Application of Artificial Intelligence in Sports

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 July 2019) | Viewed by 5214

Special Issue Editor


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Guest Editor
Centre for Sport Science and University Sports, University of Vienna, Auf der Schmelz 6A, 1150 Wien, Austria
Interests: biomechanical research in sports; biomechanical modeling; human motion analysis; performance analysis; computer science in sport
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Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) commonly refers to the ability of computers or, more generally, of machines to perform tasks by imitating human intelligence. In order to do so, machines need to be able to learn, which may be accomplished by Machine Learning methods. Knowledge may thereby be generated from experience. In recent years, the integration of machine-aided intelligence into the design and implementation of innovative systems on the basis of state-of-the-art information and communication technologies has also become increasingly important for sports-related issues, often involving large date sets. There are a number of areas that AI now has an important role in and that makes use of Machine Learning methods. Fields of application include, among others, the analysis of sports performance, the assessment of injury risks, the provision of individual recommendations to athletes, the analysis of tactics in game sports, the prediction of performance development and competition outcomes, interactive communication with fans via chatbots, or the implementation of automated sports journalism. Modern sport information systems making use of wearables, GPS-devices, high-resolution cameras, and various sensors, for example, enable a prompt and automatic evaluation of sport-specific parameter values, thereby allowing the establishment of computer-based feedback and intervention routines. Local position measurement systems, as another example, are able to track players in game sports in almost real time and provide insights into physical loads and in tactical behavior, such as player creativity. The purpose of this Special Issue is to highlight the potential of the application of Artificial Intelligence in sports-related areas. Original papers may focus on various application scenarios, such as sports performance analysis, training and coaching, talent diagnosis, team composition, injury and fatigue prevention, intelligent sports equipment, computer vision in sports, sports data mining, sports journalism, marketing and sports betting.

Prof. Dr. Arnold Baca
Guest Editor

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Published Papers (1 paper)

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Research

19 pages, 977 KiB  
Article
Asymmetric Residual Neural Network for Accurate Human Activity Recognition
by Jun Long, Wuqing Sun, Zhan Yang and Osolo Ian Raymond
Information 2019, 10(6), 203; https://doi.org/10.3390/info10060203 - 6 Jun 2019
Cited by 29 | Viewed by 4309
Abstract
Human activity recognition (HAR) using deep neural networks has become a hot topic in human–computer interaction. Machines can effectively identify human naturalistic activities by learning from a large collection of sensor data. Activity recognition is not only an interesting research problem but also [...] Read more.
Human activity recognition (HAR) using deep neural networks has become a hot topic in human–computer interaction. Machines can effectively identify human naturalistic activities by learning from a large collection of sensor data. Activity recognition is not only an interesting research problem but also has many real-world practical applications. Based on the success of residual networks in achieving a high level of aesthetic representation of automatic learning, we propose a novel asymmetric residual network, named ARN. ARN is implemented using two identical path frameworks consisting of (1) a short time window, which is used to capture spatial features, and (2) a long time window, which is used to capture fine temporal features. The long time window path can be made very lightweight by reducing its channel capacity, while still being able to learn useful temporal representations for activity recognition. In this paper, we mainly focus on proposing a new model to improve the accuracy of HAR. In order to demonstrate the effectiveness of the ARN model, we carried out extensive experiments on benchmark datasets (i.e., OPPORTUNITY, UniMiB-SHAR) and compared the results with some conventional and state-of-the-art learning-based methods. We discuss the influence of networks parameters on performance to provide insights about its optimization. Results from our experiments show that ARN is effective in recognizing human activities via wearable datasets. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Sports)
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