Arm movement activity based user authentication in P2P systems

Shin, Jungpil; Islam, Rashedul; Rahim, Abdur; Mun, Hyung-Jin

doi:10.1007/s12083-019-00775-7

Cited by 9 publications

(6 citation statements)

References 17 publications

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“…When the absolute values of some features range widely or can be large, their inner product can be dominant in the Kernel calculation. Shin et al [73] made studies on optimizing Kernel scale parameters for training Machine Learning Classifiers for a user authentication system. It was found in their research that for linear, cubic, and gaussian models, a heuristic procedure (Grid Search) implemented in Matlab provides a good prediction ability of their models.…”

Section: A Binary Svm Classifiermentioning

confidence: 99%

Establishing Statistical Correlation Between Sensor Signature Features and Lubricant Solid Particle Contamination in a Spur Gearbox

Rameshkumar

Saimurugan

et al. 2022

IEEE Access

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This paper aims to predict the severity of solid particle contaminants present in the lubricant in a Spur Gearbox using Vibration, Acoustic Emission, and Sound Signature features. Sensor signatures are acquired at various contaminant conditions of lubricant with different speed and load conditions. Statistical Features are extracted in the time domain, and feature ranking is carried out using the analysis of variance approach. Statistical models are developed using the selected features of Sound, Acoustic Emission, and Vibration separately and fusing the features in the feature level. Decision Trees and Support Vector Machine algorithms are used in this study to build statistical models. AE features have a good correlation with lubricant conditions compared to sound and vibration features. The feature-level fusion approach predicts the lubricant conditions with an accuracy of more than 99%. The feature-level fusion models built using dominant features are computationally efficient without compromising the prediction ability.

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Section: A Binary Svm Classifiermentioning

confidence: 99%

Establishing Statistical Correlation Between Sensor Signature Features and Lubricant Solid Particle Contamination in a Spur Gearbox

Rameshkumar

Saimurugan

et al. 2022

IEEE Access

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“…EPS estimates the intermittent and irregular signals. EPS can check the irregularities in the periodograms of the time series signal [48]; particularly signals that are generated from different machines. Sensors (for example: accelerometer and gyroscope) are a good example for such signals.…”

Section: Enveloped Power Spectrum (Eps)mentioning

confidence: 99%

A Robust Feature Extraction Model for Human Activity Characterization Using 3-Axis Accelerometer and Gyroscope Data

Bhuiyan

Ahmed

Amiruzzaman

et al. 2020

Sensors

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Human Activity Recognition (HAR) using embedded sensors in smartphones and smartwatch has gained popularity in extensive applications in health care monitoring of elderly people, security purpose, robotics, monitoring employees in the industry, and others. However, human behavior analysis using the accelerometer and gyroscope data are typically grounded on supervised classification techniques, where models are showing sub-optimal performance for qualitative and quantitative features. Considering this factor, this paper proposes an efficient and reduce dimension feature extraction model for human activity recognition. In this feature extraction technique, the Enveloped Power Spectrum (EPS) is used for extracting impulse components of the signal using frequency domain analysis which is more robust and noise insensitive. The Linear Discriminant Analysis (LDA) is used as dimensionality reduction procedure to extract the minimum number of discriminant features from envelop spectrum for human activity recognition (HAR). The extracted features are used for human activity recognition using Multi-class Support Vector Machine (MCSVM). The proposed model was evaluated by using two benchmark datasets, i.e., the UCI-HAR and DU-MD datasets. This model is compared with other state-of-the-art methods and the model is outperformed.

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“…This pattern can be used as a feature, or to extract hand movements or gestures that are interpretable features for performing character input. Frequency domain features [19] like center frequency (CF), root mean square (RMS) frequency, root variance frequency (RMF), and slope sign change (SSC) can be used. Table 1 represents the descriptions of frequency-domain features.…”

Section: Feature Extractionmentioning

confidence: 99%

Hand Movement Activity-Based Character Input System on a Virtual Keyboard

Rahim

Shin

2020

Electronics

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Nowadays, gesture-based technology is revolutionizing the world and lifestyles, and the users are comfortable and care about their needs, for example, in communication, information security, the convenience of day-to-day operations and so forth. In this case, hand movement information provides an alternative way for users to interact with people, machines or robots. Therefore, this paper presents a character input system using a virtual keyboard based on the analysis of hand movements. We analyzed the signals of the accelerometer, gyroscope, and electromyography (EMG) for movement activity. We explored potential features of removing noise from input signals through the wavelet denoising technique. The envelope spectrum is used for the analysis of the accelerometer and gyroscope and cepstrum for the EMG signal. Furthermore, the support vector machine (SVM) is used to train and detect the signal to perform character input. In order to validate the proposed model, signal information is obtained from predefined gestures, that is, “double-tap”, “hold-fist”, “wave-left”, “wave-right” and “spread-finger” of different respondents for different input actions such as “input a character”, “change character”, “delete a character”, “line break”, “space character”. The experimental results show the superiority of hand gesture recognition and accuracy of character input compared to state-of-the-art systems.

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Arm movement activity based user authentication in P2P systems

Cited by 9 publications

References 17 publications

Establishing Statistical Correlation Between Sensor Signature Features and Lubricant Solid Particle Contamination in a Spur Gearbox

Establishing Statistical Correlation Between Sensor Signature Features and Lubricant Solid Particle Contamination in a Spur Gearbox

A Robust Feature Extraction Model for Human Activity Characterization Using 3-Axis Accelerometer and Gyroscope Data

Hand Movement Activity-Based Character Input System on a Virtual Keyboard

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