ECG-Based Subject Identification Using Common Spatial Pattern and SVM

Alotaiby, Turky N.; Alshebeili, Saleh A.; Aljafar, Latifah M.; Alsabhan, Waleed

doi:10.1155/2019/8934905

Cited by 15 publications

(7 citation statements)

References 57 publications

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“…As such, numerous lightweight authentication methods have been introduced in [96], [97], [98], [99], [100], [101] and [102]. To secure IoT healthcare communication process, an identification system is developed in [103] while a human recognition system is presented in [104].…”

Section: Related Workmentioning

confidence: 99%

A critical review of internet of things communication environment: Privacy and security constraints

Kanini¹

2022

GSC Adv. Eng. Technol.

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The recent past has experienced a steady increase in the adoption of Internet of Things (IoT) in a number of areas such as smart cities, smart homes, smart transportation and smart health. Due to the message exchanges among IoT devices over the public internet, the transmitted data is vulnerable to many security and privacy attacks. Therefore, many schemes have been presented over the recent past to address these challenges. However, many security holes still exist in most of the current techniques. It was also noted that the resource limited nature of most IoT devices renders traditional authentication schemes for main-powered systems with high processing power and large memory infeasible and inapplicable. To address this performance issue, numerous lightweight authentication schemes have been put forward. However, this paper discovered numerous security and privacy gaps in these schemes. Based on these shortcomings, recommendations are given towards the end of this paper which are very critical for security enhancements in this pervasive computing environment.

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Section: Related Workmentioning

confidence: 99%

A critical review of internet of things communication environment: Privacy and security constraints

Kanini¹

2022

GSC Adv. Eng. Technol.

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“…The Common Spatial Pattern (CSP) algorithm [20], a mathematical technique applied in signal processing, has been widely used in Brain Computer Interface (BCI) applications for electroencephalography (EEG) systems [21][22][23]. Research has also been published applying CSP in the field of electrocardiography (ECG) [24], electromyography (EMG) [25,26] or even in astronomical images for planet detection [27]. CSP was presented as an extension of Principal Component Analysis (PCA) and it consists of finding an optimum spatial filter which reduces the dimensionality of the original signals.…”

Section: Csp-based Approachmentioning

confidence: 99%

Using Common Spatial Patterns to Select Relevant Pixels for Video Activity Recognition

et al. 2020

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Video activity recognition, despite being an emerging task, has been the subject of important research due to the importance of its everyday applications. Video camera surveillance could benefit greatly from advances in this field. In the area of robotics, the tasks of autonomous navigation or social interaction could also take advantage of the knowledge extracted from live video recording. In this paper, a new approach for video action recognition is presented. The new technique consists of introducing a method, which is usually used in Brain Computer Interface (BCI) for electroencephalography (EEG) systems, and adapting it to this problem. After describing the technique, achieved results are shown and a comparison with another method is carried out to analyze the performance of our new approach.

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“…These signals are then transformed using the Common Spatial Patterns [ 5 ] algorithm, a dimensionality reduction algorithm widely used in EEG signals. CSP has also been applied in the field of electrocardiography (ECG) [ 6 ], electromyography (EMG) [ 7 , 8 ] or even in astronomical images for planet detection [ 9 ], and recently it has been used in video action recognition tasks [ 10 ] obtaining encouraging outcomes. This approach allows for a closed form computation and therefore it is not necessary to decide termination criteria as it happens in widely applied iterative methods, e.g., gradient descent in deep learning.…”

Section: Introductionmentioning

confidence: 99%

Sign language recognition by means of common spatial patterns: An analysis

Rodríguez-Moreno

Martínez-Otzeta²,

Goienetxea³

et al. 2022

PLoS ONE

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Currently there are around 466 million hard of hearing people and this amount is expected to grow in the coming years. Despite the efforts that have been made, there is a communication barrier between deaf and hard of hearing signers and non-signers in environments without an interpreter. Different approaches have been developed lately to try to deal with this issue. In this work, we present an Argentinian Sign Language (LSA) recognition system which uses hand landmarks extracted from videos of the LSA64 dataset in order to distinguish between different signs. Different features are extracted from the signals created with the hand landmarks values, which are first transformed by the Common Spatial Patterns (CSP) algorithm. CSP is a dimensionality reduction algorithm and it has been widely used for EEG systems. The features extracted from the transformed signals have been then used to feed different classifiers, such as Random Forest (RF), K-Nearest Neighbors (KNN) or Multilayer Perceptron (MLP). Several experiments have been performed from which promising results have been obtained, achieving accuracy values between 0.90 and 0.95 on a set of 42 signs.

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ECG-Based Subject Identification Using Common Spatial Pattern and SVM

Cited by 15 publications

References 57 publications

A critical review of internet of things communication environment: Privacy and security constraints

A critical review of internet of things communication environment: Privacy and security constraints

Using Common Spatial Patterns to Select Relevant Pixels for Video Activity Recognition

Sign language recognition by means of common spatial patterns: An analysis

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