Spatial-temporal episodic memory modeling for ADLs: encoding, retrieval, and prediction

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.