Scholarlink

<p>In sports, intelligent multimedia models deployed through edge–cloud networks are widely used to build Internet of Things (IoT) capabilities. In amateur sports, however, the rapid use of personal devices, cameras, and sensors has made implementation problems increasingly significant, particularly for athlete health monitoring and basic sports training. In swimming training and amateur workouts, there is a growing need for unsupervised findings with tiny artificial intelligence (TinyAI) algorithms that can enhance image quality and perform content classification, enabling trainers to quickly and effectively provide on-site feedback to swimmers. In this paper, we propose a novel algorithm capable of improving the quality of rescaled images while preserving visual edges in swimming pool environments under various conditions, such as wavy water surfaces. The proposed method is lightweight and can be rapidly deployed on commercial sports IoT devices at the network edge. Experimental comparisons with existing approaches demonstrate that our method outperforms state-of-the-art techniques in terms of both quantitative performance and visual edge preservation.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Adaptive rescaling technique for portable vision devices in IoMT toward swimming workouts training and safety

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月30日

查看全文
  • Xiaorui Song,
  • Mingling Shang,
  • Reza Khosravi

摘要

In sports, intelligent multimedia models deployed through edge–cloud networks are widely used to build Internet of Things (IoT) capabilities. In amateur sports, however, the rapid use of personal devices, cameras, and sensors has made implementation problems increasingly significant, particularly for athlete health monitoring and basic sports training. In swimming training and amateur workouts, there is a growing need for unsupervised findings with tiny artificial intelligence (TinyAI) algorithms that can enhance image quality and perform content classification, enabling trainers to quickly and effectively provide on-site feedback to swimmers. In this paper, we propose a novel algorithm capable of improving the quality of rescaled images while preserving visual edges in swimming pool environments under various conditions, such as wavy water surfaces. The proposed method is lightweight and can be rapidly deployed on commercial sports IoT devices at the network edge. Experimental comparisons with existing approaches demonstrate that our method outperforms state-of-the-art techniques in terms of both quantitative performance and visual edge preservation.