Identity Authentication of OSA Patients Using Microwave Doppler radar and Machine Learning Classifiers

Islam, Shekh Md Mahmudul; Rahman, Ashikur; Yavari, Ehsan; Baboli, Meheran; Borić–Lubecke, Olga; Lubecke, Victor M.

doi:10.1109/rws45077.2020.9049983

Cited by 21 publications

(26 citation statements)

References 5 publications

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“…Based on reported results researchers have focused on extracting two different measures: respiratory-based and heart-based features [11]. The Biosensing Laboratory at the University of Hawaii at Manoa focused on a respiratory-based featurerelated identity authentication system [10,11,12,13,14], which is described here in-detail along with the feasibility of the use of heart-based features in recognizing people [11]. An illustration of a proposed unobtrusive biometric identification system based on respiration is shown in Figure 2.…”

Section: The Radar-based Non-contact Identity Authentication Systemmentioning

confidence: 99%

“…(1) signal acquisition, (2) pre-processing, (3) feature extraction, and (4) machine learning (ML) classifier integration. A 2.4-GHz Doppler radar system was used to capture the respiration pattern for twenty different test participants over a course of about two months [11,12,13,14]. After capturing the respiration pattern, the signals were processed for removal of noise and another random movement, and then respiratory-related features were extracted and used to recognize different participants.…”

Section: The Radar-based Non-contact Identity Authentication Systemmentioning

confidence: 99%

“…Different people have different pause times in starting the next cycle of breathing pattern and there are significant differences in air flow profile after performing physiological activities (walking upstairs) [12]. Different respiratory features like breathing rate, heart rate, inhale rate, exhale rate, inhale area, exhale area, inhale/exhale area ratio were also extracted for the twenty different participants [12,13,14]. Figure 3 shows the experimental scenario performed at the Biosensing Laboratory of the University of Hawaii at Manoa, where experiments were conducted.…”

Section: The Radar-based Non-contact Identity Authentication Systemmentioning

confidence: 99%

“…A training data set and test data set were separated for both sedentary breathing and after physiological activities (walking upstairs) [12,13,14]. Three different popular machine learning classifiers were then integrated: K-nearest neighbor (KNN), support vector machine (SVM) with radial basis function, and Random forest classifiers, all to recognize people [12,13,14].…”

Section: The Radar-based Non-contact Identity Authentication Systemmentioning

confidence: 99%

See 3 more Smart Citations

BreathID: Radar’s New Role in Biometrics

Islam¹

2021

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<p> This article is focused on providing some insight into the requirements for a non-contact continuous identity authentication system using radar sensing technologies. The article also discusses the basic principles supporting the potential new role of radar sensing technologies in a broad area of applications. It also highlights challenges associated with this new technology and provides some direction on what is needed to address requirements for real-world applications. </p>

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Section: The Radar-based Non-contact Identity Authentication Systemmentioning

confidence: 99%

Section: The Radar-based Non-contact Identity Authentication Systemmentioning

confidence: 99%

Section: The Radar-based Non-contact Identity Authentication Systemmentioning

confidence: 99%

Section: The Radar-based Non-contact Identity Authentication Systemmentioning

confidence: 99%

See 2 more Smart Citations

BreathID: Radar’s New Role in Biometrics

Islam¹

2021

Preprint

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show abstract

“…In conventional supervised machine-learning, well-defined extracted features are essential. Typical machine learning methods are the k-nearest neighbor (KNN), support vector machine (SVM), and random forest methods [ 29 , 30 , 31 , 32 , 33 ]. However, the recognition performance is strongly dependent on the predefined features.…”

Section: Introductionmentioning

confidence: 99%

Foot Gesture Recognition Using High-Compression Radar Signature Image and Deep Learning

Song

Kim

et al. 2021

Sensors

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Recently, Doppler radar-based foot gesture recognition has attracted attention as a hands-free tool. Doppler radar-based recognition for various foot gestures is still very challenging. So far, no studies have yet dealt deeply with recognition of various foot gestures based on Doppler radar and a deep learning model. In this paper, we propose a method of foot gesture recognition using a new high-compression radar signature image and deep learning. By means of a deep learning AlexNet model, a new high-compression radar signature is created by extracting dominant features via Singular Value Decomposition (SVD) processing; four different foot gestures including kicking, swinging, sliding, and tapping are recognized. Instead of using an original radar signature, the proposed method improves the memory efficiency required for deep learning training by using a high-compression radar signature. Original and reconstructed radar images with high compression values of 90%, 95%, and 99% were applied for the deep learning AlexNet model. As experimental results, movements of all four different foot gestures and of a rolling baseball were recognized with an accuracy of approximately 98.64%. In the future, due to the radar’s inherent robustness to the surrounding environment, this foot gesture recognition sensor using Doppler radar and deep learning will be widely useful in future automotive and smart home industry fields.

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Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works

Moridian

Shoeibi

Khodatars

et al. 2022

WIREs Data Min & Knowl

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Apnea is a sleep disorder that stops or reduces airflow for a short time during sleep. Sleep apnea may last for a few seconds and happen for many while sleeping. This reduction in breathing is associated with loud snoring, which may awaken the person with a feeling of suffocation. So far, a variety of methods have been introduced by researchers to diagnose sleep apnea, among which the polysomnography (PSG) method is known to be the best. Analysis of PSG signals is very complicated. Many studies have been conducted on the automatic diagnosis of sleep apnea from biological signals using artificial intelligence (AI), including machine learning (ML) and deep learning (DL) methods. This research reviews and investigates the studies on the diagnosis of sleep apnea using AI methods. First, computer aided diagnosis system (CADS) for sleep apnea using ML and DL techniques along with its parts including dataset, preprocessing, and ML and DL methods are introduced. This research also summarizes the important specifications of the studies on the diagnosis of sleep apnea using ML and DL methods in a table. In the following, a comprehensive discussion is made on the studies carried out in this field. The challenges in the diagnosis of sleep apnea using AI methods are of paramount importance for researchers. Accordingly, these obstacles are elaborately addressed. In another section, the most important future works for studies on sleep apnea detection from PSG signals and AI techniques are presented. Ultimately, the essential findings of this study are provided in the conclusion section. This article is categorized under: Technologies > Artificial Intelligence Application Areas > Data Mining Software Tools Algorithmic Development > Biological Data Mining

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Identity Authentication of OSA Patients Using Microwave Doppler radar and Machine Learning Classifiers

Cited by 21 publications

References 5 publications

BreathID: Radar’s New Role in Biometrics

BreathID: Radar’s New Role in Biometrics

Foot Gesture Recognition Using High-Compression Radar Signature Image and Deep Learning

Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works

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