Application of wearable inertial sensor in optimization of basketball player’s human motion tracking method

Ren, Hailong; Wang, Xiao

doi:10.1007/s12652-021-03110-2

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…Hal ini menyebabkan pelatih kesulitan dalam menganalisa performa para pemain selama latihan sebab data hasil pencatatan itu masih merupakan data mentah sedangkan pelatih membutuhkan data statistika untuk melihat perkembangan performa pemain [11], [12]. Dalam permainan bola basket, keterampilan para pemain memiliki dampak yang lebih besar pada seluruh tim [13]. Mencatat dan mengukur kinerja pemain dalam latihan dan pertandingan merupakan hal mendasar karena pelatih perlu mengevaluasi pemain sehubungan dengan kemampuan untuk berperan secara maksimal dalam permainan [14].…”

Section: Pendahuluanunclassified

Penerapan User Centered Design Pada Desain Interaksi dan Arsitektur Informasi Prototype Aplikasi Asisten BasketMu

Satya,

Sasongko,

Nugroho

et al. 2023

josh

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Coaches have a very important role in the achievements of sports athletes. The coach has the responsibility to organize training methods that suit the athlete's needs and goals. Coaches generally record the performance of team members during training. Recording athlete performance can be used to measure athlete development and improve athlete abilities. Based on the results of interviews and observations with several basketball coaches, they still record on paper forms. The main contribution of this research is to produce an application design that has an information architecture and interaction model to help improve the coach's user experience in recording and monitoring anthropometric developments, physical development, and athlete skill development during training and recording player statistics during matches. Apart from recording the resulting application, it is hoped that it can improve the user experience of coaches and players in analyzing performance data during training and matches. This research uses the User Centered Design method. This research has produced a prototype in high fidelity form. Based on functionality testing using the black box testing method, it shows that the prototype feature can be successful as expected. The User Experience test results show that the average Task Success value from testing the player's user interface is 77%, while the average Task Success value from testing the trainer's user interface is 82%.

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Section: Pendahuluanunclassified

Penerapan User Centered Design Pada Desain Interaksi dan Arsitektur Informasi Prototype Aplikasi Asisten BasketMu

Satya,

Sasongko,

Nugroho

et al. 2023

josh

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show abstract

“…Xiangyang Cai, Zhengzhou University of Industry Technology, China* Basketball posture recognition (Fan et al 2021;Ren et al 2021) is a kind of human posture recognition. At present, there are two main methods of human posture recognition: posture recognition based on inertial sensor (Wang et al 2019;Zhang et al 2018) and posture recognition based on image acquisition (Chevtchenko et al 2018;Oudah et al 2020).…”

Section: Wsn-driven Posture Recognition and Correction Towards Basket...mentioning

confidence: 99%

WSN-Driven Posture Recognition and Correction Towards Basketball Exercise

Cheng

2022

International Journal of Information System Modeling and Design

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In order to enhance the daily training for basketball sport, this paper establishes a human posture estimation framework by using monocular camera and wireless sensor network. First, the daily basketball training images are collected by monocular camera and transmitted through wireless sensor network. Second, the collected images are processed by an observation and reasoning model which is based on component and graph reasoning. The basketball player’s posture is depicted by the rotation invariant features of edge field. The extracted features are used to learn a boosting classifier as the observation model. The experimental results show that the posture recognition rate can achieve more than 88% for basketball player’s action.

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“…ere are many common classification algorithms, and four are listed below for detailed introduction [15].…”

Section: Classification Algorithmmentioning

confidence: 99%

A Basketball Training Posture Monitoring Algorithm Based on Machine Learning and Artificial Intelligence

Hou

Lian

et al. 2022

Mobile Information Systems

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To increase the widespread attention of human gesture recognition technology, this paper proposes a basketball pose recognition method based on unit action division. Initially, the human gesture recognition algorithm is introduced for the verification of various effects and gestures of basketball players by monitoring various actions of basketball and to obtain the data of limbs using different detectors for different basketball movements. A large amount of data collection work was carried out for the experiment, and the corresponding experimental scenarios were described in the experimental design for testing. The methods for data processing and data division presented in this work are used to process the collected data. A feature vector set that describes a particular action is acquired and used as a sample set. The sample set is then delivered to the classifier. The classifier is implemented here based on the already-existing Weka platform, and performance evaluation and analysis of various classifiers are implemented. The results show that the differential limb function category has a better recognition effect on BP. The average accuracy of upper limb function was 92.19%, the average recall rate was 92.19%, and the accuracy of lower limb was much higher. The average accuracy of the four algorithms was within the range of 96.99% to 99.19% for lower limb movements and 84.89% to 92.19% for upper limb movements. The BP prosthetic network is used to create separate classifiers, ensuring that each basketball move was more than 95% accurate and that the average accuracy per basketball move was much more accurate. As a result, the accuracy level reached up to 98.85%. The validity of the basketball gesture recognition method recognized by the authors is sufficient and reasonable.

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Application of wearable inertial sensor in optimization of basketball player’s human motion tracking method

Cited by 5 publications

References 16 publications

Penerapan User Centered Design Pada Desain Interaksi dan Arsitektur Informasi Prototype Aplikasi Asisten BasketMu

Penerapan User Centered Design Pada Desain Interaksi dan Arsitektur Informasi Prototype Aplikasi Asisten BasketMu

WSN-Driven Posture Recognition and Correction Towards Basketball Exercise

A Basketball Training Posture Monitoring Algorithm Based on Machine Learning and Artificial Intelligence

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