scholarly journals Application Algorithms for Basketball Training Based on Big Data and Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Bo Li ◽  
Xiaofeng Wang ◽  
Jinting Yao
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Background Information ◽  
Basketball Player ◽  
Sports Training ◽  
Basketball Players ◽  
Recognition Mechanism ◽  
Sports Industry ◽  
Traditional Sports ◽  
Increasing Demand

In recent years, the increasing demand for physical health promotes the basketball sports industry’s reform. The latest science and technology enter the sports industry one after another and constantly impact the traditional sports equipment. However, the conventional sports technology scheme is unreasonable, and the sports training management models are obsolete. So, it is impossible to use scientific methods in basketball training, and the overall training effect is not good. This paper proposes a basketball training algorithm using big data and the Internet of Things. The proposed algorithm uses gesture recognition from continuous video movement characteristics of key figures, trajectory characteristics, and background information fusion. It improves the recognition mechanism based on the Shuangliu C3D video basketball player action classification method. Due to the lack of a scientific exercise plan for basketball players, a training plan based on BMI and IoT-enabled big data is devised. The proposed scheme is implemented so that different basketball players customize their own scientific sports training modules.

scholarly journals Feasibility Data of Traditional Sports Industrialization Development and Informatization Development Based on Big Data

2021 ◽  
Vol 292 ◽  
pp. 03037
Author(s):  
Sun Jiaji ◽  
Li Yuezhong
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Field Investigation ◽  
Living Standard ◽  
Sports Industry ◽  
Continuous Innovation ◽  
Existing Problems ◽  
Sports Events ◽  
Depth Analysis ◽  
Traditional Sports

Since the reform and opening up, China’s economy has been developing rapidly and the material living standard has been improving continuously. People begin to pay more attention to the mental and physical health. Therefore, more and more people take part in a variety of sports activities to exercise their body and watch large-scale sports events to cultivate the sports spirit. These changes have boosted the development of China’s sports industry, which is reflected in the continuous expansion of the scale of the sports industry, the deepening of the degree of industrial segmentation and the continuous innovation of the development concept. This paper mainly studies the feasibility data analysis of traditional sports industrialization development and informatization development based on big data. In this paper, by using the research methods of literature, observation, field investigation and logical analysis, the industrialization development of traditional sports is deeply studied and systematically combed. From the perspective of informationization, this paper makes an in-depth analysis of the current situation, existing problems, the importance of industrialization, and the advantages of industrialization of traditional sports, and makes a detailed exploration and elaboration of the opportunities brought by informatization to traditional sports industry and the development strategies of the industrialization of traditional sports in the future.

The Training of Highly Skilled Basketball Players and its Evaluation: The Context of a Microstructure

2016 ◽  
Vol 7 (1) ◽  
pp. 58-73
Author(s):  
Stanislovas Norkus ◽  
Arūnas Grabauskas
Keyword(s):  
Average Duration ◽  
Work Load ◽  
Basketball Player ◽  
Initial Part ◽  
Sports Training ◽  
Basketball Players ◽  
Highly Skilled ◽  
Major Criterion ◽  
Load Intensity ◽  
Individual Training

Abstract The search for methods of optimisation of the training of highly skilled basketball players is very relevant due to an increased volume of competition activities. It is important for trainers to properly distribute training loads throughout micro-cycles and, thus, strive for more rapid regeneration, accumulation of physical and psychic efficiency of athletes. Appropriate distribution of training loads in a micro-cycle is related to reception of athletes’ feedback, too.Another highly relevant problem deals with athletes’ endeavours to develop their capacities through individual training. An athlete should be able to assess efficiency of this activity, too. The research objective is to investigate the change of highly skilled basketball players’ training loads (training conducted by trainers, additional individual training and competition activities) and the evaluation of them throughout training micro-cycles. The research methods of theoretical analysis and generalisation, observation, evaluation, mathematical statistics are applied. A fragment of an initial part of the basketball players’ training and competitions period (20 initial micro-cycles of the season 2014 – 2015) of “Šiauliai” basketball team was observed and analysed. An experienced basketball player evaluated the load undergone by the team as well as his own individual training load. Optimal sports training should be based on analysis of three activities: the training conducted by trainers, additional individual training of an athlete, athletes’ competition activities. An average duration of a sports training micro-cycle is 88.6min. The training conducted by trainers constitutes 73.1%, athlete’s additional individual training constitutes 6.0%, and the competing constitutes 20.9% of the total sports training. A major criterion of the tasks being solved throughout micro-cycles and their filling with the content are matches and the amount of them in one micro-cycle. An average evaluation of the volume of the training conducted by trainers throughout a cycle was 6.2±0.70 points; evaluation of intensity was 7.0±0.51 points. The change of evaluation of the work load, intensity throughout the micro-cycles was similar. Highly skilled basketball players do not pay appropriate attention to additional individual activities.

