Stacked Lstm Network for Human Activity Recognition Using Smartphone Data

Ullah, Mohib; Ullah, Habib; Khan, Sultan Daud; Cheikh, Faouzi Alaya

doi:10.1109/euvip47703.2019.8946180

Cited by 103 publications

(68 citation statements)

References 22 publications

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“…Stacking performs better than that of aggregation and achieves an accuracy of 0.925 for HAR tasks. Similarly, a stacked LSTM network is presented in [18] for identifying six human behaviors using smartphone data. The network consists of five LSTM cells and an L 2 regularization layer is used for network generalization.…”

Section: Related Workmentioning

confidence: 99%

“…Table 11 contains the results and the models of the above-mentioned studies. [44] Deep CNN 0.946 [45] Deep CNN 0.951 [46] Deep CNN 0.947 [47] Deep CNN 0.900 [48] Deep ConvLSTM 0.958 [16] Residual Bi LSTM 0.905 [17] Multiview stacking 0.925 [18] Stacked LSTM 0.930 [19] SDAE+GBM 0.959 [20] Stacked ensemble 0.960 [21] Stacked ensemble 0.968 DS-MLP Deep Stacked Ensemble 0.973 Performance analysis suggests that the approaches based on deep CNN have an accuracy between 0.90 to 0.95. Ensemble approaches, on the other hand, tend to have higher accuracy than that of simple deep learning approaches.…”

Section: Performance Comparison With State-of-the-art Studiesmentioning

confidence: 99%

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Sensor-Based Human Activity Recognition Using Deep Stacked Multilayered Perceptron Model

et al. 2020

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The recent development of machines exhibiting intelligent characteristics involves numerous techniques including computer hardware and software architecture development. Many different hardware devices, wearable sensors, machine learning, and deep learning model implementations are being applied in human activity recognition (HAR) applications in recent times. However, to develop high accuracy classification systems for activity recognition using low-cost hardware technology is of significant importance. To achieve this goal this study uses sensor data from two low-cost sensors, gyroscope and accelerometer along with the implementation of an Artificial Neural Network (ANN) based deep learning model for HAR. In particular, Deep Stacked Multilayered Perceptron (DS-MLP) has been proposed. In the implementation of DS-MLP, an ANN model has been used as a meta-learner while five MLP models have been used as base-learners. In this study, these base-learners and meta-learner have been combined using a stack ensemble technique. The performance evaluations have been done first on the applicability of individual base-models followed by the application of DS-MLP, the results prove the high accuracy of 97.3% and 99.4% for heterogeneous datasets used for testing. The performance of the proposed DS-MLP models has been compared to some existing machine learning classifiers and several state-of-the-art activity recognition systems. The comparative result analysis also proves that the proposed system performed better than these classification approaches in terms of important performance metrics such as accuracy, precision, recall, F-score, Cohen's Kappa, and Mathew correlation coefficient.

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Section: Related Workmentioning

confidence: 99%

Section: Performance Comparison With State-of-the-art Studiesmentioning

confidence: 99%

Sensor-Based Human Activity Recognition Using Deep Stacked Multilayered Perceptron Model

et al. 2020

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“…Em [23], se propôs a criação de uma rede empilhada de memória de Em [31] se apresenta um sistema de detecção de postura usando sensores embutidos em dispositivos portáteis de consumo, para fins de verificação de postura.…”

Section: Métodos Para Monitorizar Atletas De Alta Competiçãounclassified

The importance of software development for the monitoring of training to high competition

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“…It has the potential to be utilized in a wide spectrum of applications, and consequently, it is an active topic of research. Many breakthroughs have been achieved in this area in the last few decades [7].…”

Section: Introductionmentioning

confidence: 99%

Dimensionality Reduction for Human Activity Recognition Using Google Colab

2020

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Human activity recognition (HAR) is a classification task that involves predicting the movement of a person based on sensor data. As we can see, there has been a huge growth and development of smartphones over the last 10–15 years—they could be used as a medium of mobile sensing to recognize human activity. Nowadays, deep learning methods are in a great demand and we could use those methods to recognize human activity. A great way is to build a convolutional neural network (CNN). HAR using Smartphone dataset has been widely used by researchers to develop machine learning models to recognize human activity. The dataset has two parts: training and testing. In this paper, we propose a hybrid approach to analyze and recognize human activity on the same dataset using deep learning method on cloud-based platform. We have applied principal component analysis on the dataset to get the most important features. Next, we have executed the experiment for all the features as well as the top 48, 92, 138, and 164 features. We have run all the experiments on Google Colab. In the experiment, for the evaluation of our proposed methodology, datasets are split into two different ratios such as 70–10–20% and 80–10–10% for training, validation, and testing, respectively. We have set the performance of CNN (70% training–10% validation–20% testing) with 48 features as a benchmark for our work. In this work, we have achieved maximum accuracy of 98.70% with CNN. On the other hand, we have obtained 96.36% accuracy with the top 92 features of the dataset. We can see from the experimental results that if we could select the features properly then not only could the accuracy be improved but also the training and testing time of the model.

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Stacked Lstm Network for Human Activity Recognition Using Smartphone Data

Cited by 103 publications

References 22 publications

Sensor-Based Human Activity Recognition Using Deep Stacked Multilayered Perceptron Model

Sensor-Based Human Activity Recognition Using Deep Stacked Multilayered Perceptron Model

The importance of software development for the monitoring of training to high competition

Dimensionality Reduction for Human Activity Recognition Using Google Colab

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