Robust Human Activity Recognition by Integrating Image and Accelerometer Sensor Data Using Deep Fusion Network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kang, Junhyuk | - |
dc.contributor.author | Shin, Jieun | - |
dc.contributor.author | Shin, Jaewon | - |
dc.contributor.author | Lee, Daeho | - |
dc.contributor.author | Choi, Ahyoung | - |
dc.date.accessioned | 2022-02-12T01:40:31Z | - |
dc.date.available | 2022-02-12T01:40:31Z | - |
dc.date.created | 2022-01-19 | - |
dc.date.issued | 2022-01 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/83473 | - |
dc.description.abstract | Studies on deep-learning-based behavioral pattern recognition have recently received considerable attention. However, if there are insufficient data and the activity to be identified is changed, a robust deep learning model cannot be created. This work contributes a generalized deep learning model that is robust to noise not dependent on input signals by extracting features through a deep learning model for each heterogeneous input signal that can maintain performance while minimizing preprocessing of the input signal. We propose a hybrid deep learning model that takes heterogeneous sensor data, an acceleration sensor, and an image as inputs. For accelerometer data, we use a convolutional neural network (CNN) and convolutional block attention module models (CBAM), and apply bidirectional long short-term memory and a residual neural network. The overall accuracy was 94.8% with a skeleton image and accelerometer data, and 93.1% with a skeleton image, coordinates, and accelerometer data after evaluating nine behaviors using the Berkeley Multimodal Human Action Database (MHAD). Furthermore, the accuracy of the investigation was revealed to be 93.4% with inverted images and 93.2% with white noise added to the accelerometer data. Testing with data that included inversion and noise data indicated that the suggested model was robust, with a performance deterioration of approximately 1%. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.relation.isPartOf | Sensors | - |
dc.title | Robust Human Activity Recognition by Integrating Image and Accelerometer Sensor Data Using Deep Fusion Network | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000751291200001 | - |
dc.identifier.doi | 10.3390/s22010174 | - |
dc.identifier.bibliographicCitation | Sensors, v.22, no.1 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85121686689 | - |
dc.citation.title | Sensors | - |
dc.citation.volume | 22 | - |
dc.citation.number | 1 | - |
dc.contributor.affiliatedAuthor | Kang, Junhyuk | - |
dc.contributor.affiliatedAuthor | Shin, Jieun | - |
dc.contributor.affiliatedAuthor | Shin, Jaewon | - |
dc.contributor.affiliatedAuthor | Lee, Daeho | - |
dc.contributor.affiliatedAuthor | Choi, Ahyoung | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Accelerometer sensors | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Fusion network | - |
dc.subject.keywordAuthor | Human activity recognition | - |
dc.subject.keywordAuthor | Skeleton detection | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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