Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

12-2015

Abstract

Activities of Daily Living (ADLs) are indicatives of a person’s lifestyle. In particular, daily ADL routines closely relate to a person’s well-being. With the objective of promoting active lifestyles, this paper presents an agent system that provides recommendations of suitable ADL plans (i.e., selected ADL sequences) to individual users based on the more active lifestyles of the others. Specifically, we develop a set of quantitative measures, named wellness scores, spanning the evaluation across the physical, cognitive, emotion, and social aspects based on his or her ADL routines. Then we propose an ADL sequence learning model, named Recommendation ADL ART, or RADLART, which proactively recommends healthier choices of activities based on the learnt associations among the user profiles, ADL sequence, and wellness scores. For empirical evaluation, extensive simulations have been conducted to assess the improvement in wellness scores for synthetic users with different acceptance rates of the provided recommendations. Experiments on real users further show that recommendations given by RADLART are generally more acceptable by the users because it takes into considerations of both the user profiles and the performed activities.

Discipline

Databases and Information Systems | Software Engineering

Research Areas

Data Science and Engineering

Publication

Proceedings of 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), Siingapore, December 6-9

Volume

2

First Page

360

Last Page

367

ISBN

9781467396172

Identifier

10.1109/WI-IAT.2015.171

Publisher

IEEE

City or Country

Singapore

Additional URL

https://doi.org/10.1109/WI-IAT.2015.171

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