A Physical Activity Recommender System for Patients With Arterial Hypertension

Ferretto, Luciano Rodrigo; Bellei, Ericles Andrei; Biduski, Daiana; Bin, Luiz Carlos Pereira; Moro, Mirella M.; Cervi, Cristiano Roberto; Marchi, Ana Carolina Bertoletti De

doi:10.1109/access.2020.2983564

Cited by 36 publications

(28 citation statements)

References 51 publications

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“…However, most of health recommenders currently available do not implement a holistic user model nor a holistic recommendation strategy. Usually, they aim to deliver trustworthy relevant information to end-users, improving their safety [39], or providing lifestyle change recommendations: e.g., by suggesting how to adopt more healthy habits [16], by improving users' eating behavior [32], [40], [45], [46], by recommending physical activities while taking into account the user's health state [17], or by preventing not healthy behavior, such as alcohol consumption [55].…”

Section: Health Recommendersmentioning

confidence: 99%

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Generating Recommendations From Multiple Data Sources: A Methodological Framework for System Design and Its Application

et al. 2020

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Recommender systems (RSs) are systems that produce individualized recommendations as output or drive the user in a personalized way to interesting or useful objects in a space of possible options. Recently, RSs emerged as an effective support for decision making. However, when people make decisions, they usually take into account different and often conflicting information such as preferences, long-term goals, context, and their current condition. This complexity is often ignored by RSs. In order to provide an effective decision-making support, a RS should be "holistic", i.e., it should rely on a complete representation of the user, encoding heterogeneous user features (such as personal interests, psychological traits, health data, social connections) that may come from multiple data sources. However, to obtain such holistic recommendations some steps are necessary: first, we need to identify the goal of the decision-making process; then, we have to exploit common-sense and domain knowledge to provide the user with the most suitable suggestions that best fit the recommendation scenario. In this article, we present a methodological framework that can drive researchers and developers during the design process of this kind of "holistic" RS. We also provide evidence of the framework validity by presenting the design process and the evaluation of a food RS based on holistic principles.

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Section: Health Recommendersmentioning

confidence: 99%

“…HyperRecSysPA [17] is a system for recommending physical activities for hypertensive patients based on a hypertensive user profile model. The model is composed of 32 elements divided into three groups, which were used in the modeling of user profiles within the system for generating HyperRecSysPA recommendations.…”

Section: Health Recommendersmentioning

confidence: 99%

Generating Recommendations From Multiple Data Sources: A Methodological Framework for System Design and Its Application

et al. 2020

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“…Authors designed HyperRecSysPA, a physical fitness recommender model for hypertensive patients [22]. The Physical Activity Result Indexes were used to analyse the importance of physical activity.…”

Section: Related Workmentioning

confidence: 99%

“…5,2020 DOI: 10.22266/ijies2020.1031. 22 system is one of the information filtering approach which suggests services to users of their interests. Health improvement recommender systems provide suggestions for nutrition, disease prevention therapy, and recipes.…”

Section: Introductionmentioning

confidence: 99%

Physical Fitness Recommender Framework for Thyroid Patients using Restricted Boltzmann Machines

Vairale¹,

Christ²,

Shukla³

2020

IJIES

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These days, people can easily acquire the information from online sources. Individuals are generally using recommendation services before buying products considering the availability of online. Recommendation systems propose the relevant services or products to users. But sometimes people face issues while retrieving health related information from the recommender systems. A focus on keeping people healthy is one way to address the serious societal concern of healthcare domain. A health-based physical recommender system suggests workout plans for users using their activity level and health condition. A personalized approach is the most effective solution for the fitnessbased recommender framework based on user's desired characteristics. This article presents a personalized fitness recommender system for thyroid patients. The proposed fitness recommender model integrates the user's data like personal and health profile, preferences, calorie intake, and activity level. The proposed hybrid model is built using Restricted Boltzmann Machines (RBM) integrating content based and matrix factorization techniques. The results of experiments prove that the proposed hybrid model outperforms than content based, pure RBM and matrix factorization recommendation techniques. The current proposal achieves the personalization approach by incorporating user's thyroid health condition and exercise preferences in recommendation process. The recommended result of hybrid RBM method is revised based on user's new preferences.

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“…The prompt responses are fed as recommendations for the end-users through continuous monitoring. The continuous monitoring instances identify the changes in physiological vitals, and hence the diagnosis is recommended without time delay (Ferretto et al 2020). The personalized solutions are provided through the interactive application environment from the user equipment.…”

Section: Introductionmentioning

confidence: 99%

Situation-aware recommendation system for personalized healthcare applications

Saad

Fouad

Mohamed

2021

J Ambient Intell Human Comput

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Personalized healthcare applications are designed with the help of handheld devices for providing instantaneous monitoring and diagnosis. Modern healthcare applications are designed with recommendation systems for providing diagnosis assistance for the users. This article introduces a novel situation-aware recommendation system for improving the reliability of personal healthcare applications. This recommendation system relies on recurrent neural learning for identifying the situation based on different physiological vitals. The learning method identifies a similar cause from the previous history and augments a recommendation from the connected healthcare server. The process of the situation is understood for providing equal and instantaneous recommendations for the patients. In this learning, the situation's training and the previous cases of occurrence are performed concurrently for identifying matching instances, reducing the complexity. The proposed system's performance is verified using the metrics matching ratio, complexity, recommendation delay, and accuracy.

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A Physical Activity Recommender System for Patients With Arterial Hypertension

Cited by 36 publications

References 51 publications

Generating Recommendations From Multiple Data Sources: A Methodological Framework for System Design and Its Application

Generating Recommendations From Multiple Data Sources: A Methodological Framework for System Design and Its Application

Physical Fitness Recommender Framework for Thyroid Patients using Restricted Boltzmann Machines

Situation-aware recommendation system for personalized healthcare applications

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