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Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of c...
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| Publicado no: | Sensors (Basel) |
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| Main Authors: | , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
MDPI
2016
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4732148/ https://ncbi.nlm.nih.gov/pubmed/26797612 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/s16010115 |
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