| Presentation | 2014-08-01 A Fall Detection System Using Low Resolution Infrared Array Sensor Shota MASHIYAMA, Jihoon HONG, Tomoaki OHTSUKI, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Nowadays, aging society is a big problem and demand for monitoring systems is becoming higher. Under this circumstance, a fall is a main factor of accidents at home. From this point of view, we need to detect falls expeditiously and correctly. However, usual methods like using a video camera or a wearable device have some issues in privacy and convenience. In this report, we propose a system of fall detection using a low resolution infrared array sensor. The proposed system uses this sensor with advantages of privacy protection (low resolution), low cost (cheap sensor), and convenience (small device). We propose four features and based on them, classify activities as either a fall or a non-fall. We show a proof-of-concept of our proposed system using a commercial-off-the-shelf (COTS) hardware. Results of experiments show the detection rate of higher than 94 % irrespective of training data contains object's data or not. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Infrared Array Sensor / Fall Detection / Privacy / Supervised Learning / k-nearest neighbor algorithm |
| Paper # | ASN2014-81 |
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| Conference Information | |
| Committee | ASN |
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| Conference Date | 2014/7/23(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Ambient intelligence and Sensor Networks(ASN) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Fall Detection System Using Low Resolution Infrared Array Sensor |
| Sub Title (in English) | |
| Keyword(1) | Infrared Array Sensor |
| Keyword(2) | Fall Detection |
| Keyword(3) | Privacy |
| Keyword(4) | Supervised Learning |
| Keyword(5) | k-nearest neighbor algorithm |
| 1st Author's Name | Shota MASHIYAMA |
| 1st Author's Affiliation | Graduate School of Science and Technology, Keio University() |
| 2nd Author's Name | Jihoon HONG |
| 2nd Author's Affiliation | Graduate School of Science and Technology, Keio University |
| 3rd Author's Name | Tomoaki OHTSUKI |
| 3rd Author's Affiliation | Department of Information and Computer science, Keio University |
| Date | 2014-08-01 |
| Paper # | ASN2014-81 |
| Volume (vol) | vol.114 |
| Number (no) | 166 |
| Page | pp.pp.- |
| #Pages | 6 |
| Date of Issue | |