Emotion classification using temporal and spectral features from IR-UWB-based respiration data

Siddiqui, Hafeez Ur Rehman; Zafar, Kainat; Saleem, Adil Ali; Raza, Muhammad Amjad; Dudley, Sandra; Rustam, Furqan; Ashraf, Imran

doi:10.1007/s11042-022-14091-5

Cited by 6 publications

(4 citation statements)

References 31 publications

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“…As one will see later in this work, by comparing the work herein presented with the emotion recognition results reported on [10,[14][15][16][17][18][19][20][21][22][23], we could demonstrate that our simple setup and methods are able to improve the results obtained in the literature so far. Furthermore, we also observed that the Bio-Radar system might even outperform traditional contact-based systems considering the same experiment conditions.…”

Section: Related Workmentioning

confidence: 58%

“…They reported improvements in the results compared to using a single sensor but did not quantify this enhancement or provide precision values for each sensor individually. The work developed in [ 20 ] is focused on classifying emotions such as happiness, disgust, and fear by using temporal and spectral features from IR-UWB-based respiration data, but this study did not present measurements with a certified contact sensor for comparison to validate the radar experiments. The authors of the study [ 23 ] proposed a unimodal emotion classifier using non-contact ECG signals, achieving high classification accuracies with machine learning classifiers.…”

Section: Related Workmentioning

confidence: 99%

“…Emotion recognition using vital signs captured remotely with a radar system has been extensively researched in studies such as [10,[14][15][16][17][18][19][20][21][22][23]. However, many of these works lack comprehensive comparisons with certified equipment.…”

Section: Related Workmentioning

confidence: 99%

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Remote Emotion Recognition Using Continuous-Wave Bio-Radar System

Gouveia,

Soares,

Albuquerque

et al. 2024

Sensors

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The Bio-Radar is herein presented as a non-contact radar system able to capture vital signs remotely without requiring any physical contact with the subject. In this work, the ability to use the proposed system for emotion recognition is verified by comparing its performance on identifying fear, happiness and a neutral condition, with certified measuring equipment. For this purpose, machine learning algorithms were applied to the respiratory and cardiac signals captured simultaneously by the radar and the referenced contact-based system. Following a multiclass identification strategy, one could conclude that both systems present a comparable performance, where the radar might even outperform under specific conditions. Emotion recognition is possible using a radar system, with an accuracy equal to 99.7% and an F1-score of 99.9%. Thus, we demonstrated that it is perfectly possible to use the Bio-Radar system for this purpose, which is able to be operated remotely, avoiding the subject awareness of being monitored and thus providing more authentic reactions.

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Section: Related Workmentioning

confidence: 58%

Section: Related Workmentioning

confidence: 99%

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Remote Emotion Recognition Using Continuous-Wave Bio-Radar System

Gouveia,

Soares,

Albuquerque

et al. 2024

Sensors

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“…The temporal and spectral features-based dataset is created and utilized to conduct the study experiments [ 12 ]. The temporal and spectral features [ 24 ] are extracted from UWB radar-based biological signals. The features ‘energy entropy’, ‘short time energy’, ‘time zero crossing rate’, ‘spectral crest factor’, ‘time Rms’, ‘spectral kurtosis’, ‘spectral rolloff’, ‘spectral skewness’, ‘spectral flatness’, ‘spectral decrease’, ‘spectral centroid’, ‘spectral spread’, ‘spectral slope’, and ‘spectral flux’ are the extracted temporal and spectral features from the raw UWB signal.…”

Section: Study Methodologymentioning

confidence: 99%

An Approach to Detect Chronic Obstructive Pulmonary Disease Using UWB Radar-Based Temporal and Spectral Features

et al. 2023

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Chronic obstructive pulmonary disease (COPD) is a severe and chronic ailment that is currently ranked as the third most common cause of mortality across the globe. COPD patients often experience debilitating symptoms such as chronic coughing, shortness of breath, and fatigue. Sadly, the disease frequently goes undiagnosed until it is too late, leaving patients without the care they desperately need. So, COPD detection at an early stage is crucial to prevent further damage to the lungs and improve quality of life. Traditional COPD detection methods often rely on physical examinations and tests such as spirometry, chest radiography, blood gas tests, and genetic tests. However, these methods may not always be accurate or accessible. One of the key vital signs for detecting COPD is the patient’s respiration rate. However, it is crucial to consider a patient’s medical and demographic characteristics simultaneously for better detection results. To address this issue, this study aims to detect COPD patients using artificial intelligence techniques. To achieve this goal, a novel framework is proposed that utilizes ultra-wideband (UWB) radar-based temporal and spectral features to build machine learning and deep learning models. This new set of temporal and spectral features is extracted from respiration data collected non-invasively from 1.5 m distance using UWB radar. Different machine learning and deep learning models are trained and tested on the collected dataset. The findings are promising, with a high accuracy score of 100% for COPD detection. This means that the proposed framework could potentially save lives by identifying COPD patients at an early stage. The k-fold cross-validation technique and performance comparison with the state-of-the-art studies are applied to validate its performance, ensuring that the results are robust and reliable. The high accuracy score achieved in the study implies that the proposed framework has the potential for the efficient detection of COPD at an early stage.

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