Training data selection with user's physical characteristics data for acceleration-based activity modeling

Takuya Maekawa*, Shinji Watanabe

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


This paper proposes an activity recognition method that models an end user's activities without using any labeled/unlabeled acceleration sensor data obtained from the user. Our method employs information about the end user's physical characteristics such as height and gender to find and select appropriate training data obtained from other users in advance. Then, we model the end user's activities by using the selected labeled sensor data. Therefore, our method does not require the end user to collect and label her training sensor data. In this paper, we propose and test two methods for finding appropriate training data by using information about the end user's physical characteristics. Moreover, our recognition method improves the recognition performance without the need for any effort by the end user because the method automatically adapts the activity models to the end user when it recognizes her unlabeled sensor data. We confirmed the effectiveness of our method by using 100 h of sensor data obtained from 40 participants.

Original languageEnglish
Pages (from-to)451-463
Number of pages13
JournalPersonal and Ubiquitous Computing
Issue number3
Publication statusPublished - 2013 Mar
Externally publishedYes


  • Acceleration sensor
  • Activity recognition
  • Physical characteristics
  • Wearable sensors

ASJC Scopus subject areas

  • Library and Information Sciences
  • Hardware and Architecture
  • Computer Science Applications
  • Management Science and Operations Research


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