Investigating recognition accuracy improvement by adding user's acceleration data to location and power consumption-based in-home activity recognition system

Eri Nakagawa, Kazuki Moriya, Hirohiko Suwa, Manato Fujimoto, Yutaka Arakawa, Toshiyuki Hatta, Shotaro Miwa, Keiichi Yasumoto

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

2 Citations (Scopus)

Abstract

Recently, there are many studies on automatic recognition of activities of daily living (ADL) to provide various ser-vices such as elderly monitoring, intelligent concierge, and health support. In particular, real-time ADL recognition is essential to realize an intelligent concierge service since the service needs to know user's current or next activity for supporting it. We have been studying real-time ADL recognition using only user's position data and appliances' power consumption data which are considered to include less privacy information than audio and visual data. In the study, we found that some activities such as reading and operating smartphone that happen in similar conditions cannot be classified with only position and power data. In this paper, we propose a new method that adds the acceleration data from wearable devices for classifying activities happening in similar conditions with higher accuracy. In the proposed method, we use the acceleration data from a smart watch and a smartphone worn by user's arm and waist, respectively, in addition to user's position data and appliances' power consumption data, and construct a machine learning model for recognizing 15 types of target activities. We evaluated the recognition accuracy of 3 methods: our previous method (using only position data and power consumption data); the proposed method using the mean value and the standard deviation of the acceleration norm; and the pro-posed method using the ratio of the activity topics. We collected the sensor data in our smart home facility for 12 days, and applied the proposed method to these sensor data. As a result, the proposed method could recognize the activities with 57 % which is 12 % improvement from our previous method without acceleration data.

Original languageEnglish
Title of host publicationAdjunct Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems
Subtitle of host publicationComputing, Networking and Services, MobiQuitous 2016
PublisherAssociation for Computing Machinery
Pages100-105
Number of pages6
ISBN (Electronic)9781450347594
DOIs
Publication statusPublished - Nov 28 2016
Externally publishedYes
Event13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2016 - Hiroshima, Japan
Duration: Nov 28 2016Dec 1 2016

Publication series

NameACM International Conference Proceeding Series
Volume28-November-2016

Conference

Conference13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2016
CountryJapan
CityHiroshima
Period11/28/1612/1/16

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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  • Cite this

    Nakagawa, E., Moriya, K., Suwa, H., Fujimoto, M., Arakawa, Y., Hatta, T., Miwa, S., & Yasumoto, K. (2016). Investigating recognition accuracy improvement by adding user's acceleration data to location and power consumption-based in-home activity recognition system. In Adjunct Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2016 (pp. 100-105). (ACM International Conference Proceeding Series; Vol. 28-November-2016). Association for Computing Machinery. https://doi.org/10.1145/3004010.3004036