使用数据挖掘算法的休闲跑步者智能损伤康复指导系统

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Algorithms Pub Date : 2023-11-15 DOI:10.3390/a16110523
Theodoros Tzelepis, George Matlis, Nikos Dimokas, Petros Karvelis, P. Malliou, A. Beneka
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引用次数: 0

摘要

近年来,每天锻炼的人数急剧增加。更确切地说,由于 COVID 时期的到来,许多人成为了休闲跑步者。休闲跑步是任何年龄段的人保持活跃和健康的常规方式。此外,跑步也是一种广受欢迎的体育锻炼,对健康有诸多益处。然而,据休闲跑步者报告,因跑步而导致肌肉骨骼损伤的发生率很高。由于电子系统、互联网和电信的快速增长和发展,医疗保健行业不得不使用信息技术。我们提出的智能系统采用数据挖掘算法,为患有肌肉骨骼不适的休闲跑步者提供康复指导。该系统根据已建立的调查问卷,按照严重性、刺激性、性质、阶段和稳定性模型对休闲跑步者进行分类,并建议他们遵循适当的治疗计划/运动。通过对各种案例研究的严格测试,我们的方法取得了非常有前景的结果,凸显了其对面临肌肉骨骼挑战的休闲跑步者的健康和康复做出重大贡献的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Intelligent Injury Rehabilitation Guidance System for Recreational Runners Using Data Mining Algorithms
In recent years the number of people who exercise every day has increased dramatically. More precisely, due to COVID period many people have become recreational runners. Recreational running is a regular way to keep active and healthy at any age. Additionally, running is a popular physical exercise that offers numerous health advantages. However, recreational runners report a high incidence of musculoskeletal injuries due to running. The healthcare industry has been compelled to use information technology due to the quick rate of growth and developments in electronic systems, the internet, and telecommunications. Our proposed intelligent system uses data mining algorithms for the rehabilitation guidance of recreational runners with musculoskeletal discomfort. The system classifies recreational runners based on a questionnaire that has been built according to the severity, irritability, nature, stage, and stability model and advise them on the appropriate treatment plan/exercises to follow. Through rigorous testing across various case studies, our method has yielded highly promising results, underscoring its potential to significantly contribute to the well-being and rehabilitation of recreational runners facing musculoskeletal challenges.
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来源期刊
Algorithms
Algorithms Mathematics-Numerical Analysis
CiteScore
4.10
自引率
4.30%
发文量
394
审稿时长
11 weeks
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