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DeepVehicleSense: An Energy-efficient Transportation Mode Recognition Leveraging Staged Deep Learning over Sound Samples
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Sungyong | - |
dc.contributor.author | Lee, Jinsung | - |
dc.contributor.author | Lee, Kyunghan | - |
dc.date.accessioned | 2022-06-24T08:27:32Z | - |
dc.date.available | 2022-06-24T08:27:32Z | - |
dc.date.created | 2022-05-19 | - |
dc.date.created | 2022-05-19 | - |
dc.date.created | 2022-05-19 | - |
dc.date.created | 2022-05-19 | - |
dc.date.created | 2022-05-19 | - |
dc.date.created | 2022-05-19 | - |
dc.date.created | 2022-05-19 | - |
dc.date.created | 2022-05-19 | - |
dc.date.issued | 2023-06 | - |
dc.identifier.citation | IEEE Transactions on Mobile Computing, Vol.22 No.6, pp.3270-3286 | - |
dc.identifier.issn | 1536-1233 | - |
dc.identifier.uri | https://hdl.handle.net/10371/184086 | - |
dc.description.abstract | IEEEIn this paper, we present a new transportation mode recognition system for smartphones called DeepVehicleSense, which is widely applicable to mobile context-aware services. DeepVehicleSense aims at achieving three performance objectives: high accuracy, low latency, and low power consumption at once by exploiting sound characteristics captured from the built-in microphone while being on candidate transportations. To attain high energy efficiency, DeepVehicleSense adopts hierarchical accelerometer-based triggers that minimize the activation of the microphone of smartphones. Further, to achieve high accuracy and low latency, DeepVehicleSense makes use of non-linear filters that can best extract the transportation sound samples. For recognition of five different transportation modes, we design a deep learning based sound classifier using a novel deep neural network architecture with multiple branches. Our staged inference technique can significantly reduce runtime and energy consumption while maintaining high accuracy for the majority of samples. Through 263-hour datasets collected by seven different Android phone models, we demonstrate that DeepVehicleSense achieves the recognition accuracy of 97.44\% with only sound samples of 2 seconds at the power consumption of 35.08 mW on average for all-day monitoring. | - |
dc.language | 영어 | - |
dc.publisher | Institute of Electrical and Electronics Engineers | - |
dc.title | DeepVehicleSense: An Energy-efficient Transportation Mode Recognition Leveraging Staged Deep Learning over Sound Samples | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/TMC.2022.3141392 | - |
dc.citation.journaltitle | IEEE Transactions on Mobile Computing | - |
dc.identifier.wosid | 001020877300011 | - |
dc.identifier.scopusid | 2-s2.0-85122876071 | - |
dc.citation.endpage | 3286 | - |
dc.citation.number | 6 | - |
dc.citation.startpage | 3270 | - |
dc.citation.volume | 22 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Lee, Kyunghan | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.subject.keywordAuthor | activity recognition | - |
dc.subject.keywordAuthor | Context-aware computing | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | low power | - |
dc.subject.keywordAuthor | sound data | - |
dc.subject.keywordAuthor | staged inference | - |
dc.subject.keywordAuthor | transportation mode | - |
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