SERVICE DESIGN WITH MACHINE LEARNING BASED ON USER ACTION HISTORY
With the development of IoT techniques, it become easier to collect users’ action data. By analyzing and using those data, consumers and producers will mutually exchange their intelligence and better customize product development processes. This study focuses on the users’ daily life, examines a p...
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| Format: | Article |
| Language: | English |
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Sciendo
2020-08-01
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| Series: | Acta Electrotechnica et Informatica |
| Subjects: | |
| Online Access: | http://www.aei.tuke.sk/papers/2020/2/02_Wang.pdf |
| _version_ | 1797760040954757120 |
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| author | Xinyue WANG Nobutada FUJII Toshiya KAIHARA Daisuke KOKURYO |
| author_facet | Xinyue WANG Nobutada FUJII Toshiya KAIHARA Daisuke KOKURYO |
| author_sort | Xinyue WANG |
| collection | DOAJ |
| description | With the development of IoT techniques, it become easier to collect users’ action data. By analyzing and using those data,
consumers and producers will mutually exchange their intelligence and better customize product development processes. This study
focuses on the users’ daily life, examines a proposed system using sensor shoes with several sensor devices embedded in the insoles,
collecting action data of users, extracting their action features, and then issuing some advice to help users train more efficiently. As
described herein, a service model uses a backpropagation (BP) network to distinguish users' actions and to extract their action
features using Self-organization Map from the presented sensing data. Experiments to confirm the feasibility of the proposed
methods are undertaken. With the former result indicating an overall accuracy of 89.61% in distinction of 5 actions: sit, stand, walk,
run and jump. The latter results showing that SOM is helpful to classify action feature in detail. After the analysing parameters of
each cluster numbers, it is possible to make the action feature visualized by providing a colored cluster map, which makes it easier to
compare train periods. Results also show the potential of utilization of data collected by devices to provide personal service. |
| first_indexed | 2024-03-12T18:53:03Z |
| format | Article |
| id | doaj.art-e6811195051f44c5af05a0218feff188 |
| institution | Directory Open Access Journal |
| issn | 1335-8243 1338-3957 |
| language | English |
| last_indexed | 2024-03-12T18:53:03Z |
| publishDate | 2020-08-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Acta Electrotechnica et Informatica |
| spelling | doaj.art-e6811195051f44c5af05a0218feff1882023-08-02T07:04:29ZengSciendoActa Electrotechnica et Informatica1335-82431338-39572020-08-01202111810.15546/aeei-2020-0008SERVICE DESIGN WITH MACHINE LEARNING BASED ON USER ACTION HISTORYXinyue WANG0Nobutada FUJII1 Toshiya KAIHARA2Daisuke KOKURYO3Department of System Science, Graduate School of System Informatics, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe, Japan, Department of System Science, Graduate School of System Informatics, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe, Japan, Department of System Science, Graduate School of System Informatics, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe, Japan, Department of System Science, Graduate School of System Informatics, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe, Japan, With the development of IoT techniques, it become easier to collect users’ action data. By analyzing and using those data, consumers and producers will mutually exchange their intelligence and better customize product development processes. This study focuses on the users’ daily life, examines a proposed system using sensor shoes with several sensor devices embedded in the insoles, collecting action data of users, extracting their action features, and then issuing some advice to help users train more efficiently. As described herein, a service model uses a backpropagation (BP) network to distinguish users' actions and to extract their action features using Self-organization Map from the presented sensing data. Experiments to confirm the feasibility of the proposed methods are undertaken. With the former result indicating an overall accuracy of 89.61% in distinction of 5 actions: sit, stand, walk, run and jump. The latter results showing that SOM is helpful to classify action feature in detail. After the analysing parameters of each cluster numbers, it is possible to make the action feature visualized by providing a colored cluster map, which makes it easier to compare train periods. Results also show the potential of utilization of data collected by devices to provide personal service.http://www.aei.tuke.sk/papers/2020/2/02_Wang.pdfneural networkself-organizing map |
| spellingShingle | Xinyue WANG Nobutada FUJII Toshiya KAIHARA Daisuke KOKURYO SERVICE DESIGN WITH MACHINE LEARNING BASED ON USER ACTION HISTORY Acta Electrotechnica et Informatica neural network self-organizing map |
| title | SERVICE DESIGN WITH MACHINE LEARNING BASED ON USER ACTION HISTORY |
| title_full | SERVICE DESIGN WITH MACHINE LEARNING BASED ON USER ACTION HISTORY |
| title_fullStr | SERVICE DESIGN WITH MACHINE LEARNING BASED ON USER ACTION HISTORY |
| title_full_unstemmed | SERVICE DESIGN WITH MACHINE LEARNING BASED ON USER ACTION HISTORY |
| title_short | SERVICE DESIGN WITH MACHINE LEARNING BASED ON USER ACTION HISTORY |
| title_sort | service design with machine learning based on user action history |
| topic | neural network self-organizing map |
| url | http://www.aei.tuke.sk/papers/2020/2/02_Wang.pdf |
| work_keys_str_mv | AT xinyuewang servicedesignwithmachinelearningbasedonuseractionhistory AT nobutadafujii servicedesignwithmachinelearningbasedonuseractionhistory AT toshiyakaihara servicedesignwithmachinelearningbasedonuseractionhistory AT daisukekokuryo servicedesignwithmachinelearningbasedonuseractionhistory |