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A Combined-activity Recognition Method with Accelerometers
https://ipsj.ixsq.nii.ac.jp/records/160383
https://ipsj.ixsq.nii.ac.jp/records/160383039f3da4-f525-405a-8f2c-85b94c48210f
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
IPSJ-JNL5705020.pdf (601.3 kB)
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Copyright (c) 2016 by the Information Processing Society of Japan
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| オープンアクセス | ||
| Item type | Journal(1) | |||||||||
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| 公開日 | 2016-05-15 | |||||||||
| タイトル | ||||||||||
| タイトル | A Combined-activity Recognition Method with Accelerometers | |||||||||
| タイトル | ||||||||||
| 言語 | en | |||||||||
| タイトル | A Combined-activity Recognition Method with Accelerometers | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | [一般論文] activity recognition, combined activities, wearable computing, accelerometers | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||
| 資源タイプ | journal article | |||||||||
| 著者所属 | ||||||||||
| College of Information Science and Engineering, Ritsumeikan University | ||||||||||
| 著者所属 | ||||||||||
| Graduate School of Engineering, Kobe University / Japan Science and Technology Agency, PRESTO | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| College of Information Science and Engineering, Ritsumeikan University | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Graduate School of Engineering, Kobe University / Japan Science and Technology Agency, PRESTO | ||||||||||
| 著者名 |
Kazuya, Murao
× Kazuya, Murao
× Tsutomu, Terada
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| 著者名(英) |
Kazuya, Murao
× Kazuya, Murao
× Tsutomu, Terada
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| 論文抄録 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | 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. \n------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.24(2016) No.3 (online) DOI http://dx.doi.org/10.2197/ipsjjip.24.512 ------------------------------ |
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| 論文抄録(英) | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | 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. \n------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.24(2016) No.3 (online) DOI http://dx.doi.org/10.2197/ipsjjip.24.512 ------------------------------ |
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| 書誌レコードID | ||||||||||
| 収録物識別子タイプ | NCID | |||||||||
| 収録物識別子 | AN00116647 | |||||||||
| 書誌情報 |
情報処理学会論文誌 巻 57, 号 5, 発行日 2016-05-15 |
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| ISSN | ||||||||||
| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 1882-7764 | |||||||||
