User-Independent Motion and Location Analysis for Sussex-Huawei Locomotion Data
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hwang, Sungjin | - |
dc.contributor.author | Cho, Youngwug | - |
dc.contributor.author | Kim, Kwanguk | - |
dc.date.accessioned | 2023-11-24T03:08:18Z | - |
dc.date.available | 2023-11-24T03:08:18Z | - |
dc.date.created | 2023-11-14 | - |
dc.date.issued | 2023-10 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/192906 | - |
dc.description.abstract | Transportation mode detection (TMD) is a context-aware computing technology with significant potential in several applications. However, the development of TMD technologies for real-world scenarios remains challenging, including user-independent evaluations and multimodal analyses. In this study, our team (HYU-CSE) suggested a TMD model as part of the Sussex-Huawei Locomotion (SHL) recognition challenge, and we used the SHL motion and location data. The proposed TMD model was based on the DenseNet architecture, and post-processing using voting schemes was applied to refine the detection performance. The results suggested that the proposed method achieved 94.13% of an F1 score with user-independent analysis. We hope that our study will ultimately help in the design of better TMD applications. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Association for Computing Machinery, Inc | - |
dc.title | User-Independent Motion and Location Analysis for Sussex-Huawei Locomotion Data | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Kwanguk | - |
dc.identifier.doi | 10.1145/3594739.3610748 | - |
dc.identifier.scopusid | 2-s2.0-85175484353 | - |
dc.identifier.bibliographicCitation | UbiComp/ISWC 2023 Adjunct - Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2023 ACM International Symposium on Wearable Computing, pp.517 - 522 | - |
dc.relation.isPartOf | UbiComp/ISWC 2023 Adjunct - Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2023 ACM International Symposium on Wearable Computing | - |
dc.citation.title | UbiComp/ISWC 2023 Adjunct - Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2023 ACM International Symposium on Wearable Computing | - |
dc.citation.startPage | 517 | - |
dc.citation.endPage | 522 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Detection models | - |
dc.subject.keywordPlus | Independent motions | - |
dc.subject.keywordPlus | Location analysis | - |
dc.subject.keywordPlus | Mode detection | - |
dc.subject.keywordPlus | Smart phones | - |
dc.subject.keywordPlus | Smartphone sensor | - |
dc.subject.keywordPlus | Transportation mode | - |
dc.subject.keywordPlus | Transportation mode detection | - |
dc.subject.keywordPlus | User independents | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Smartphone sensors | - |
dc.subject.keywordAuthor | Transportation mode detection | - |
dc.identifier.url | https://dl.acm.org/doi/10.1145/3594739.3610748 | - |
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