Publication Type

Journal Article

Version

acceptedVersion

Publication Date

1-2021

Abstract

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.

Keywords

ADL sequence, fusion ART, activity pattern, spatiotemporal features

Discipline

Databases and Information Systems | Digital Communications and Networking

Research Areas

Data Science and Engineering

Publication

IEEE Transactions on Knowledge and Data Engineering

Volume

33

Issue

1

First Page

143

Last Page

153

ISSN

1041-4347

Identifier

10.1109/TKDE.2019.2924623

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TKDE.2019.2924623

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