Data Collection and Analysis of Track and Field Athletes’ Behavior Based on Edge Computing and Reinforcement Learning

Han, Di

doi:10.1155/2021/9981767

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…Therefore, a comparative analysis was carried out between deep learning modeling and traditional learning models with supervised learning or classification types. Several previous studies used traditional machine learning in modeling, including support vector machine [41], k-nearest neighbor [42], and naïve Bayes classifier [43]. The three models are implemented in the case of a model built with a modeling process in several stages, including (1) preparing the same dataset used in deep learning modeling, the fish pond parameter dataset, consisting of four features divided into three feature parameters (temperature, pH, and turbidity), class features consisting of "normal" and "not normal" labels, and data of 1000 records consisting of 500 "normal" labels and 500 "not normal" labels; (2) data transformation process by converting "normal" labels with 0 and "not normal" with 1; (3) model implementation with 80% train data and 20% test data; (4) evaluation process through the accuracy parameters of each model.…”

Section: Comparison Of Deep Learning and Traditional Learning Modelmentioning

confidence: 99%

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Designing Aquaculture Monitoring System Based on Data Fusion through Deep Reinforcement Learning (DRL)

2023

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The aquaculture production sector is one of the suppliers of global food consumption needs. Countries that have a large amount of water contribute to the needs of aquaculture production, especially the freshwater fisheries sector. Indonesia is a country that has a large number of large bodies of water and is the top-five producer of aquaculture production. Technology and engineering continue to be developed to improve the quality and quantity of aquaculture production. One aspect that can be observed is how the condition of fish pond water is healthy and supports fish growth. Various studies have been conducted related to the aquaculture monitoring system, but the problem is how effective it is in terms of accuracy of the resulting output, implementation, and costs. In this research, data fusion (DF) and deep reinforcement learning (DRL) were implemented in an aquaculture monitoring system with temperature, turbidity, and pH parameters to produce valid and accurate output. The stage begins with testing sensor accuracy as part of sensor quality validation, then integrating sensors with wireless sensor networks (WSNs) so they can be accessed in real time. The implemented DF is divided into three layers: first, the signal layer consists of WSNs and their components. Second, the feature layer consists of DRL combined with deep learning (DL). Third, the decision layer determines the output of the condition of the fish pond in “normal” or “not normal” conditions. The analysis and testing of this system look at several factors, i.e., (1) the accuracy of the performance of the sensors used; (2) the performance of the models implemented; (3) the comparison of DF-DRL-based systems with rule-based algorithm systems; and (4) the cost effectiveness compared to labor costs. Of these four factors, the DF-DRL-based aquaculture monitoring system has a higher percentage value and is a low-cost alternative for an accurate aquaculture monitoring system.

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Section: Comparison Of Deep Learning and Traditional Learning Modelmentioning

confidence: 99%

“…Study I (Support Vector Machine) [41] 95.75% Study II (k-Nearest Neighbor) [42] 92.15% Study III (Naïve Bayes Classifier) [43] 95.02% Average Accuracy 94.31%…”

Section: Model Accuracy Percentagementioning

confidence: 99%

Designing Aquaculture Monitoring System Based on Data Fusion through Deep Reinforcement Learning (DRL)

2023

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“…While strides are being made in traditional broadcasting to align with new media trends, such as personalized short-form video content for effective communication with younger audiences, events with lower budgets, such as youth races or non-televised sports, still lag in this domain. In these cases, interested parties, like parents, fans, and family, often lack access to real-time performance updates, relying instead on others to convey information, despite the availability of tracking technology [3][4][5][6]. Participants may desire action shots for social media, but the current reliance on roadside photographers or spectators with mobile phones poses limitations.…”

Section: Introductionmentioning

confidence: 99%

Fully Automatic Camera for Personalized Highlight Generation in Sporting Events

Decorte,

De Bock,

Taelman

et al. 2024

Sensors

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Personally curated content in short-form video formats provides added value for participants and spectators but is often disregarded in lower-level events because it is too labor-intensive to create or is not recorded at all. Our smart sensor-driven tripod focuses on supplying a unified sensor and video solution to capture personalized highlights for participants in various sporting events with low computational and hardware costs. The relevant parts of the video for each participant are automatically determined by using the timestamps of his/her received sensor data. This is achieved through a customizable clipping mechanism that processes and optimizes both video and sensor data. The clipping mechanism is driven by sensing nearby signals of Adaptive Network Topology (ANT+) capable devices worn by the athletes that provide both locality information and identification. The device was deployed and tested in an amateur-level cycling race in which it provided clips with a detection rate of 92.9%. The associated sensor data were used to automatically extract peloton passages and report riders’ positions on the course, as well as which participants were grouped together. Insights derived from sensor signals can be processed and published in real time, and an upload optimization scheme is proposed that can provide video clips for each rider a maximum of 5 min after the passage if video upload is enabled.

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“…With such a massive workload, they may not meet the real-time constraint due to limited computational resources [5]. Two other case studies that also face similar problems are the monitoring system of consumer behavior in supermarkets [6] monitoring and analysis systems of sports athletes [7].…”

Section: Introductionmentioning

confidence: 99%

System performance and optimization in NOMA mobile edge computing surveillance network using GA and PSO

Truong

Nayyar

2023

Computer Networks

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Data Collection and Analysis of Track and Field Athletes’ Behavior Based on Edge Computing and Reinforcement Learning

Cited by 5 publications

References 16 publications

Designing Aquaculture Monitoring System Based on Data Fusion through Deep Reinforcement Learning (DRL)

Designing Aquaculture Monitoring System Based on Data Fusion through Deep Reinforcement Learning (DRL)

Fully Automatic Camera for Personalized Highlight Generation in Sporting Events

System performance and optimization in NOMA mobile edge computing surveillance network using GA and PSO

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