DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yun, Myeongji | - |
dc.contributor.author | Hong, Seungwoo | - |
dc.contributor.author | Yoo, Sunwoo | - |
dc.contributor.author | Kim, Junho | - |
dc.contributor.author | Park, Sung-Min | - |
dc.contributor.author | Lee, Youngjoo | - |
dc.date.accessioned | 2023-03-02T04:21:36Z | - |
dc.date.available | 2023-03-02T04:21:36Z | - |
dc.date.created | 2023-03-02 | - |
dc.date.issued | 2022-06-14 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/116150 | - |
dc.description.abstract | In this paper, we propose a novel end-to-end stress recognition model by combining binarized convolutional neural network (CNN) and long short-term memory (LSTM) models. Based on the previous CNN-LSTM model using electrocardiogram (ECG) and respiration (RESP) signals, we newly apply the bandit-based hyperparameter optimization to find more accurate solutions. Analyzing the computational costs of the accuracy-aware model, we also introduce advanced memory-reduction techniques with downscaling and binarization for realizing the cost-efficient stress recognition solution. As a result, compared to the state-of-the-art methods, the proposed model reduces the memory size, the inference latency, and the energy consumption by 93 %, 39 %, and 42 %, respectively, while even increasing the recognition accuracy up to 87%. | - |
dc.language | English | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.relation.isPartOf | 4th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022 | - |
dc.relation.isPartOf | Proceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022 | - |
dc.title | Lightweight End-to-End Stress Recognition using Binarized CNN-LSTM Models | - |
dc.type | Conference | - |
dc.type.rims | CONF | - |
dc.identifier.bibliographicCitation | 4th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022, pp.270 - 273 | - |
dc.identifier.wosid | 000859273200069 | - |
dc.citation.conferenceDate | 2022-06-13 | - |
dc.citation.conferencePlace | KO | - |
dc.citation.endPage | 273 | - |
dc.citation.startPage | 270 | - |
dc.citation.title | 4th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022 | - |
dc.contributor.affiliatedAuthor | Lee, Youngjoo | - |
dc.identifier.scopusid | 2-s2.0-85139022589 | - |
dc.description.journalClass | 2 | - |
dc.description.journalClass | 2 | - |
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