Improving recognition accuracy for activities of daily living by adding time and area related features

Yutaka Arakawa, Keiichi Yasumoto, Krita Pattamasiriwat, Teruhiro Mizumoto

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

1 Citation (Scopus)

Abstract

Recognizing the activities of daily living (ADL) of residents in housing is indispensable for operating Daily Life Support Services such as Elderly Monitoring, Smart Home Automation, and Health Support. However, the existing methods have various problems: invasion of privacy, limited target activities, low recognition accuracy, initial installation cost, and long recognition time. As our prior work, we proposed a real-time ADL recognition method using indoor positioning sensor and power meters. We got a result that the method can recognize ten types of ADL with the average accuracy of 79%. However, the accuracy of some activities such as work/study and bathroom-related were not satisfactory. In this work, we aim to improve the accuracy of our prior method by newly adding several new time and are related features such as time slot when activity occurs, staying time in the same area, and previous position. As a result, we could achieve 82% of average recognition accuracy for 10 different activities.

Original languageEnglish
Title of host publication2017 10th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9784907626310
DOIs
Publication statusPublished - Apr 2 2018
Externally publishedYes
Event10th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2017 - Toyama, Japan
Duration: Oct 3 2017Oct 5 2017

Publication series

Name2017 10th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2017
Volume2018-January

Other

Other10th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2017
CountryJapan
CityToyama
Period10/3/1710/5/17

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Safety, Risk, Reliability and Quality

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  • Cite this

    Arakawa, Y., Yasumoto, K., Pattamasiriwat, K., & Mizumoto, T. (2018). Improving recognition accuracy for activities of daily living by adding time and area related features. In 2017 10th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2017 (pp. 1-6). (2017 10th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ICMU.2017.8330104