Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges

Nweke, Henry Friday; Wah, Teh Ying; Al-Garadi, Mohammed Ali; Alo, Uzoma Rita

doi:10.1016/j.eswa.2018.03.056

Cited by 726 publications

(420 citation statements)

References 102 publications

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“…We made this choice because we believe that the main goal of deep learning approaches is to remove the bias due to manually designed features (Ordónez and Roggen, 2016), thus enabling the network to learn the most discriminant useful features for the classification task. This has also been the consensus in the human activity recognition literature, where the accuracy of deep learning methods depends highly on the quality of the extracted features (Nweke et al, 2018). Finally, since our goal is to provide an empirical study of domain agnostic deep learning approaches for any TSC task, we found that it is best to compare models that do not incorporate any domain knowledge into their approach.…”

Section: Why Discriminative End-to-end Approaches ?mentioning

confidence: 80%

“…In contrast to feature engineering, end-to-end deep learning aims to incorporate the feature learning process while fine-tuning the discriminative classifier (Nweke et al, 2018). Since this type of deep learning approach is domain agnostic and does not include any domain specific pre-processing steps, we decided to further separate these end-to-end approaches using their neural network architectures.…”

Section: Discriminative Modelsmentioning

confidence: 99%

“…Strodthoff and Strodthoff (2019) proposed to detect myocardial infractions from electrocardiography data using deep CNNs. For human activity recognition from wearable sensors, deep learning is replacing the feature engineering approaches (Nweke et al, 2018) where features are no longer hand-designed but rather learned by deep learning models trained through backpropagation. One other type of time series data is present in Electronic Health Records, where a recent generative adversarial network with a CNN (Che et al, 2017a) was trained for risk prediction based on patients historical medical records.…”

Section: Discriminative Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Deep learning for time series classification: a review

Fawaz

Forestier

Weber

et al. 2019

Data Min Knowl Disc

2,173

712

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Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learning has seen very successful applications in the last years. DNNs have indeed revolutionized the field of computer vision especially with the advent of novel deeper architectures such as Residual and Convolutional Neural Networks. Apart from images, sequential data such as text and audio can also be processed with DNNs to reach state-of-the-art performance for document classification and speech recognition. In this article, we study the current state-ofthe-art performance of deep learning algorithms for TSC by presenting an empirical study of the most recent DNN architectures for TSC. We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC. We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated them on a univariate TSC benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By training 8,730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date.

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Section: Why Discriminative End-to-end Approaches ?mentioning

confidence: 80%

Section: Discriminative Modelsmentioning

confidence: 99%

Section: Discriminative Modelsmentioning

confidence: 99%

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Deep learning for time series classification: a review

Fawaz

Forestier

Weber

et al. 2019

Data Min Knowl Disc

2,173

712

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“…Time series classification is one of the most challenging problems in machine learning [20] with a wide range of applications in human activity recognition [21], acoustic scene classification [22], and cybersecurity [23]. In this section, we describe five different architectures that we have considered for classifying time series generated by discrete and continuous dynamical systems.…”

Section: Neural Network For Time Series Classificationmentioning

confidence: 99%

Classification of chaotic time series with deep learning

Boullé

Dallas

Nakatsukasa

et al. 2020

Physica D: Nonlinear Phenomena

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We use standard deep neural networks to classify univariate time series generated by discrete and continuous dynamical systems based on their chaotic or non-chaotic behaviour. Our approach to circumvent the lack of precise models for some of the most challenging real-life applications is to train different neural networks on a data set from a dynamical system with a basic or low-dimensional phase space and then use these networks to classify univariate time series of a dynamical system with more intricate or high-dimensional phase space. We illustrate this generalisation approach using the logistic map, the sine-circle map, the Lorenz system, and the Kuramoto-Sivashinsky equation. We observe that a convolutional neural network without batch normalization layers outperforms state-of-the-art neural networks for time series classification and is able to generalise and classify time series as chaotic or not with high accuracy.

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“…In a DBN, training is accomplished layer by layer, each of which is executed as an RBM trained on top of the formerly trained layer (DBNs are a set of RBMs layers used for the pre-training phase and subsequently become a feed-forward network for weight fine-tuning with contrastive convergence.) [192]. In the pre-training phase, the initial features are trained through a greedy layer-wise unsupervised approach, whereas a softmax layer is applied in the fine-tuning phase to the top layer to fine-tune the features with respect to the labelled samples [195].…”

Section: ) Deep Belief Network (Dbns)mentioning

confidence: 99%

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Al-Garadi

Mohamed

Al-Ali

et al. 2020

IEEE Commun. Surv. Tutorials

Self Cite

835

333

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The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. It is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. On the one hand, IoT technologies play a crucial role in enhancing several real-life smart applications that can improve life quality. On the other hand, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network security and application security, for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to secure the IoT ecosystem effectively. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory curiosity to practical machinery in several important applications. The ability to monitor IoT devices intelligently provides a significant solution to new or zero-day attacks. ML/DL are powerful methods of data exploration for learning about 'normal' and 'abnormal' behaviour according to how IoT components and devices perform within the IoT environment. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems.IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

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Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges

Cited by 726 publications

References 102 publications

Deep learning for time series classification: a review

Deep learning for time series classification: a review

Classification of chaotic time series with deep learning

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

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