e-BMI: A gait based smart remote BMI monitoring framework implementing edge computing and incremental machine learning

Adhikary, Subhrangshu; Ghosh, Arindam

doi:10.1016/j.smhl.2022.100277

Cited by 16 publications

(7 citation statements)

References 58 publications

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“…Principal Component Analysis is a dimensionality reduction approach that reduces a large dataset to a smaller number of dimensions Adhikary and Ghosh. 10 CA is utilized in the extraction of features and the reduction of images. PCA is a method for discovering data patterns and displaying them in a way that highlights their similarities and differences.…”

Section: Basic Biometric System Operationsmentioning

confidence: 99%

“…Principal Component Analysis is a dimensionality reduction approach that reduces a large dataset to a smaller number of dimensions Adhikary and Ghosh 10 . CA is utilized in the extraction of features and the reduction of images.…”

Section: Review Of Related Workmentioning

confidence: 99%

“…The distance between two pixels is also referred to as the Euclidean distance. This Euclidean distance is calculated as follows (10):…”

mentioning

confidence: 99%

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Secure personal authentication in fog devices via multimodal rank‐level fusion

Selvaraj

Thangavel

2023

Concurrency and Computation

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Summary In recent years, Fog and edge computing have gained enormous increases and usage of Internet of Things (IoT) devices in research and development even though it provides challenges such as security and privacy. One way to ensure this is a fusion of multimodal biometrics authentication systems, which offer highly reliable biometric authentication solutions to reduce the failure to enroll rate, increase the degree of freedom, and deter spoof attacks. Since unimodal biometric systems often face significant limitations due to sensitivity to noise, data quality, non‐universality, and illumination variations. In this research, we propose management of access control to ensure the desired level of security, a Rank level fusion integration method. ORL, GT, CASIA, and VGG‐face databases were used for testing and evaluating the implemented system. Thus, using the LDA with SCE based feature selection approach, the average GAR percent for the facial, hearing, and palm vein is 3.25% for the facial and muffs pulse and 8.55% for the head and palm vein result was the overall performance of the biometric system even in the presence of low‐quality data in the Fog environment.

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Section: Basic Biometric System Operationsmentioning

confidence: 99%

Section: Review Of Related Workmentioning

confidence: 99%

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Secure personal authentication in fog devices via multimodal rank‐level fusion

Selvaraj

Thangavel

2023

Concurrency and Computation

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“…Fourier transformation is a popular signal processing technique used to identify the frequencies that the signal is formed from. This would help to isolate the frequency ranges that are unique to the mood based on the EEG signals [12,13]. However, for more complicated data like EEG signals which are made up of several different frequency components and have an irregular pattern, it is difficult to identify the most significant frequency component of the signal by Fourier transformation.…”

Section: Introductionmentioning

confidence: 99%

Optimized EEG based mood detection with signal processing and deep neural networks for brain-computer interface

Adhikary¹,

Jain

Saha³

et al. 2023

Biomed. Phys. Eng. Express

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Electroencephalogram (EEG) is a very promising and widely implemented procedure to study brain signals and activities by amplifying and measuring the post-synaptical potential arising from electrical impulses produced by neurons and detected by specialized electrodes attached to specific points in the scalp. It can be studied for detecting brain abnormalities, headaches, and other conditions. However, there are limited studies performed to establish a smart decision-making model to identify EEG's relation with the mood of the subject. In this experiment, EEG signals of 28 healthy human subjects have been observed with consent and attempts have been made to study and recognise moods. Savitzky-Golay band-pass filtering and Independent Component Analysis have been used for data filtration. Different neural network algorithms have been implemented to analyze and classify the EEG data based on the mood of the subject. The model is further optimised by the usage of Blackman window-based Fourier Transformation and extracting the most significant frequencies for each electrode. Using these techniques, up to 96.01% detection accuracy has been obtained.

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“…Some researchers have used state-of-art methods, such as artificial intelligence (AI), internet of things (IoT), machine learning (ML), and deep learning for healthcare systems [ 1 , 2 ]. These methods are also utilized for studying various aspects of HHC [ 26 ].…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%