| DC Field | Value | Language |
|---|
| dc.contributor.author | Chen, Z. Y. | - |
| dc.contributor.author | Yang, Z. X. | - |
| dc.contributor.author | Li, H. | - |
| dc.coverage.spatial | Минск | ru_RU |
| dc.date.accessioned | 2023-06-12T12:24:55Z | - |
| dc.date.available | 2023-06-12T12:24:55Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.citation | Chen, Z. Y. Human physical activity recognition algorithm based on smartphone data and long short time memory neural network / Chen Z. Y., Yang Z. X., Li H. // Информационная безопасность : сборник материалов 59-й научной конференции аспирантов, магистрантов и студентов БГУИР, Минск, 17–21 апреля 2023 г. / Белорусский государственный университет информатики и радиоэлектроники. – Минск, 2023. – С. 160–162. | ru_RU |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/51955 | - |
| dc.description.abstract | The continuous advancement of smartphone sensors has brought more opportunities for the universal application of human motion recognition technology. Based on the data of the mobile phone's three-axis acceleration sensor, using combining a double-layer Long Short Time Memory (LSTM) and full connected layers allow us to improve human actions recognition accuracy, including walking, jogging, sitting, standing, and going up and down stairs. This is helpful for smart assistive technology. It is shown that physical activity classification accuracy is equal to 98.4 %. | ru_RU |
| dc.language.iso | en | ru_RU |
| dc.publisher | БГУИР | ru_RU |
| dc.subject | материалы конференций | ru_RU |
| dc.subject | Mobile acceleration sensor | ru_RU |
| dc.subject | long short time memory | ru_RU |
| dc.subject | action recognition | ru_RU |
| dc.title | Human physical activity recognition algorithm based on smartphone data and long short time memory neural network | ru_RU |
| dc.type | Article | ru_RU |
| Appears in Collections: | Информационная безопасность : материалы 59-й научной конференции аспирантов, магистрантов и студентов (2023)
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