DCNN-based elderly activity recognition using binary sensors

Munkhjargal Gochoo, Tan Hsu Tan, Shih Chia Huang, Shing Hong Liu, Fady S. Alnajjar

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

20 Citations (Scopus)

Abstract

In the past few decades, the number of elderly people who prefer to live independently is significantly increasing among the elderly people due to the issues of privacy invasion and elderly care cost. Device-free non-privacy invasive activity recognition is preferred for long-term monitoring. Thus, we propose a deep learning classification method for elderly activities using binary sensors (PIR sensor and door sensor). In particular, we present a Deep Convolutional Neural Network (DCNN) classification approach for detecting four basic activity classes which represent the basic human activities in a home monitoring environment, namely: Bed-to-Toilet, Eating, Meal-Preparation, and Relax. A real-world long-term annotated dataset is employed for evaluation of the activity recognition classifier. Dataset was offered by Center for Advanced Studies in Adaptive Systems (CASAS) project, and was collected by monitoring a cognitively normal elderly resident by binary sensors for 21 months First, we converted the annotated binary sensor data into a binary activity images for corresponding activities. Then, activity images are used for training and testing the DCNN classifier. Finally, classifiers are evaluated with 10-fold cross validation method. Experimental results showed the best DCNN classifier gives 99.36% of accuracy. Our next step is to improve this classifier for detection of intertwined complex activities of elderly and to implement it on a real life long-term elderly monitoring system.

Original languageEnglish
Title of host publication2017 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781538608722
DOIs
Publication statusPublished - Jun 28 2017
Event2017 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2017 - Ras Al Khaimah, United Arab Emirates
Duration: Nov 21 2017Nov 23 2017

Publication series

Name2017 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2017
Volume2018-January

Other

Other2017 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2017
Country/TerritoryUnited Arab Emirates
CityRas Al Khaimah
Period11/21/1711/23/17

Keywords

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

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Instrumentation
  • Computer Science Applications

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