Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

Abstract

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 complex sequences of motor movements, and capturing this… (More)
DOI: 10.3390/s16010115

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@inproceedings{Morales2016DeepCA, title={Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition}, author={Francisco Javier Ord{\'o}{\~n}ez Morales and Daniel Roggen}, booktitle={Sensors}, year={2016} }