Social recommendations for personalized fitness assistance

Dharia, Saumil; Eirinaki, Magdalini; Jain, Vijesh; Patel, Jvalant; Varlamis, Iraklis; Vora, Jainikkumar; Yamauchi, Rizen

doi:10.1007/s00779-017-1039-8

Cited by 22 publications

(23 citation statements)

References 32 publications

(43 reference statements)

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“…In another study, PRO-fit recommended a fitness partner using geolocation, activity preference, and calendar-based availability on a smartphone [56]. It also provided activity recommendation using collaborative filtering [57] and activity prediction from raw accelerometer data. An Internet of Things–based app [94] proposed a context-aware recommendation system to generate a suitable activity for the user based on current fatigue and fitness level.…”

Section: Resultsmentioning

confidence: 99%

User Models for Personalized Physical Activity Interventions: Scoping Review

Ghanvatkar¹,

Kankanhalli²,

Rajan³

2019

JMIR Mhealth Uhealth

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BackgroundFitness devices have spurred the development of apps that aim to motivate users, through interventions, to increase their physical activity (PA). Personalization in the interventions is essential as the target users are diverse with respect to their activity levels, requirements, preferences, and behavior.ObjectiveThis review aimed to (1) identify different kinds of personalization in interventions for promoting PA among any type of user group, (2) identify user models used for providing personalization, and (3) identify gaps in the current literature and suggest future research directions.MethodsA scoping review was undertaken by searching the databases PsycINFO, PubMed, Scopus, and Web of Science. The main inclusion criteria were (1) studies that aimed to promote PA; (2) studies that had personalization, with the intention of promoting PA through technology-based interventions; and (3) studies that described user models for personalization.ResultsThe literature search resulted in 49 eligible studies. Of these, 67% (33/49) studies focused solely on increasing PA, whereas the remaining studies had other objectives, such as maintaining healthy lifestyle (8 studies), weight loss management (6 studies), and rehabilitation (2 studies). The reviewed studies provide personalization in 6 categories: goal recommendation, activity recommendation, fitness partner recommendation, educational content, motivational content, and intervention timing. With respect to the mode of generation, interventions were found to be semiautomated or automatic. Of these, the automatic interventions were either knowledge-based or data-driven or both. User models in the studies were constructed with parameters from 5 categories: PA profile, demographics, medical data, behavior change technique (BCT) parameters, and contextual information. Only 27 of the eligible studies evaluated the interventions for improvement in PA, and 16 of these concluded that the interventions to increase PA are more effective when they are personalized.ConclusionsThis review investigates personalization in the form of recommendations or feedback for increasing PA. On the basis of the review and gaps identified, research directions for improving the efficacy of personalized interventions are proposed. First, data-driven prediction techniques can facilitate effective personalization. Second, use of BCTs in automated interventions, and in combination with PA guidelines, are yet to be explored, and preliminary studies in this direction are promising. Third, systems with automated interventions also need to be suitably adapted to serve specific needs of patients with clinical conditions. Fourth, previous user models focus on single metric evaluations of PA instead of a potentially more effective, holistic, and multidimensional view. Fifth, with the widespread adoption of activity monitoring devices and mobile phones, personalized and dynamic user models can be created using available user data, including users’ social profile. Finally, the long-term effects of suc...

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Section: Resultsmentioning

confidence: 99%

User Models for Personalized Physical Activity Interventions: Scoping Review

Ghanvatkar¹,

Kankanhalli²,

Rajan³

2019

JMIR Mhealth Uhealth

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“…The system was developed PRO-Fit, a personalized fitness recommender framework to motivate users in physical activities by incorporating a social factor [17]. PRO-fit collected data from tracking devices and classified user's activity type.…”

Section: Related Workmentioning

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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“…These include, among other things, encouraging people to be physically active and to create an environment that makes it easier for people to be physically active in their homes. For example, [ 5 ] use smartphone data and developed a fitness assistant framework that automatically generates a fitness schedule. The framework also incorporates social interaction to increase the engagement of its users.…”

Section: Introductionmentioning

confidence: 99%

ExerSense: Physical Exercise Recognition and Counting Algorithm from Wearables Robust to Positioning

Ishii

Yokokubo

Luimula

et al. 2020

Sensors

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Wearable devices are currently popular for fitness tracking. However, these general usage devices only can track limited and prespecified exercises. In our previous work, we introduced ExerSense that segments, classifies, and counts multiple physical exercises in real-time based on a correlation method. It also can track user-specified exercises collected only one motion in advance. This paper is the extension of that work. We collected acceleration data for five types of regular exercises by four different wearable devices. To find the best accurate device and its position for multiple exercise recognition, we conducted 50 times random validations. Our result shows the robustness of ExerSense, working well with various devices. Among the four general usage devices, the chest-mounted sensor is the best for our target exercises, and the upper-arm-mounted smartphone is a close second. The wrist-mounted smartwatch is third, and the worst one is the ear-mounted sensor.

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Social recommendations for personalized fitness assistance

Cited by 22 publications

References 32 publications

User Models for Personalized Physical Activity Interventions: Scoping Review

User Models for Personalized Physical Activity Interventions: Scoping Review

Physical Fitness Recommender Framework for Thyroid Patients using Restricted Boltzmann Machines

ExerSense: Physical Exercise Recognition and Counting Algorithm from Wearables Robust to Positioning

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