A robust convolutional neural network for online smartphone-based human activity recognition

ِAlmaslukh, Bandar Saleh Mouhammed; Artoli, Abdelmonim; Al‐Muhtadi, Jalal

doi:10.3233/jifs-169699

Cited by 29 publications

(15 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The reduced representation of the second max-pooling layer is flattened to be a 1D vector. The flattened vector is stretched by concatenating the time-domain statistical features, as in [ 14 ]. Then, the stretched vector is connected to a fully-connected layer made up of 1024 neurons which is connected to another fully-connected layer made up of 512 neurons.…”

Section: Methodsmentioning

confidence: 99%

“…This paper is considered an extension to our previous paper [ 14 ]. In that paper, we proposed a deep learning architecture using the Convolutional Neural Network (CNN) together with time-domain statistical features that effectively represented the raw time series data of smartphone-based HAR.…”

Section: Introductionmentioning

confidence: 99%

“…The deep architecture of these approaches enables the extraction of high-level representation (deep features) which are more suitable for complex problems such as HAR. Recently, deep learning approaches have been employed to build a robust smartphone-based HAR [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Robust Deep Learning Approach for Position-Independent Smartphone-Based Human Activity Recognition

ِAlmaslukh

Artoli

Al‐Muhtadi

2018

Sensors

Self Cite

View full text Add to dashboard Cite

Recently, modern smartphones equipped with a variety of embedded-sensors, such as accelerometers and gyroscopes, have been used as an alternative platform for human activity recognition (HAR), since they are cost-effective, unobtrusive and they facilitate real-time applications. However, the majority of the related works have proposed a position-dependent HAR, i.e., the target subject has to fix the smartphone in a pre-defined position. Few studies have tackled the problem of position-independent HAR. They have tackled the problem either using handcrafted features that are less influenced by the position of the smartphone or by building a position-aware HAR. The performance of these studies still needs more improvement to produce a reliable smartphone-based HAR. Thus, in this paper, we propose a deep convolution neural network model that provides a robust position-independent HAR system. We build and evaluate the performance of the proposed model using the RealWorld HAR public dataset. We find that our deep learning proposed model increases the overall performance compared to the state-of-the-art traditional machine learning method from 84% to 88% for position-independent HAR. In addition, the position detection performance of our model improves superiorly from 89% to 98%. Finally, the recognition time of the proposed model is evaluated in order to validate the applicability of the model for real-time applications.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Robust Deep Learning Approach for Position-Independent Smartphone-Based Human Activity Recognition

ِAlmaslukh

Artoli

Al‐Muhtadi

2018

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, CNN is proposed to classify HAR using smartphone sensors achieved an overall performance of 94.79% on the test set with raw sensor data, and 95.75% with additional information of temporal fast Fourier transform of HAR dataset. Also, stacked autoencoder (SAE) based DL increased the overall classification accuracy from 96.4% to 97.5% [20].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, DNN is an extension to NN work with large datasets which need deep learning (DL) to combine multiple nonlinear processing layers, using simple elements operating in parallel and inspired by biological nervous systems [19] [20]. DNN evolves rapidly to learn useful representations of features directly from data not only to achieve accuracy in object classification, but also sometimes exceeding human-level performance [21] [22].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network for Human Activity Recognition

Khairy¹

2018

IJCA

View full text Add to dashboard Cite

Human activity recognition (HAR) has given a lot of attention in the recent years due to the need of high level context about the human activities in several applications. Many domains have attempted to overcome the lack of performance techniques used to collect raw data such as cameras to record or capture activities and inertial sensor units to record correct readings. As a result, few studies have regarded to acquire raw data and extract features instead of understanding, recognizing, inferring, and predicting human activities in future to obtain recommendations or detecting healthcare, daily, and educational positions to humans. This paper aims to analyze the performance of Neural Network (NN) and Deep Neural Network(DNN) for HAR. To achieve this aim, we select Daily and Sports Activities data set (DSA) to match paper's needs. This paper depends on NN and DNN based on softmax function. We form three sets of DSA dataset: small, medium, and large. The results showed that DNN based on softmax function reduce the computational cost than NN, increase the performance of network, and achieved high overall successful differentiation rate in testing on large dataset (97.74%) than on medium dataset (67.81%). or on small dataset (67.63%).

show abstract