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

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Abstract

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.

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Authors and Affiliations

  1. NTT Communication Science Laboratories, 2-4 Hikaridai Seika-cho, Souraku-gun, Kyoto, Japan

    Takuya Maekawa & Shinji Watanabe

Authors
  1. Takuya Maekawa
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  2. Shinji Watanabe
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Correspondence to Takuya Maekawa.

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Based on ‘Unsupervised Activity Recognition with user’s Physical Characteristics Data’ by Takuya Maekawa and Shinji Watanabe which appeared in the Proceedings of the International Symposium on Wearable Computers, San Francisco, California, June 2011. © 2011 IEEE.

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Maekawa, T., Watanabe, S. Training data selection with user’s physical characteristics data for acceleration-based activity modeling. Pers Ubiquit Comput 17, 451–463 (2013). https://doi.org/10.1007/s00779-011-0491-0

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  • DOI: https://doi.org/10.1007/s00779-011-0491-0

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