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- Murao Kazuya
- College of Information Science and Engineering, Ritsumeikan University
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- Terada Tsutomu
- Graduate School of Engineering, Kobe University Japan Science and Technology Agency, PRESTO
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抄録
Many activity recognition systems using accelerometers have been proposed. Activities that have been recognized are single activities which can be expressed with one verb, such as sitting, walking, holding a mobile phone, and throwing a ball. In fact, combined activities that include more than two kinds of state and movement are often taking place. Focusing on hand gestures, they are performed not only while standing, but also while walking and sitting. Though the simplest way to recognize such combined activities is to construct the recognition models for all the possible combinations of the activities, the number of combinations becomes immense. In this paper, firstly we propose a method that classifies activities into postures (e.g., sitting), behaviors (e.g., walking), and gestures (e.g., a punch) by using the autocorrelation of the acceleration values. Postures and behaviors are states lasting for a certain length of time. Gestures, however, are sporadic or once-off actions. It has been a challenging task to find gestures buried in other activities. Then, by utilizing the technique, we propose a recognition method for combined activities by learning single activities only. Evaluation results confirmed that our proposed method achieved 0.84 recall and 0.86 precision, which is comparable to the method that had learned all the combined activities.
収録刊行物
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- Journal of Information Processing
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Journal of Information Processing 24 (3), 512-521, 2016
一般社団法人 情報処理学会
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詳細情報 詳細情報について
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- CRID
- 1390001205294921472
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- NII論文ID
- 130005151524
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- NII書誌ID
- AA00700121
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- ISSN
- 18826652
- 03876101
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- HANDLE
- 20.500.14094/90005163
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- IRDB
- Crossref
- CiNii Articles
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- 使用不可