Learning ADL daily routines with spatiotemporal neural networks
The activities of daily living (ADLs) refer to the activities performed by individuals on a daily basis and are the indicators of a person’s habits, lifestyle, and wellbeing. Learning an individual’s ADL daily routines has significant value in the healthcare domain. Specifically, ADL recognition and...
Saved in:
Main Authors: | , , |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2021
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/5177 https://ink.library.smu.edu.sg/context/sis_research/article/6180/viewcontent/NimbusRomNo9L_Regu.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
id |
sg-smu-ink.sis_research-6180 |
---|---|
record_format |
dspace |
spelling |
sg-smu-ink.sis_research-61802021-01-19T01:42:11Z Learning ADL daily routines with spatiotemporal neural networks GAO, Shan TAN, Ah-hwee SETCHI, Rossi The activities of daily living (ADLs) refer to the activities performed by individuals on a daily basis and are the indicators of a person’s habits, lifestyle, and wellbeing. Learning an individual’s ADL daily routines has significant value in the healthcare domain. Specifically, ADL recognition and inter-ADL pattern learning problems have been studied extensively in the past couple of decades. However, discovering the patterns performed in a day and clustering them into ADL daily routines has been a relatively unexplored research area. In this paper, a self-organizing neural network model, called the Spatiotemporal ADL Adaptive Resonance Theory (STADLART), is proposed for learning ADL daily routines. STADLART integrates multimodal contextual information that involves the time and space wherein the ADL is performed. By encoding spatiotemporal information explicitly as input features, STADLART enables the learning of time-sensitive knowledge. Moreover, a STADLART variation named STADLART-NC is proposed to normalize and customize ADL weighting for daily routine learning. A weighting assignment scheme is developed that facilitates the assignment of weighting according to ADL importance in specific domains. Empirical experiments using both synthetic and real-world public data sets validate the performance of STADLART and STADLART-NC when compared with alternative pattern discovery methods. The results show STADLART could cluster ADL routines with better performance than baseline algorithms. 2021-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5177 info:doi/10.1109/TKDE.2019.2924623 https://ink.library.smu.edu.sg/context/sis_research/article/6180/viewcontent/NimbusRomNo9L_Regu.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University ADL sequence fusion ART activity pattern spatiotemporal features Databases and Information Systems Digital Communications and Networking |
institution |
Singapore Management University |
building |
SMU Libraries |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
SMU Libraries |
collection |
InK@SMU |
language |
English |
topic |
ADL sequence fusion ART activity pattern spatiotemporal features Databases and Information Systems Digital Communications and Networking |
spellingShingle |
ADL sequence fusion ART activity pattern spatiotemporal features Databases and Information Systems Digital Communications and Networking GAO, Shan TAN, Ah-hwee SETCHI, Rossi Learning ADL daily routines with spatiotemporal neural networks |
description |
The activities of daily living (ADLs) refer to the activities performed by individuals on a daily basis and are the indicators of a person’s habits, lifestyle, and wellbeing. Learning an individual’s ADL daily routines has significant value in the healthcare domain. Specifically, ADL recognition and inter-ADL pattern learning problems have been studied extensively in the past couple of decades. However, discovering the patterns performed in a day and clustering them into ADL daily routines has been a relatively unexplored research area. In this paper, a self-organizing neural network model, called the Spatiotemporal ADL Adaptive Resonance Theory (STADLART), is proposed for learning ADL daily routines. STADLART integrates multimodal contextual information that involves the time and space wherein the ADL is performed. By encoding spatiotemporal information explicitly as input features, STADLART enables the learning of time-sensitive knowledge. Moreover, a STADLART variation named STADLART-NC is proposed to normalize and customize ADL weighting for daily routine learning. A weighting assignment scheme is developed that facilitates the assignment of weighting according to ADL importance in specific domains. Empirical experiments using both synthetic and real-world public data sets validate the performance of STADLART and STADLART-NC when compared with alternative pattern discovery methods. The results show STADLART could cluster ADL routines with better performance than baseline algorithms. |
format |
text |
author |
GAO, Shan TAN, Ah-hwee SETCHI, Rossi |
author_facet |
GAO, Shan TAN, Ah-hwee SETCHI, Rossi |
author_sort |
GAO, Shan |
title |
Learning ADL daily routines with spatiotemporal neural networks |
title_short |
Learning ADL daily routines with spatiotemporal neural networks |
title_full |
Learning ADL daily routines with spatiotemporal neural networks |
title_fullStr |
Learning ADL daily routines with spatiotemporal neural networks |
title_full_unstemmed |
Learning ADL daily routines with spatiotemporal neural networks |
title_sort |
learning adl daily routines with spatiotemporal neural networks |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2021 |
url |
https://ink.library.smu.edu.sg/sis_research/5177 https://ink.library.smu.edu.sg/context/sis_research/article/6180/viewcontent/NimbusRomNo9L_Regu.pdf |
_version_ |
1770575321892913152 |