| DC Field | Value | Language |
|---|
| dc.contributor.author | Xueying, Y. | - |
| dc.date.accessioned | 2022-05-14T12:51:44Z | - |
| dc.date.available | 2022-05-14T12:51:44Z | - |
| dc.date.issued | 2022 | - |
| dc.identifier.citation | Xueying, Y. Human activity recognition based on random forest / Y. Xueying // Технологии передачи и обработки информации : материалы международного научно-технического семинара, Минск, март-апрель 2022 г. / Белорусский государственный университет информатики и радиоэлектроники. – Минск, 2022. – С. 77–81. | ru_RU |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/46957 | - |
| dc.description.abstract | With the improvement of pattern recognition algorithm, human activity recognition (HAR) based on smart phone sensor data has become a highly concerned and rapidly developing research field. According to the basic principle of HAR, this paper proposes an activity recognition system based on random forest classifier. Data collection, data preprocessing and feature extraction are specified. The random forest classifier is summarized. The accuracy rate, TPR, FNR, PPV and FNR were used to evaluate HAR system proposed in this paper. Experimental results show that this study can accurately distinguish six basic activities, and the average accuracy of the random forest algorithm is 94,6 %. | ru_RU |
| dc.language.iso | en | ru_RU |
| dc.publisher | БГУИР | ru_RU |
| dc.subject | материалы конференций | ru_RU |
| dc.subject | human activity recognition | ru_RU |
| dc.subject | classifier | ru_RU |
| dc.subject | random forest algorithm | ru_RU |
| dc.title | Human activity recognition based on random forest | ru_RU |
| dc.type | Статья | ru_RU |
| Appears in Collections: | Технологии передачи и обработки информации : материалы международного научно-технического семинара (2022)
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