One-D Convolution Neural Network Models for Human Activity Recognition using mHealth Datasets | Semantic Scholar (2024)

This method adopts a deep convolutional neural networks (CNN) to automate feature learning from the raw inputs in a systematic way and makes it outperform other HAR algorithms, as verified in the experiments on the Opportunity Activity Recognition Challenge and other benchmark datasets.

A hybrid deep framework based on convolution operations, LSTM recurrent units, and ELM classifier is proposed, which does not require expert knowledge in extracting features, and is more suitable to classify the extracted features and shortens the runtime.

This paper proposes a one-dimensional Convolutional Neural Network (1D CNN) for HAR that employs a divide and conquer-based classifier learning coupled with test data sharpening that outperforms both the two-stage 1D CNN-only method and other state of the art approaches.

A generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which is suitable for multimodal wearable sensors, does not require expert knowledge in designing features, and explicitly models the temporal dynamics of feature activations is proposed.

An approach to automatically extract discriminative features for activity recognition based on Convolutional Neural Networks, which can capture local dependency and scale invariance of a signal as it has been shown in speech recognition and image recognition domains is proposed.

This paper proposes to use Convolutional Neural Networks (CNNs) to classify human activities, and explores several combinations of activities and sensors, showing how motion signals can be adapted to be fed into CNNs by using different network architectures.

Experimental results show that the proposed deep recurrent neural networks (DRNNs) used for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs.

This paper proposes a human activity recognition algorithm based on SDAE (Stacking Denoising Autoencoder and LightGBM (LGB) and demonstrates that the proposed algorithm achieves an average accuracy of 95.99%, outperforming other comparative algorithms using XGBoost, CNN (Convolutional Neural Network), CNN + Statistical features, or single SdaE.

This paper rigorously explore deep, convolutional, and recurrent approaches across three representative datasets that contain movement data captured with wearable sensors, and describes how to train recurrent approaches in this setting, introduces a novel regularisation approach, and illustrates how they outperform the state-of-the-art on a large benchmark dataset.

A novel deep learning framework for multivariate time series classification is proposed that is not only more efficient than the state of the art but also competitive in accuracy and demonstrates that feature learning is worth to investigate for time series Classification.

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