Cited by 65Context-Aware Activity Recognition SystemsGait Recognition and AnalysisTime Series Analysis and Forecasting

A hybrid deep convolutional and recurrent neural network for complex activity recognition using multimodal sensors

Lv, Mingqi, Xu, Wei, Chen, Tieming·2019·Neurocomputing

Integrity signals

Registry

Crossref · 10.1016/j.neucom.2019.06.051

Cites

34 of 34 references verified0 citation errors · 0 not checkable

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Claims other papers make about this one

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  • Lv et al. built a CNN + RNN model with stacked multisensor data in each channel for fusion before feeding into the CNN layer.

    “Unlike previous models with one-dimension time-series input, Lv et al. [192] builds a CNN + RNN model with stacked multisensor data in each channel for fusion before feeding into the CNN layer.”

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Authors
Lv, M., Xu, W., & Chen, T.
Affiliations
Zhejiang University of Technology (CN)
Corresponding author
Chen, Tieming
Venue
Neurocomputing
Publisher
Elsevier BV
Published
2019-10
Volume
362
Pages
33-40
ISSN
0925-2312 (ISSN-L)
OpenAlex
W2962958625
Cited by
64
References
34
Citations by year
2026: 5 · 2025: 10 · 2024: 11 · 2023: 11 · 2022: 10 · 2021: 10 · 2020: 7
FWCI
3.02
Type
journal article
Language
en
Open access
closed
Locations
Neurocomputing (publishedVersion)
Funders
  • National Natural Science Foundation of China (61772026)
  • Natural Science Foundation of Zhejiang Province (LY18F020033)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
  • Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
  • Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
Keywords
multimodal sensors, wearable computing, mobile computing, convolutional recurrent neural network, hierarchical convolutional neural network, temporal relations, sensor fusion, human activity recognition

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

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