Deep convolutional neural network classifier for travel patterns using binary sensors

Munkhjargal Gochoo, Shing Hong Liu, Damdinsuren Bayanduuren, Tan Hsu Tan, Vijayalakshmi Velusamy, Tsung Yu Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

The early detection of dementia is crucial in independent life style of elderly people. Main intention of this study is to propose device-free non-privacy invasive Deep Convolutional Neural Network classifier (DCNN) for Martino-Saltzman's (MS) travel patterns of elderly people living alone using open dataset collected by binary (passive infrared) sensors. Travel patterns are classified as direct, pacing, lapping, or random according to MS model. MS travel pattern is highly related with person's cognitive state, thus can be used to detect early stage of dementia. The dataset was collected by monitoring a cognitively normal elderly resident by wireless passive infrared sensors for 21 months. First, over 70000 travel episodes are extracted from the dataset and classified by MS travel pattern classifier algorithm for the ground truth. Later, 12000 episodes (3000 for each pattern) were randomly selected from the total episodes to compose training and testing dataset. Finally, DCNN performance was compared with three other classical machine-learning classifiers. The Random Forest and DCNN yielded the best classification accuracies of 94.48% and 97.84%, respectively. Thus, the proposed DCNN classifier can be used to infer dementia through travel pattern matching.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 8th International Conference on Awareness Science and Technology, iCAST 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages132-137
Number of pages6
ISBN (Electronic)9781538629659
DOIs
Publication statusPublished - Jul 1 2017
Externally publishedYes
Event8th IEEE International Conference on Awareness Science and Technology, iCAST 2017 - Taichung, Taiwan, Province of China
Duration: Nov 8 2017Nov 10 2017

Publication series

NameProceedings - 2017 IEEE 8th International Conference on Awareness Science and Technology, iCAST 2017
Volume2018-January

Conference

Conference8th IEEE International Conference on Awareness Science and Technology, iCAST 2017
Country/TerritoryTaiwan, Province of China
CityTaichung
Period11/8/1711/10/17

Keywords

  • assistive technology
  • deep learning
  • device-free
  • elder care
  • non-invasive
  • smart house
  • travel pattern

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Control and Optimization
  • Health Informatics

Fingerprint

Dive into the research topics of 'Deep convolutional neural network classifier for travel patterns using binary sensors'. Together they form a unique fingerprint.

Cite this