Design and Development of a Non-Contact ECG-Based Human Emotion Recognition System Using SVM and RF Classifiers

Alam, Anwar; Urooj, Shabana; Ansari, Abdul Quaiyum

doi:10.3390/diagnostics13122097

Cited by 9 publications

(5 citation statements)

References 42 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%

“…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%

“…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. The authors of the study [22] introduced an effective non-contact emotion recognition system using millimeter wave radar, RF-Emotion, which showed high accuracy in estimating heartbeat intervals and inferring emotional states.…”

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%

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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“…Here, λ i represents the Lagrange coefficients. In sample space problems that cannot be linearly separated, SVM uses kernel functions to move the sample space to another space where it can be linearly separated [30,31]. These functions are the Linear Kernel function, the Polynomial Kernel function, the Radial Basis Function (RBF), the Kernel function, and the Sigmoid Kernel function.…”

Section: Support Vector Machinesmentioning

confidence: 99%

A Comparative Analysis of Skin Cancer Detection Applications Using Histogram-Based Local Descriptors

Aydin

2023

Diagnostics

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Among the most serious types of cancer is skin cancer. Despite the risk of death, when caught early, the rate of survival is greater than 95%. This inspires researchers to explore methods that allow for the early detection of skin cancer that could save millions of lives. The ability to detect the early signs of skin cancer has become more urgent in light of the rising number of illnesses, the high death rate, and costly healthcare treatments. Given the gravity of these issues, experts have created a number of existing approaches for detecting skin cancer. Identifying skin cancer and whether it is benign or malignant involves detecting features of the lesions such as size, form, symmetry, color, etc. The aim of this study is to determine the most successful skin cancer detection methods by comparing the outcomes and effectiveness of the various applications that categorize benign and malignant forms of skin cancer. Descriptors such as the Local Binary Pattern (LBP), the Local Directional Number Pattern (LDN), the Pyramid of Histogram of Oriented Gradients (PHOG), the Local Directional Pattern (LDiP), and Monogenic Binary Coding (MBC) are used to extract the necessary features. Support vector machines (SVM) and XGBoost are used in the classification process. In addition, this study uses colored histogram-based features to classify the various characteristics obtained from the color images. In the experimental results, the applications implemented with the proposed color histogram-based features were observed to be more successful. Under the proposed method (the colored LDN feature obtained using the YCbCr color space with the XGBoost classifier), a 90% accuracy rate was achieved on Dataset 1, which was obtained from the Kaggle website. For the HAM10000 data set, an accuracy rate of 96.50% was achieved under a similar proposed method (the colored MBC feature obtained using the HSV color space with the XGBoost classifier).

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“…It can also connect people working in healthcare, materials and institutions and can actively manage and respond in an intelligent fashion to the requirements of medical ecosystems [51]. In addition, machine learning techniques have been employed in cochlear implants and various other systems [52][53][54][55] that can be embedded in portable devices in the smart healthcare domain. In tandem with developments in signal processing in cochlear implants, there have also been developments in other components of technology to assist the hearing impaired.…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation of Acoustic Features to Optimize Intelligibility in Cochlear Implants

Henry,

Parsi,

Glavin

et al. 2023

Sensors

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Although cochlear implants work well for people with hearing impairment in quiet conditions, it is well-known that they are not as effective in noisy environments. Noise reduction algorithms based on machine learning allied with appropriate speech features can be used to address this problem. The purpose of this study is to investigate the importance of acoustic features in such algorithms. Acoustic features are extracted from speech and noise mixtures and used in conjunction with the ideal binary mask to train a deep neural network to estimate masks for speech synthesis to produce enhanced speech. The intelligibility of this speech is objectively measured using metrics such as Short-time Objective Intelligibility (STOI), Hit Rate minus False Alarm Rate (HIT-FA) and Normalized Covariance Measure (NCM) for both simulated normal-hearing and hearing-impaired scenarios. A wide range of existing features is experimentally evaluated, including features that have not been traditionally applied in this application. The results demonstrate that frequency domain features perform best. In particular, Gammatone features performed best for normal hearing over a range of signal-to-noise ratios and noise types (STOI = 0.7826). Mel spectrogram features exhibited the best overall performance for hearing impairment (NCM = 0.7314). There is a stronger correlation between STOI and NCM than HIT-FA and NCM, suggesting that the former is a better predictor of intelligibility for hearing-impaired listeners. The results of this study may be useful in the design of adaptive intelligibility enhancement systems for cochlear implants based on both the noise level and the nature of the noise (stationary or non-stationary).

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Design and Development of a Non-Contact ECG-Based Human Emotion Recognition System Using SVM and RF Classifiers

Cited by 9 publications

References 42 publications

Remote Emotion Recognition Using Continuous-Wave Bio-Radar System

Remote Emotion Recognition Using Continuous-Wave Bio-Radar System

A Comparative Analysis of Skin Cancer Detection Applications Using Histogram-Based Local Descriptors

Experimental Investigation of Acoustic Features to Optimize Intelligibility in Cochlear Implants

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