Skill grouping method: Mining and clustering skill differences from body movement BigData

Yamagiwa, Shinichi; Kawahara, Yoshinobu; Tabuchi, Noriyuki; Watanabe, Yoshinobu; Naruo, Takeshi

doi:10.1109/bigdata.2015.7364049

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…We are not able to find absolute maximums from a small dataset due to the large diversity of human motor skills. Therefore, in [ 8 ], the authors define the distance of skills between novices and experts as the differences of movement data patterns. They apply a machine learning approach that drives the skill distances of the data patterns captured from movements.…”

Section: Backgrounds and Definitionsmentioning

confidence: 99%

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Statistical Extraction Method for Revealing Key Factors from Posture before Initiating Successful Throwing Technique in Judo

Kato

Yamagiwa

2021

Sensors

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Many methods such as biomechanics and coaching have been proposed to help people learn a certain movement. There have been proposals for methods to discover characteristics of movement based on information obtained from videos and sensors. Especially in sports, it is expected that these methods can provide hints to improve movement skills. However, conventional methods focus on individual movements, and do not consider cases where external factors influence the movement, such as combat sports. In this paper, we propose a novel method called the Extraction for Successful Movement method (XSM method). Applying the method, this paper focuses on throwing techniques in judo to discover key factors that induce successful throwing from the postures right before initiating the throwing techniques. We define candidate factors by observing the video scenes where the throwing techniques are successfully performed. The method demonstrates the significance of the key factors according to the predominance of factors by χ2 test and residual analysis. Applying the XSM method to the dataset obtained from the videos of the Judo World Championships, we demonstrate the validity of the method with discussing the key factors related to the successful throwing techniques.

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Section: Backgrounds and Definitionsmentioning

confidence: 99%

“…The method statistically compares between performances of advanced athletes and novices, and defines equations for the models for comparing numerically the target movements. We can also find a method with a machine learning approach [ 8 ]. It exploits key points of a target movement between advanced athletes and novices.…”

Section: Introductionmentioning

confidence: 99%

Statistical Extraction Method for Revealing Key Factors from Posture before Initiating Successful Throwing Technique in Judo

Kato

Yamagiwa

2021

Sensors

Self Cite

View full text Add to dashboard Cite

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“…Subsequently, can without much of a stretch record the human action as the body development Big-Data and dissect it to mission ability to turn into a specialist of an objective body drive. [3] Big-data analytics as a new significant field of education for both investigators and doctors, representative the important request for explanations to commercial problems in a data-driven knowledge-based economy. Additionally, experiential studies examining the influences of the budding technology on organizational presentation, particularly the effect of organizational ethos on the five Big V s of big data, continue rare.…”

Section: Traitmentioning

confidence: 99%

Efficient Implementation of Big Data Access Control Scheme with Privacy-Preserving Policy

M.¹,

Jain²,

Nigam³

et al. 2019

IJITEE

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In the time of big data, cloud computing, an immense measure of information can be created rapidly from different IT, non-IT related sources. Towards these big data, cloud computing, customary PC frameworks are not up to required skilled to store and process this information. Due to the adaptable and flexible figuring assets, distributed computing is a characteristic fit for putting away and preparing big data. With cloud computing, end-clients store their information into the cloud server and depend on the advanced cloud server to share their information to different clients. To share end-client's information to just approved clients, it is important to configuration access control systems as indicated by the prerequisites of end clients. When re-appropriating information into the cloud, end-clients free the physical control, virtual physical control of their information. In addition, cloud specialist co-ops are not completely trusted by end-clients, which make the entrance control additionally testing. on the off chance that the conventional access control systems (e.g., Access Control Lists) are connected, the cloud server turns into the judge to assess the entrance approach and settle on access choice. Subsequently, end-clients may stress that the cloud server may settle on wrong access choices purposefully or accidentally and uncover their information to some unapproved clients. To empower end-clients to control the entrance of their own information, a proficient and fine-grained huge information access control plot with protection saving strategy is proposed. In particular, the entire trait (as opposed to just its qualities) in the entrance strategies are scrambled. To help information decoding, encoding, a novel Attribute Bloom Filter is utilized [14][16] to assess whether a characteristic is in the entrance arrangement and find the accurate position in the entrance approach on the off chance that it is in the entrance strategy. Just the clients whose traits fulfill the entrance arrangement are qualified to unscramble the information.

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A Normalized Mutual Information Estimator Compensating Variance Fluctuations for Motion Detection

Qin

Sun

Zhou

et al. 2018

Proceedings in Adaptation, Learning and Optimization

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Skill grouping method: Mining and clustering skill differences from body movement BigData

Cited by 6 publications

References 26 publications

Statistical Extraction Method for Revealing Key Factors from Posture before Initiating Successful Throwing Technique in Judo

Statistical Extraction Method for Revealing Key Factors from Posture before Initiating Successful Throwing Technique in Judo

Efficient Implementation of Big Data Access Control Scheme with Privacy-Preserving Policy

A Normalized Mutual Information Estimator Compensating Variance Fluctuations for Motion Detection

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