با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
عنوان انگلیسی مقاله:
A cross-domain framework for designing healthcare mobile applications mining social networks to generate recommendations of training and nutrition planning
ترجمه فارسی عنوان مقاله:
یک چارچوب متقابل دامنه برای طراحی برنامه های کاربردی سیار بهداشت و درمان برای ایجاد برنامه های آموزشی و برنامه ریزی تغذیه
Sciencedirect - Elsevier - Telematics and Informatics, Corrected proof. doi:10.1016/j.tele.2017.04.005
Felix Mata, Miguel Torres-Ruiz ⇑, Roberto Zagal, Giovanni Guzman, Marco Moreno-Ibarra, Rolando Quintero
Nowadays, people are practicing physical exercise in order to maintain good health
conditions. Such physical workouts are required by a plan, which should be designed
and supervised by sport specialists and medical assistants. Thus, the exercise sessions shall
start with consultation of a coach, doctor and dietician; however, many times this scenario
is not presented. In typical activities such as running, cycling and fitness, people use health
mobile apps with their smartphones, which offer support for these activities. Nevertheless,
the functionality and operation of these applications are isolated, because many and long
questionnaires are performed. Additionally, the physical and health state of a user is not
considered. These issues would be taken into account for determining recommendations
about the time for doing exercise and the kind of activity for each person. In this work, a
social semantic mobile framework to generate recommendations where a mobile applica
tion allows sensing the physical performance, taking into consideration medical criteria
with smartphones is proposed. The approach includes a semantic cross-information that
comes from social network and official data as well as sport activities and medical knowl
edge. This knowledge is translated into application ontologies related directly to health,
nutrition and training domains. The methodology also covers physical fitness tests and a
monitoring tool for evaluating the nutrition plan and the correct execution of the training.
As case study, the mobile application offers to evaluate the physical and health conditions
of a runner, automatically generate a nutrition plan and training, monitor plans and recom
puted them if users make changes in their routines. The data provided from the social net
work are used as feedback in the application, in order to make the training and nutrition
plans more flexible by applying spatio-temporal analysis based on machine learning.
Finally, the generated training and nutrition plans were validated by specialists, they have
demonstrated 82% of effectiveness rate in exercise training routines and 86% in nutrition
plans. In addition, the results were compared with isolated approaches and manual recom
mendations made by specialists, the obtained overall performance was 81%.