Progressive sequence matching for ADL plan recommendation
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 (...
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| Format: | Conference Paper |
| Language: | English |
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2018
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| Online Access: | https://hdl.handle.net/10356/89671 http://hdl.handle.net/10220/47043 |
| _version_ | 1811695779993092096 |
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| author | Gao, Shan Wang, Di Tan, Ah-Hwee Miao, Chunyan |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Gao, Shan Wang, Di Tan, Ah-Hwee Miao, Chunyan |
| author_sort | Gao, Shan |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T07:28:54Z |
| format | Conference Paper |
| id | ntu-10356/89671 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:28:54Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/896712020-03-07T11:48:46Z Progressive sequence matching for ADL plan recommendation Gao, Shan Wang, Di Tan, Ah-Hwee Miao, Chunyan School of Computer Science and Engineering 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) NTU-UBC Research Centre of Excellence in Active Living for the Elderly Sequence Matching DRNTU::Engineering::Computer science and engineering Active Lifestyles 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. NRF (Natl Research Foundation, S’pore) Accepted version 2018-12-18T04:03:13Z 2019-12-06T17:30:49Z 2018-12-18T04:03:13Z 2019-12-06T17:30:49Z 2015-12-01 2015 Conference Paper 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 https://hdl.handle.net/10356/89671 http://hdl.handle.net/10220/47043 10.1109/WI-IAT.2015.171 193900 en © 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]. 8 p. application/pdf |
| spellingShingle | Sequence Matching DRNTU::Engineering::Computer science and engineering Active Lifestyles Gao, Shan Wang, Di Tan, Ah-Hwee Miao, Chunyan Progressive sequence matching for ADL plan recommendation |
| title | Progressive sequence matching for ADL plan recommendation |
| title_full | Progressive sequence matching for ADL plan recommendation |
| title_fullStr | Progressive sequence matching for ADL plan recommendation |
| title_full_unstemmed | Progressive sequence matching for ADL plan recommendation |
| title_short | Progressive sequence matching for ADL plan recommendation |
| title_sort | progressive sequence matching for adl plan recommendation |
| topic | Sequence Matching DRNTU::Engineering::Computer science and engineering Active Lifestyles |
| url | https://hdl.handle.net/10356/89671 http://hdl.handle.net/10220/47043 |
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