Experimental evaluation of variations in primary features used for accelerometric context recognition

Ernst A. Heinz, Kai S. Kunze, Stefan Sulistyo, Holger Junker, Paul Lukowicz, Gerhard Tröster

研究成果: Chapter

15 被引用数 (Scopus)

抄録

The paper describes initial results in an ongoing project aimed at providing and analyzing standardized representative data sets for typical context recognition tasks. Such data sets can be used to develop user-independent feature sets and recognition algorithms. In addition, we aim to establish standard benchmark data sets that can be used for quantitative comparisons of different recognition methodologies. Benchmark data sets are commonly used in speech and image recognition, but so far none are available for general context recognition tasks. We outline the experimental considerations and procedures used to record the data in a controlled manner, observing strict experimental standards. We then discuss preliminary results obtained with common features on a well-understood scenario with 8 test subjects. The discussion shows that even for a small sample like this variations between subjects are substantial, thus underscoring the need for large representative data sets.

本文言語English
ホスト出版物のタイトルLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
編集者Emile Aarts, Rene Collier, Evert van Loenen, Boris de Ruyter
出版社Springer Verlag
ページ252-263
ページ数12
ISBN(印刷版)3540204180
DOI
出版ステータスPublished - 2003
外部発表はい

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
2875
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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