| Presentation | 2020-11-27 Detection of human activity based on hybrid deep learning model using a low-resolution infrared array sensor. Muthukumar K A, Mondher Bouazizi, Tomoaki Ohtsuki, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Artificial Intelligence (AI) plays a significant role in the healthcare industry. Many applications have been developed using AI in healthcare. Among these, activity detection is one of the most important applications. Many AI-based activity detection systems use conventional machine learning methods to detect various activities. In a conventional machine learning model, activity features are manually extracted and detected which presents one the main drawbacks of this family of techniques.. This report proposes an activity detection approach based on a hybrid deep learning model using a low-resolution infrared array sensor placed on the ceiling. The hybrid deep learning model automatically learns the features and detect the activity. Upon training, the classification is performed faster than that using conventional machine learning models.. The data collected from the infrared array sensor is classified using a CNN (Convolutional Neural Network) where each frame is individually classified. The CNN’s output is passed to the LSTM (Long Short Term Memory) for sequential classification with a time window size equal to five frames. The classification accuracy reach 96.60% and 97.74% for the CNN and the CNN+LSTM models, respectively, respectively. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | AI healthcareactivity detectionhybdrid deep learing |
| Paper # | SeMI2020-39 |
| Date of Issue | 2020-11-19 (SeMI) |
| Conference Information | |
| Committee | SRW / SeMI / CNR |
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| Conference Date | 2020/11/26(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | IoT Workshop |
| Chair | Satoshi Denno(Okayama Univ.) / Susumu Ishihara(Shizuoka Univ.) / Kazunori Takashio(Keio Univ.) |
| Vice Chair | Keiichi Mizutani(Kyoto Univ.) / Kentaro Saito(Tokyo Inst. of Tech.) / Hanako Noda(Anritsu) / Kazuya Monden(Hitachi) / Koji Yamamoto(Kyoto Univ.) / Masayuki Kanbara(NAIST) / Yoshihiko Murakawa(Fujitsu Labs.) |
| Secretary | Keiichi Mizutani(NTT) / Kentaro Saito(NIigata Univ.) / Hanako Noda(Kyoto Univ.) / Kazuya Monden(Osaka Univ.) / Koji Yamamoto(Hitachi) / Masayuki Kanbara(Waseda Univ.) / Yoshihiko Murakawa(Shibaura Inst. of Tech.) |
| Assistant | Masaaki Fuse(Anritsu) / Akihito Noda(Nanzan Univ.) / Yuki Katsumata(NTT DOCOMO) / Yu Nakayama(Tokyo Univ. of Agri. and Tech.) / Akira Uchiyama(Osaka Univ.) / Yuka Kobayashi(Toshiba) / Masanori Yokoyama(ＮＴＴ) |
| Paper Information | |
| Registration To | Technical Committee on Short Range Wireless Communications / Technical Committee on Sensor Network and Mobile Intelligence / Technical Committee on Cloud Network Robotics |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Detection of human activity based on hybrid deep learning model using a low-resolution infrared array sensor. |
| Sub Title (in English) | |
| Keyword(1) | AI healthcareactivity detectionhybdrid deep learing |
| 1st Author's Name | Muthukumar K A |
| 1st Author's Affiliation | Keio University(Keio Univ.) |
| 2nd Author's Name | Mondher Bouazizi |
| 2nd Author's Affiliation | Keio University(Keio Univ.) |
| 3rd Author's Name | Tomoaki Ohtsuki |
| 3rd Author's Affiliation | Keio University(Keio Univ.) |
| Date | 2020-11-27 |
| Paper # | SeMI2020-39 |
| Volume (vol) | vol.120 |
| Number (no) | SeMI-261 |
| Page | pp.pp.99-104(SeMI), |
| #Pages | 6 |
| Date of Issue | 2020-11-19 (SeMI) |