Intra-/inter-user adaptation framework for wearable gesture sensing device

Kosuke Kikui, Yuta Itoh, Makoto Yamada, Yuta Sugiura, Maki Sugimoto

研究成果: Conference contribution

1 引用 (Scopus)

抜粋

The photo reflective sensor (PRS), a tiny distant-measurement module, is a popular electronic component widely used in wearable user-interfaces. An unavoidable issue of such wearable PRS devices in practical use is the need of user-independent training to have high gesture recognition accuracy. Each new user has to re-train a device by providing new training data (we call the inter-user setup). Even worse, re-training is also necessary ideally every time when the same user re-wears the device (we call the intra-user setup). In this paper, we propose a domain adaptation framework to reduce this training cost of users. Specifically, we adapt a pre-trained convolutional neural network (CNN) for both inter-user and intra-user setups to maintain the recognition accuracy high. We demonstrate, with an actual PRS device, that our framework significantly improves the average classification accuracy of the intra-user and inter-user setups up to 87.43% and 80.06% against the baseline (non-adapted) setups with the accuracy 68.96% and 63.26% respectively.

元の言語English
ホスト出版物のタイトルISWC 2018 - Proceedings of the 2018 ACM International Symposium on Wearable Computers
出版者Association for Computing Machinery
ページ21-24
ページ数4
ISBN(電子版)9781450359672
DOI
出版物ステータスPublished - 2018 10 8
イベント22nd International Symposium on Wearable Computers, ISWC 2018 - Singapore, Singapore
継続期間: 2018 10 82018 10 12

Other

Other22nd International Symposium on Wearable Computers, ISWC 2018
Singapore
Singapore
期間18/10/818/10/12

    フィンガープリント

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

これを引用

Kikui, K., Itoh, Y., Yamada, M., Sugiura, Y., & Sugimoto, M. (2018). Intra-/inter-user adaptation framework for wearable gesture sensing device. : ISWC 2018 - Proceedings of the 2018 ACM International Symposium on Wearable Computers (pp. 21-24). Association for Computing Machinery. https://doi.org/10.1145/3267242.3267256