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本体知识引擎与健康筛查数据助力实现无处不在的个性化体能训练(UFIT)。

Ontological knowledge engine and health screening data enabled ubiquitous personalized physical fitness (UFIT).

作者信息

Su Chuan-Jun, Chiang Chang-Yu, Chih Meng-Chun

机构信息

Department of Industrial Engineering & Management, Yuan Ze University, No.135, Yuandong Rd., Zhongli City, Taoyuan County 320, Taiwan.

Project Management, Shun On Electronic Co. Ltd, 6F., No.22, Taiyuan St., Zhubei City, Hsinchu County 302, Taiwan.

出版信息

Sensors (Basel). 2014 Mar 7;14(3):4560-84. doi: 10.3390/s140304560.

Abstract

Good physical fitness generally makes the body less prone to common diseases. A personalized exercise plan that promotes a balanced approach to fitness helps promotes fitness, while inappropriate forms of exercise can have adverse consequences for health. This paper aims to develop an ontology-driven knowledge-based system for generating custom-designed exercise plans based on a user's profile and health status, incorporating international standard Health Level Seven International (HL7) data on physical fitness and health screening. The generated plan exposing Representational State Transfer (REST) style web services which can be accessed from any Internet-enabled device and deployed in cloud computing environments. To ensure the practicality of the generated exercise plans, encapsulated knowledge used as a basis for inference in the system is acquired from domain experts. The proposed Ubiquitous Exercise Plan Generation for Personalized Physical Fitness (UFIT) will not only improve health-related fitness through generating personalized exercise plans, but also aid users in avoiding inappropriate work outs.

摘要

良好的身体素质通常会使身体更不容易患上常见疾病。一个促进健身平衡方法的个性化运动计划有助于提升健康水平,而不恰当的运动形式可能会对健康产生不利影响。本文旨在开发一个本体驱动的基于知识的系统,该系统根据用户的个人资料和健康状况生成定制的运动计划,并纳入国际标准的健康级别七国际组织(HL7)关于身体素质和健康筛查的数据。生成的计划以代表性状态转移(REST)样式的网络服务形式呈现,这些服务可以从任何联网设备访问,并部署在云计算环境中。为确保生成的运动计划的实用性,系统中用作推理基础的封装知识是从领域专家那里获取的。所提出的用于个性化身体素质的普适运动计划生成系统(UFIT)不仅将通过生成个性化运动计划来改善与健康相关的身体素质,还将帮助用户避免不恰当的锻炼。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e223/4003957/0dfc28019d79/sensors-14-04560f1.jpg

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