Publication Type
Journal Article
Version
publishedVersion
Publication Date
12-2023
Abstract
Activities of daily living (ADLs) relate to people’s daily self-care activities, which reflect their living habits and lifestyle. A prior study presented a neural network model called STADLART for ADL routine learning. In this paper, we propose a cognitive model named Spatial-Temporal Episodic Memory for ADL (STEM-ADL), which extends STADLART to encode event sequences in the form of distributed episodic memory patterns. Specifically, STEM-ADL encodes each ADL and its associated contextual information as an event pattern and encodes all events in a day as an episode pattern. By explicitly encoding the temporal characteristics of events as activity gradient patterns, STEM-ADL can be suitably employed for activity prediction tasks. In addition, STEM-ADL can predict both the ADL type and starting time of the subsequent event in one shot. A series of experiments are carried out on two real-world ADL data sets: Orange4Home and OrdonezB, to estimate the efficacy of STEM-ADL. The experimental results indicate that STEM-ADL is remarkably robust in event retrieval using incomplete or noisy retrieval cues. Moreover, STEM-ADL outperforms STADLART and other state-of-the-art models in ADL retrieval and subsequent event prediction tasks. STEM-ADL thus offers a vast potential to be deployed in real-life healthcare applications for ADL monitoring and lifestyle recommendation.
Keywords
Spatial-temporal episodic memory, Encoding and retrieval, ADL retrieval, Subsequent event prediction
Discipline
Databases and Information Systems | Health Information Technology
Research Areas
Data Science and Engineering
Publication
Complex & Intelligent Systems
First Page
1
Last Page
18
ISSN
2199-4536
Identifier
10.1007/s40747-023-01298-8
Publisher
Springer
Citation
SONG, Xinjing; WANG, Di; Quek, Chai; TAN, Ah-hwee; and Wang, Yanjiang.
Spatial-temporal episodic memory modeling for ADLs: Encoding, retrieval, and prediction. (2023). Complex & Intelligent Systems. 1-18.
Available at: https://ink.library.smu.edu.sg/sis_research/8472
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional URL
https://doi.org/10.1007/s40747-023-01298-8