecock motion, the key algorithms of machine learning algorithms, and other theories and construct the shuttlecock motion recognition based on multiview clustering algorithm. The model analyzes the robustness and accuracy of the machine learning algorithm and other algorithms, such as a variety of performance comparisons, and the results of the shuttlecock motion recognition image. For the key movements of shuttlecock movement, disk, stretch, hook, wipe, knock, and abduction, the algorithm proposed in this paper has a good movement recognition rate, which can reach 91.2%. Although several similar actions can be recognized well, the average recognition accuracy rate can exceed 75%, and even through continuous image capture, the number of occurrences of the action can be automatically analyzed, which is beneficial to athletes. And the coach can better analyze tactics and research strategies.

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Section: Using Machine Learning Algorithms Tomentioning

confidence: 99%