DGRU based human activity recognition using channel state information

Bokhari, Syed Mohsin Matloob; Sohaib, Sarmad; Khan, Ahsan Raza; Shafi, Muhammad; Khan, Atta ur Rehman

doi:10.1016/j.measurement.2020.108245

Cited by 31 publications

(13 citation statements)

References 27 publications

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“…This research [32] introduces a Deep Gated Recurrent Unit (DGRU) model for non-intrusive human activity identification. The model utilizes Channel State Information (CSI) as input.…”

Section: Literature Reviewmentioning

confidence: 99%

A method for human behavior identification based on integrated sensor data using XGBoost classifier with PCA techniques

Thakur,

Lalwani

2024

Phys. Scr.

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In recent decades, there has been a noticeable increase in the recognition amongprofessionals of the importance of human acts. The identification of human activityhas gained significant prominence because of its wide-ranging applications inseveral domains, including healthcare and recreational activities. Moreover, a substantialbody of research in the field of machine learning has been dedicated to thedevelopment of methodologies aimed at automating the identification and analysisof human behavior. This increased interest is mostly due to the fact that there arenow more tools that can collect information about how people live their daily lives.The data utilized in this study is readily available for public access on the Internet.The data set under consideration comprises sensor readings from several componentsintegrated inside the smartphone, including the global positioning system (GPS),accelerometer, magnetometer, and gyroscope. The data sets are indifferent to thecategories, positions, or alignments of the items. The data set is of considerablemagnitude due to its collection from several sensors, including GPS, accelerometer,magnetometer, and gyroscope. Consequently, we are employing the Principal ComponentAnalysis (PCA) technique to diminish the dimensionality of the data andenhance its precision. Our recommendation is to utilize the XGBoost classifier incombination with Principal Component Analysis (PCA). The recommended modelhad a total identification rate of 97.58%. In order to demonstrate the flexibilityof the proposed method, we employ a 10-fold cross-validation technique, togetherwith the utilization of a confusion matrix and ROC curve. These evaluation metricsserve to provide a tangible illustration of the recommended strategy. The proposedmethodology might have encouraging outcomes in effectively discerning human behaviors,thereby potentially facilitating the restoration of walking and pushing gaitsinto a Bipedal Robot and other Parkinson’s diseases.

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“…This research [32] introduces a Deep Gated Recurrent Unit (DGRU) model for non-intrusive human activity identification. The model utilizes Channel State Information (CSI) as input.…”

Section: Literature Reviewmentioning

confidence: 99%

A method for human behavior identification based on integrated sensor data using XGBoost classifier with PCA techniques

Thakur,

Lalwani

2024

Phys. Scr.

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“…The Deep Gated Recurrent Unit (DGRU) was used by Bokhari et al (2021) to classify seven activities: running, sitting, walking, jumping, lying, falling, and NA. The datasets were collected using Channel State Information (CSI), while discrete wavelet transforms and linear discriminant analysis were used for features extraction and selection.…”

Section: Related Workmentioning

confidence: 99%

Human activity recognition using visibility graph features coupled with machine learning algorithm

Ibrahim

Kata

Oudah³

2023

JLMA

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Human activities refer to the actions and behaviors of human beings. These activities can be physical, such as working, playing sports, or playing sports; or mentally, such as learning, problem-solving, or decision-making. Technical development and the emergence of mobile devices such as phones and smart watches, as well as wearable sensors, led to the emergence of many systems to recognize and classify human activities. These systems were developed using the data collected by these devices from a variety of individuals who volunteered to do several activities, such as downstairs, upstairs, sitting, running, standing, and more. Using the WISDM (Wireless Sensor Data Mining) dataset, a new machine learning model is proposed to recognize six different human activities: walking, jogging, going up stairs, going down stairs, sitting, and standing. For signal segmentation, the sliding window technique was used, along with two visibility graph techniques for feature extraction: mean degree and Jaccard coefficient. The Least-Squares Support Vector Machines (LS-SVM) used to classify these activities This model achieves 94% accuracy, demonstrating that the proposed model has a high classification rate.

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“…In the end all the DCNN ensemble are merged using late fusion method. A different approach used by Bokhari et al [17], who exploited Channel State Information (CSI) to estimate and classify activities performed in an indoor environment using a deep Gated Recurrent network (DGRU).…”

Section: Review Of Literature IImentioning

confidence: 99%

A Hybrid Deep Neural Network for Human Activity Recognition based on IoT Sensors

Benhaili¹,

Balouki²,

Moumoun³

2021

IJACSA

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Internet of things (IOT) sensors, has received a lot of interest in recent years due to the rise of application demands in domains like ubiquitous and context-aware computing, activity surveillance, ambient assistive living and more specifically in Human activity recognition. The recent development in deep learning allows to extract high-level features automatically, and eliminates the reliance on traditional machine learning techniques, which depended heavily on hand crafted features. In this paper, we introduce a network that can identify a variety of everyday human actions that can be carried out in a smart home environment, by using raw signals generated from Internet of Thing's motion sensors. We design our architecture basing on a combination of convolutional neural network (CNN) and Gated recurrent unit (GRU) layers. The CNN is first deployed to extract local and scale-invariance features, then the GRU layers are used to extract sequential temporal dependencies. We tested our model called (CNGRU) on three public datasets. It achieves an accuracy better or comparable to existing state of the art models.

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DGRU based human activity recognition using channel state information

Cited by 31 publications

References 27 publications

A method for human behavior identification based on integrated sensor data using XGBoost classifier with PCA techniques

A method for human behavior identification based on integrated sensor data using XGBoost classifier with PCA techniques

Human activity recognition using visibility graph features coupled with machine learning algorithm

A Hybrid Deep Neural Network for Human Activity Recognition based on IoT Sensors

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