An Intelligent Optimization Strategy Based on Deep Reinforcement Learning for Step Counting
With the popularity of Internet of things technology and intelligent devices, the application prospect of accurate step counting has gained more and more attention. To solve the problems that the existing algorithms use threshold to filter noise, and the parameters cannot be updated in time, an inte...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2021/9536309 |
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| _version_ | 1832557135180857344 |
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| author | Zhoubao Sun Pengfei Chen Xiaodong Zhang |
| author_facet | Zhoubao Sun Pengfei Chen Xiaodong Zhang |
| author_sort | Zhoubao Sun |
| collection | DOAJ |
| description | With the popularity of Internet of things technology and intelligent devices, the application prospect of accurate step counting has gained more and more attention. To solve the problems that the existing algorithms use threshold to filter noise, and the parameters cannot be updated in time, an intelligent optimization strategy based on deep reinforcement learning is proposed. In this study, the counting problem is transformed into a serialization decision optimization. This study integrates the noise recognition and the user feedback to update parameters. The end-to-end processing is direct, which alleviates the inaccuracy of step counting in the follow-up step counting module caused by the inaccuracy of noise filtering in the two-stage processing and makes the model parameters continuously updated. Finally, the experimental results show that the proposed model achieves superior performance to existing approaches. |
| format | Article |
| id | doaj-art-eb6d8d88bf014d1492901855b4c94053 |
| institution | Kabale University |
| issn | 1607-887X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-eb6d8d88bf014d1492901855b4c940532025-02-03T05:43:34ZengWileyDiscrete Dynamics in Nature and Society1607-887X2021-01-01202110.1155/2021/9536309An Intelligent Optimization Strategy Based on Deep Reinforcement Learning for Step CountingZhoubao Sun0Pengfei Chen1Xiaodong Zhang2Jiangsu Key Laboratory of Public Project AuditDepartment of EconomicsJiangsu Key Laboratory of Public Project AuditWith the popularity of Internet of things technology and intelligent devices, the application prospect of accurate step counting has gained more and more attention. To solve the problems that the existing algorithms use threshold to filter noise, and the parameters cannot be updated in time, an intelligent optimization strategy based on deep reinforcement learning is proposed. In this study, the counting problem is transformed into a serialization decision optimization. This study integrates the noise recognition and the user feedback to update parameters. The end-to-end processing is direct, which alleviates the inaccuracy of step counting in the follow-up step counting module caused by the inaccuracy of noise filtering in the two-stage processing and makes the model parameters continuously updated. Finally, the experimental results show that the proposed model achieves superior performance to existing approaches.http://dx.doi.org/10.1155/2021/9536309 |
| spellingShingle | Zhoubao Sun Pengfei Chen Xiaodong Zhang An Intelligent Optimization Strategy Based on Deep Reinforcement Learning for Step Counting Discrete Dynamics in Nature and Society |
| title | An Intelligent Optimization Strategy Based on Deep Reinforcement Learning for Step Counting |
| title_full | An Intelligent Optimization Strategy Based on Deep Reinforcement Learning for Step Counting |
| title_fullStr | An Intelligent Optimization Strategy Based on Deep Reinforcement Learning for Step Counting |
| title_full_unstemmed | An Intelligent Optimization Strategy Based on Deep Reinforcement Learning for Step Counting |
| title_short | An Intelligent Optimization Strategy Based on Deep Reinforcement Learning for Step Counting |
| title_sort | intelligent optimization strategy based on deep reinforcement learning for step counting |
| url | http://dx.doi.org/10.1155/2021/9536309 |
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