Application of ensemble RNN deep neural network to the fall detection through IoT environment

Farsi, Mohammed

doi:10.1016/j.aej.2020.06.056

Cited by 37 publications

(15 citation statements)

References 24 publications

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“…Amongst them, one of the most fascinating applications for the IoT is medical care and health care. An ensemble RNN deep neural network was applied in [8,9] to monitor health risk. Yet another comprehensive survey on the Internet of Things for health care was investigated in [10].…”

Section: Related Workmentioning

confidence: 99%

Deep Neural Artificial Intelligence for IoT Based Tele Health Data Analytics

Sivakumar¹,

Ibrahim²

2022

Computers, Materials &Amp; Continua

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Tele health utilizes information and communication mechanisms to convey medical information for providing clinical and educational assistances. It makes an effort to get the better of issues of health service delivery involving time factor, space and laborious terrains, validating cost-efficiency and finer ingress in both developed and developing countries. Tele health has been categorized into either real-time electronic communication, or store-andforward communication. In recent years, a third-class has been perceived as remote healthcare monitoring or tele health, presuming data obtained via Internet of Things (IOT). Although, tele health data analytics and machine learning have been researched in great depth, there is a dearth of studies that entirely concentrate on the progress of ML-based techniques for tele health data analytics in the IoT healthcare sector. Motivated by this fact, in this work a method called, Weighted Bayesian and Polynomial Taylor Deep Network (WB-PTDN) is proposed to improve health prediction in a computationally efficient and accurate manner. First, the Independent Component Data Arrangement model is designed with the objective of normalizing the data obtained from the Physionet dataset. Next, with the normalized data as input, Weighted Bayesian Feature Extraction is applied to minimize the dimensionality involved and therefore extracting the relevant features for further health risk analysis. Finally, to obtain reliable predictions concerning tele health data analytics, First Order Polynomial Taylor DNN-based Feature Homogenization is proposed that with the aid of First Order Polynomial Taylor function updates the new results based on the result analysis of old values and therefore provides increased transparency in decision making. The comparison of proposed and existing methods indicates that the WB-PTDN method achieves higher accuracy, true positive rate and lesser response time for IoT based tele health data analytics than the traditional methods.

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Section: Related Workmentioning

confidence: 99%

Deep Neural Artificial Intelligence for IoT Based Tele Health Data Analytics

Sivakumar¹,

Ibrahim²

2022

Computers, Materials &Amp; Continua

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“…RNN has been applied in several IoT domains including IoT security. More specifically, it has been used to detect attacks against IoT-connected smart home environments [17]. In the context of Industrial IoT, RNN has been used to predict maintenance needs of industrial and commercial plants [47].…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Game Theory Based Privacy Preserving Approach for Collaborative Deep Learning in IoT

Gupta¹,

Bhatt²,

Bhatt³

et al. 2021

Preprint

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The exponential growth of Internet of Things (IoT) has become a transcending force in creating innovative smart devices and connected domains including smart homes, healthcare, transportation and manufacturing. With billions of IoT devices, there is a huge amount of data continuously being generated, transmitted, and stored at various points in the IoT architecture. Deep learning is widely being used in IoT applications to extract useful insights from IoT data. However, IoT users have security and privacy concerns and prefer not to share their personal data with third party applications or stakeholders. In order to address user privacy concerns, Collaborative Deep Learning (CDL) has been largely employed in data-driven applications which enables multiple IoT devices to train their models locally on edge gateways. In this chapter, we first discuss different types of deep learning approaches and how these approaches can be employed in the IoT domain. We present a privacypreserving collaborative deep learning approach for IoT devices which can achieve benefits from other devices in the system. This learning approach is analyzed from the behavioral perspective of mobile edge devices using a game-theoretic model. We analyze the Nash Equilibrium in N-player static game model. We further present a novel fair collaboration strategy among edge IoT devices using cluster based approach to solve the CDL game, which enforces mobile edge devices for cooperation.

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“…For example, indoor localization and IoT applications inside smart buildings such as keeping social distances have been used since the COVID-19 pandemic began [ 11 ]. In addition, such applications are used for traditional tasks such as the monitoring of daily life activity [ 5 , 12 , 13 ], fall detection [ 14 ] and assisted living [ 15 , 16 , 17 , 18 ], bad habits (such as smoking) detection and control [ 19 ], monitoring of industrial workers’ activity [ 20 ], monitoring the heart rate of vehicle drivers [ 21 ], using wearable sensors to monitor heart activity [ 22 , 23 ], mHealth Apps for Self-Management [ 24 ], gait detection for people with Parkinson’s disease [ 25 , 26 ], and many others.…”

Section: Introductionmentioning

confidence: 99%

“…and the frequency domain (energy, entropy, FFT coefficients, etc. ), or they may follow the modern trend of deep learning networks [ 16 , 34 ]. In the latter approach, features are implicitly extracted as the encodings of hidden layers of the network, while outer layers such as fully connected layers together with softmax layer are responsible for the decision-making (i.e., classification and recognition).…”

Section: Introductionmentioning

confidence: 99%

“…However, deep learning models perform well for many available human activity datasets in the literature [ 34 ], but the RF algorithm, for example, performs better than a single LSTM classifier for a specific dataset addressed in [ 16 ]. In addition, the recent studies in [ 17 , 39 , 40 , 41 ] in IoT applications depend on shallow classifiers.…”

Section: Introductionmentioning

confidence: 99%

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Human Activity Recognition Based on Embedded Sensor Data Fusion for the Internet of Healthcare Things

et al. 2022

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Nowadays, the emerging information technologies in smart handheld devices are motivating the research community to make use of embedded sensors in such devices for healthcare purposes. In particular, inertial measurement sensors such as accelerometers and gyroscopes embedded in smartphones and smartwatches can provide sensory data fusion for human activities and gestures. Thus, the concepts of the Internet of Healthcare Things (IoHT) paradigm can be applied to handle such sensory data and maximize the benefits of collecting and analyzing them. The application areas contain but are not restricted to the rehabilitation of elderly people, fall detection, smoking control, sportive exercises, and monitoring of daily life activities. In this work, a public dataset collected using two smartphones (in pocket and wrist positions) is considered for IoHT applications. Three-dimensional inertia signals of thirteen timestamped human activities such as Walking, Walking Upstairs, Walking Downstairs, Writing, Smoking, and others are registered. Here, an efficient human activity recognition (HAR) model is presented based on efficient handcrafted features and Random Forest as a classifier. Simulation results ensure the superiority of the applied model over others introduced in the literature for the same dataset. Moreover, different approaches to evaluating such models are considered, as well as implementation issues. The accuracy of the current model reaches 98.7% on average. The current model performance is also verified using the WISDM v1 dataset.

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Application of ensemble RNN deep neural network to the fall detection through IoT environment

Cited by 37 publications

References 24 publications

Deep Neural Artificial Intelligence for IoT Based Tele Health Data Analytics

Deep Neural Artificial Intelligence for IoT Based Tele Health Data Analytics

Game Theory Based Privacy Preserving Approach for Collaborative Deep Learning in IoT

Human Activity Recognition Based on Embedded Sensor Data Fusion for the Internet of Healthcare Things

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