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Human activity recognition using multi-input CNN model with..:
, In:
Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers
,
Yaguchi, Kei
;
Ikarigawa, Kazukiyo
;
Kawasaki, Ryo
... - p. 364-367 , 2020
Link:
https://dl.acm.org/doi/10.1145/3410530.3414342
RT T1
Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers
: T1
Human activity recognition using multi-input CNN model with FFT spectrograms
UL https://suche.suub.uni-bremen.de/peid=acm-3414342&Exemplar=1&LAN=DE A1 Yaguchi, Kei A1 Ikarigawa, Kazukiyo A1 Kawasaki, Ryo A1 Miyazaki, Wataru A1 Morikawa, Yuki A1 Ito, Chihiro A1 Shuzo, Masaki A1 Maeda, Eisaku PB ACM YR 2020 K1 CNN K1 FFT spectrogram K1 SHL dataset K1 human activity recognition K1 Computing methodologies K1 Machine learning K1 Human-centered computing K1 Human computer interaction (HCI) K1 HCI design and evaluation methods K1 User models SP 364 OP 367 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3410530.3414342 DO https://dl.acm.org/doi/10.1145/3410530.3414342 SF ELIB - SuUB Bremen
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