scholarly journals The Construction of WTO Framework for the Development of My Country’s Information Technology Sports Industry Based on Big Data

2021 ◽  
Vol 292 ◽  
pp. 03039
Author(s):  
Li Yuezhong ◽  
Liu Yichong
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Culture Industry ◽  
Television Industry ◽  
Development Status ◽  
Sports Industry ◽  
Sports Culture ◽  
Film And Television ◽  
Economic Level ◽  
Traditional Sports

With the improvement of China’s economic level and the level of science and technology, people’s demand for sports products and services is greatly increasing, and China’s sports industry is showing a trend of diversified development. This article discusses the characteristics and connotations of the traditional sports industry and the characteristics of the sports industry in the era of big data, and then points out the challenges faced by the sports industry under the WTO framework and makes relevant recommendations. This paper also investigates the development status of the sports culture industry in our city through experiments, and the results show that the sports culture industry in our city involves 12 categories, of which sports film and television industry accounts for only 14.3%.

scholarly journals WearableDL: Wearable Internet-of-Things and Deep Learning for Big Data Analytics—Concept, Literature, and Future

2018 ◽  
Vol 2018 ◽  
pp. 1-20 ◽  
Author(s):  
Aras R. Dargazany ◽  
Paolo Stegagno ◽  
Kunal Mankodiya
Keyword(s):  
Nervous System ◽  
Big Data ◽  
Deep Learning ◽  
Internet Of Things ◽  
Data Analytics ◽  
Fog Computing ◽  
Big Data Analytics ◽  
Background Information ◽  
Wearable Technologies ◽  
The Brain

This work introduces Wearable deep learning (WearableDL) that is a unifying conceptual architecture inspired by the human nervous system, offering the convergence of deep learning (DL), Internet-of-things (IoT), and wearable technologies (WT) as follows: (1) the brain, the core of the central nervous system, represents deep learning for cloud computing and big data processing. (2) The spinal cord (a part of CNS connected to the brain) represents Internet-of-things for fog computing and big data flow/transfer. (3) Peripheral sensory and motor nerves (components of the peripheral nervous system (PNS)) represent wearable technologies as edge devices for big data collection. In recent times, wearable IoT devices have enabled the streaming of big data from smart wearables (e.g., smartphones, smartwatches, smart clothings, and personalized gadgets) to the cloud servers. Now, the ultimate challenges are (1) how to analyze the collected wearable big data without any background information and also without any labels representing the underlying activity; and (2) how to recognize the spatial/temporal patterns in this unstructured big data for helping end-users in decision making process, e.g., medical diagnosis, rehabilitation efficiency, and/or sports performance. Deep learning (DL) has recently gained popularity due to its ability to (1) scale to the big data size (scalability); (2) learn the feature engineering by itself (no manual feature extraction or hand-crafted features) in an end-to-end fashion; and (3) offer accuracy or precision in learning raw unlabeled/labeled (unsupervised/supervised) data. In order to understand the current state-of-the-art, we systematically reviewed over 100 similar and recently published scientific works on the development of DL approaches for wearable and person-centered technologies. The review supports and strengthens the proposed bioinspired architecture of WearableDL. This article eventually develops an outlook and provides insightful suggestions for WearableDL and its application in the field of big data analytics.

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