Preliminary Investigation of an Innovative Digital Motion Analysis Device for Badminton Athlete Performance Evaluation

Taha, Zahari; Hassan, Mohammad S.; Yap, Hwa Jen; Yeo, Wee Kian

doi:10.1016/j.proeng.2016.06.341

Cited by 15 publications

(8 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Relevant scholars pointed out that the fundamental frequency of human walking is generally around 2 Hz, while running is about 2.5-3 Hz [19]. In order to obtain more details from human motion, researchers often set the sampling frequency of the sensor to tens to hundreds of Hz, making the sampling rate much higher than the Nyquist sampling frequency [20]. Regarding the issue of the number of sensors, in early studies, researchers tended to place multiple sensors on volunteers to better capture motion details.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Image Recognition of Badminton Swing Motion Based on Single Inertial Sensor

Chu

2021

Journal of Sensors

View full text Add to dashboard Cite

This article analyzes the method of reading data from inertial sensors. We introduce how to create a 3D scene and a 3D human body model and use inertial sensors to drive the 3D human body model. We capture the movement of the lower limbs of the human body when a small number of inertial sensor nodes are used. This paper introduces the idea of residual error into the deep LSTM network to solve the problem of gradient disappearance and gradient explosion. The main problem to be solved by wearable inertial sensor continuous human motion recognition is the modeling of time series. This paper chooses the LSTM network which can handle time series as well as the main frame. In order to reduce the gradient disappearance and gradient explosion problems in the deep LSTM network, the structure of the deep LSTM network is adjusted based on the residual learning idea. In this paper, a data acquisition method using a single inertial sensor fixed on the bottom of a badminton racket is proposed, and a window segmentation method based on the combination of sliding window and action window in real-time motion data stream is proposed. We performed feature extraction on the intercepted motion data and performed dimensionality reduction. An improved Deep Residual LSTM model is designed to identify six common swing movements. The first-level recognition algorithm uses the C4.5 decision tree algorithm to recognize the athlete’s gripping style, and the second-level recognition algorithm uses the random forest algorithm to recognize the swing movement. Simulation experiments confirmed that the proposed improved Deep Residual LSTM algorithm has an accuracy of over 90.0% for the recognition of six common swing movements.

show abstract

Section: Related Workmentioning

confidence: 99%

[Retracted] Image Recognition of Badminton Swing Motion Based on Single Inertial Sensor

Chu

2021

Journal of Sensors

View full text Add to dashboard Cite

show abstract

“…The use of IMUs on the Non-Dominant Wrist and Dominant Wrist had significant positive responses. Wrist-worn IMUs have been found to be a reliable and valid method for stroke recognition and the assessment of movement within a controlled setting with Badminton (Shan, Sen, Fai, & Ming, 2015;Rusydi et al, 2015;Taha et al, 2016;Wang et al, 2016;Anand et al, 2017), Table Tennis (Guo et al, 2010) and Tennis (Ahmadi et al, 2009;Kos et al, 2016;Anand et al 2017;Whiteside et al, 2017). As wearable IMUs become smaller and less intrusive, and given the coaches' support for the use of IMUs in this position, it is likely that the use of IMUs on the wrist can become part of regular monitoring in racket sports.…”

Section: Discussionmentioning

confidence: 99%

Placement of inertial measurement units in Racket Sports: Perceptions of coaches for IMU use during training and competition.

Wylde

Masismadi

Yong³

et al. 2021

International Journal of Racket Sports Science

View full text Add to dashboard Cite

show abstract

“…(21). Sonuçlar seçilen spor programı tamamlanınca hareketlerin doğru yapılma yüzdesi ile birlikte ortalama olarak ekranda gösterilir ve veri tabanına ilgili katılımcı performansı olarak kayıt edilir.…”

Section: Spor Bilimlerinde Teknoloji Kullanımıunclassified

Motion Capture Technology in Sports Science: 3D Virtual Sports Platform with Kinect

2017

View full text Add to dashboard Cite

Regular sports activity is considered to be beneficial for life quality and health. On the other hand, regular sporting opportunities are not widespread, especially in developing countries. It may be possible to provide an alternative solution for physical activity in the control of a virtual instructor, without being physically in the same place, thanks to the facilities provided by technologies such as Internet and Kinect. In this respect, the three dimensional virtual sports platform Kinect was designed and developed. The different features of the platform are described, and the advantages of new technology achievement and possibilities of alternative sports practice are given. Key Words: Exercise, virtual platform, technology, internet ÖZDüzenli spor yapmanın yaşam ve sağlık kalitesi için yararlı olduğu kabul edilmektedir. Fakat düzenli spor yapma olanaklarının özellikle gelişmekte olan ülkelerde yeterince yaygınlaşmadığı gözlenmektedir. İnternet ve Kinect gibi güncel ve gelişmiş teknolojilerin sağladığı olanaklar ile, spor yapamayanlara fiziksel olarak aynı ortamda bulunmaksızın, sanal eğitmen kontrolünde spor yapabilme olanağı sunmanın alternatif bir çözüm yaratabileceği düşünülmektedir. Bu doğrultudaki çalışmalarla, Kinect üç boyutlu sanal spor platformu tasarlanıp geliştirilmiştir. Geliştirilen platformun özellikleri, yeni teknolojilerin avantajlarının kullanıldığı platformlar ile alternatif spor yapma olanakları gözden geçirilmektedir.

show abstract

Preliminary Investigation of an Innovative Digital Motion Analysis Device for Badminton Athlete Performance Evaluation

Cited by 15 publications

References 22 publications

[Retracted] Image Recognition of Badminton Swing Motion Based on Single Inertial Sensor

[Retracted] Image Recognition of Badminton Swing Motion Based on Single Inertial Sensor

Placement of inertial measurement units in Racket Sports: Perceptions of coaches for IMU use during training and competition.

Motion Capture Technology in Sports Science: 3D Virtual Sports Platform with Kinect

Contact Info

Product

Resources

About