Preprint Article Version 1 This version is not peer-reviewed

H-Rec2: A Novel Mobile-Social System for Automatic Health Recognition and Recommendation

Version 1 : Received: 10 March 2017 / Approved: 10 March 2017 / Online: 10 March 2017 (17:32:31 CET)

How to cite: Li, H.; Lu, K.; Zhang, Q. H-Rec2: A Novel Mobile-Social System for Automatic Health Recognition and Recommendation. Preprints 2017, 2017030058 (doi: 10.20944/preprints201703.0058.v1). Li, H.; Lu, K.; Zhang, Q. H-Rec2: A Novel Mobile-Social System for Automatic Health Recognition and Recommendation. Preprints 2017, 2017030058 (doi: 10.20944/preprints201703.0058.v1).

Abstract

Over the past decades, overweight and obesity has become a global epidemic and the leading threat for death. To prevent the serious risk, an overweight or obese individual must apply a long-term weight-management strategy to control food intake and physical activities, which is however, not easy. Recently, with the advances of information technology, more and more people can use wearable devices and smartphones to obtain physical activity information, while they can also access various health-related information from Internet online social networks (OSNs). Nevertheless, there is a lack of an integrated approach that can combine these two methods in an efficient way. In this paper, we address this issue and propose a novel mobile-social framework for health recognition and recommendation, namely, H-Rec2. The main ideas of H-Rec2 include (1) to recognize the individual's health status using smartphone as a general platform, and (2) to recommend physical activity and food intake based on personal health information, life science principles, and health-related information obtained from OSNs. To demonstrate the potentials of the H-Rec2 framework, we develop a prototype that consists of four important components: (1) an activity recognition module that senses physical activity using accelerometer, (2) a health status modeling module that applies a novel algorithm to generate personalized health status index, (3) a restaurant information collection module that collects relevant information from OSN, and (4) a restaurant recommendation module that provides personalized and context-aware recommendation. To evaluate the prototype, we conduct both objective and subjective experiments, which confirm the performance and effectiveness of the proposed system.

Subject Areas

Smartphone sensing; mobile-social integration; automatic recognition; social data; long-term health monitoring

Readers' Comments and Ratings (0)

Leave a public comment
Send a private comment to the author(s)
Rate this article
Views 0
Downloads 0
Comments 0
Metrics 0
Leave a public comment

×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.