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Hokkaido University Collection of Scholarly and Academic Papers >
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Peer-reviewed Journal Articles, etc >
Privacy-Preserved Behavior Analysis and Fall Detection by an Infrared Ceiling Sensor Network
This item is licensed under:Creative Commons Attribution 3.0 Unported
| Title: | Privacy-Preserved Behavior Analysis and Fall Detection by an Infrared Ceiling Sensor Network |
| Authors: | Tao, Shuai Browse this author | | Kudo, Mineichi Browse this author →KAKEN DB | | Nonaka, Hidetoshi Browse this author →KAKEN DB |
| Keywords: | behavior analysis | | fall detection | | privacy-preserved | | ceiling sensor network | | infrared sensors |
| Issue Date: | Dec-2012 |
| Publisher: | MDPI |
| Journal Title: | Sensors |
| Volume: | 12 |
| Issue: | 12 |
| Start Page: | 16920 |
| End Page: | 16936 |
| Publisher DOI: | 10.3390/s121216920 |
| Abstract: | An infrared ceiling sensor network system is reported in this study to realize behavior analysis and fall detection of a single person in the home environment. The sensors output multiple binary sequences from which we know the existence/non-existence of persons under the sensors. The short duration averages of the binary responses are shown to be able to be regarded as pixel values of a top-view camera, but more advantageous in the sense of preserving privacy. Using the "pixel values" as features, support vector machine classifiers succeeded in recognizing eight activities (walking, reading, etc.) performed by five subjects at an average recognition rate of 80.65%. In addition, we proposed a martingale framework for detecting falls in this system. The experimental results showed that we attained the best performance of 95.14% (F1 value), the FAR of 7.5% and the FRR of 2.0%. This accuracy is not sufficient in general but surprisingly high with such low-level information. In summary, it is shown that this system has the potential to be used in the home environment to provide personalized services and to detect abnormalities of elders who live alone. |
| Rights: | http://creativecommons.org/licenses/by/3.0/ |
| Type: | article |
| URI: | http://hdl.handle.net/2115/51701 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: 陶 帥
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