Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89671
Title: Progressive sequence matching for ADL plan recommendation
Authors: Gao, Shan
Wang, Di
Tan, Ah-Hwee
Miao, Chunyan
Keywords: Sequence Matching
DRNTU::Engineering::Computer science and engineering
Active Lifestyles
Issue Date: 2015
Source: Gao, S., Wang, D., Tan, A.-H., & Miao, C. (2015). Progressive sequence matching for ADL plan recommendation. 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 360-367. doi:10.1109/WI-IAT.2015.171
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.
URI: https://hdl.handle.net/10356/89671
http://hdl.handle.net/10220/47043
DOI: 10.1109/WI-IAT.2015.171
Rights: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/WI-IAT.2015.171].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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