| Presentation | 2020-01-10 Prediction method of tumble using machine learning of footsteps. Takehiro Mori, Hiroyuki Nishi, Manabu Okamoto, |
|---|---|
| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | With the progress of super-aged society, the number of elderlies living alone has increased, and the number of falls in the home has also increased. In order to improve these situations, we are studying a walking sound identification method that predicts walking movements using elderly walking sounds and prevents falls. In this study, we focus on the fact that elderly people tend to walk on sliding feet as a pre-step to fall. The fact that the acoustic features of walking sound are greatly different between normal walking and sliding feet is used. After the two are identified using a neural network, if sliding feet is detected, the elderlies are warned or alerted to prevent falls. As an evaluation of the discrimination method, we recorded normal walking sounds from barefoot, slippers, and socks, and walking sounds from the normal and sliding footsteps. The walking sound of multiple people was used. We examined the effects of the learning method on the discrimination performance, such as changes in performance depending on whether footwear and walking people are distinguished as learning categories, and performance when evaluating walking people that were not used for learning. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Elderly people / Observation / Neural network / tumble |
| Paper # | ICM2019-36,LOIS2019-51 |
| Date of Issue | 2020-01-02 (ICM, LOIS) |
| Conference Information | |
| Committee | LOIS / ICM |
|---|---|
| Conference Date | 2020/1/9(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | ARKAS SASEBO |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Practical Use of Lifelog, Office Information System, Business Management, etc. |
| Chair | Tomohiro Yamada(NEL) / Kiyohito Yoshihara(KDDI Research) |
| Vice Chair | Toru Kobayashi(Nagasaki Univ.) / Takumi Miyoshi(Shibaura Inst. of Tech.) / Yoichi Sato(Open Systems Laboratory) |
| Secretary | Toru Kobayashi(Research Organization of Information and Systems) / Takumi Miyoshi(NTT) / Yoichi Sato(NTT) |
| Assistant | Kenichi Arai(Nagasaki Univ.) / Hiroki Nakayama(Bosco) |
| Paper Information | |
| Registration To | Technical Committee on Life Intelligence and Office Information Systems / Technical Committee on Information and Communication Management |
|---|---|
| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Prediction method of tumble using machine learning of footsteps. |
| Sub Title (in English) | Evaluation results that increased the number of subjects. |
| Keyword(1) | Elderly people |
| Keyword(2) | Observation |
| Keyword(3) | Neural network |
| Keyword(4) | tumble |
| 1st Author's Name | Takehiro Mori |
| 1st Author's Affiliation | Sojo University(Sojo Univ.) |
| 2nd Author's Name | Hiroyuki Nishi |
| 2nd Author's Affiliation | Sojo University(Sojo Univ.) |
| 3rd Author's Name | Manabu Okamoto |
| 3rd Author's Affiliation | Sojo University(Sojo Univ.) |
| Date | 2020-01-10 |
| Paper # | ICM2019-36,LOIS2019-51 |
| Volume (vol) | vol.119 |
| Number (no) | ICM-358,LOIS-359 |
| Page | pp.pp.33-37(ICM), pp.33-37(LOIS), |
| #Pages | 5 |
| Date of Issue | 2020-01-02 (ICM, LOIS) |