Exploring accuracy-cost tradeoff in in-home living activity recognition based on power consumptions and user positions

Kenki Ueda, Hirohiko Suwa, Yutaka Arakawa, Keiichi Yasumoto

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

11 Citations (Scopus)

Abstract

Advanced context-aware services at home such as elderly monitoring requires highly accurate living activity recognition in a home environment. Existing studies on living activity recognition suffer from high deployment and maintenance costs, privacy intrusion due to utilization of cameras and microphones, and few recognizable activities or low recognition accuracy. In this paper, to solve these problems, we propose a new living activity recognition method. Our method utilizes only power meters attached to appliances and a positioning sensor attached to a resident of a home to mitigate privacy intrusion. We target 10 different living activities which cover most of our daily lives at home and construct activity recognition models based on machineleaning. To accurately recognize the activities from the sensor data by power meters and position sensor, we explore the best combination of time window width for samples of training/test data, features, and machine-learning algorithms. Furthermore, we thoroughly investigate the tradeoff between the sensor data granularity and the consequent recognition accuracy. Through experiments using sensor data collected by four participants in our smart home, the proposed method achieved 97.8 % average F-measure of recognizing 10 target activities with the finest sensor data granularity (position estimation error ≤ 0.1m, 16 power meters) and 86.9 % F-measure with room-level position accuracy and one power meter for each of four rooms.

Original languageEnglish
Title of host publicationProceedings - 15th IEEE International Conference on Computer and Information Technology, CIT 2015, 14th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2015, 13th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2015 and 13th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2015
EditorsLuigi Atzori, Xiaolong Jin, Stephen Jarvis, Lei Liu, Ramon Aguero Calvo, Jia Hu, Geyong Min, Nektarios Georgalas, Yulei Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1130-1137
Number of pages8
ISBN (Electronic)9781509001545
DOIs
Publication statusPublished - Dec 22 2015
Externally publishedYes
Event15th IEEE International Conference on Computer and Information Technology, CIT 2015, 14th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2015, 13th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2015 and 13th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2015 - Liverpool, United Kingdom
Duration: Oct 26 2015Oct 28 2015

Publication series

NameProceedings - 15th IEEE International Conference on Computer and Information Technology, CIT 2015, 14th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2015, 13th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2015 and 13th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2015

Conference

Conference15th IEEE International Conference on Computer and Information Technology, CIT 2015, 14th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2015, 13th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2015 and 13th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2015
CountryUnited Kingdom
CityLiverpool
Period10/26/1510/28/15

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Artificial Intelligence
  • Computer Networks and Communications

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