Deep learning with long short-term memory networks for classification of dementia related travel patterns

Page view(s)
100
Checked on Sep 19, 2024
Deep learning with long short-term memory networks for classification of dementia related travel patterns
Title:
Deep learning with long short-term memory networks for classification of dementia related travel patterns
Journal Title:
Publication URL:
Publication Date:
27 August 2020
Citation:
Abstract:
Wandering pattern classification is important for early recognition of cognitive deterioration and other health conditions in people with dementia (PWD). In this paper, we leverage the orientation data available on mobile devices to recognize dementia-related wandering patterns. In particular, we propose to use deep learning (DL) with long short-term memory networks (LSTM) as classifiers for detecting travel patterns including direct, pacing, lapping and random. Experimental results on a real dataset collected from 14 subjects show that deep LSTM classifiers perform better than traditional machine learning (ML) classifiers. Our proposed method can thus be potentially used in healthcare applications for dementia related wandering monitoring and management.Clinical Relevance— This demonstrates the potential of using readily available yet non-privacy information to detect dementia-related wandering patterns with high accuracy.
License type:
Funding Info:
No specific funding
Description:
ISSN:
1558-4615
Files uploaded:
File Size Format Action
There are no attached files.