Sistema Para La Deteccion De Patologias Psicologicas: Stresstic

Franco, Javier Sedano; Chira, Camelia; Bernal, Jerónimo Javier González; Flecha, José Ramón Villar

doi:10.6036/4429

Cited by 4 publications

(2 citation statements)

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“…In addition, it is important to mention that most of these studies have been focused on analyzing ECG and EDA (electrodermal activity) signals, which can present diverse problems. For instance, the ECG signals can be affected by several heart conditions such as arrhythmias [22]; on the other hand, EDA signals are strongly affected by sweat generated, which depends on the area of the body that is being studied [23]. In this regard, EMG signals can offer an alternative to detect automobile drivers with stress since it presents several advantages in comparison with those mentioned above.…”

Section: Introductionmentioning

confidence: 99%

“…They mention that their proposal is capable of recognizing stressed automobile drivers with an accuracy of 58%. [22]; on the other hand, EDA signals are strongly affected by sweat generated, which depends on the area of the body that is being studied [23]. In this regard, EMG signals can offer an alternative to detect automobile drivers with stress since it presents several advantages in comparison with those mentioned above.…”

Section: Introductionmentioning

confidence: 99%

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An Explainable Machine Learning Approach Based on Statistical Indexes and SVM for Stress Detection in Automobile Drivers Using Electromyographic Signals

Vargas-Lopez

Perez-Ramirez

Valtierra-Rodríguez

et al. 2021

Sensors

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The economic and personal consequences that a car accident generates for society have been increasing in recent years. One of the causes that can generate a car accident is the stress level the driver has; consequently, the detection of stress events is a highly desirable task. In this article, the efficacy that statistical time features (STFs), such as root mean square, mean, variance, and standard deviation, among others, can reach in detecting stress events using electromyographical signals in drivers is investigated, since they can measure subtle changes that a signal can have. The obtained results show that the variance and standard deviation coupled with a support vector machine classifier with a cubic kernel are effective for detecting stress events where an AUC of 0.97 is reached. In this sense, since SVM has different kernels that can be trained, they are used to find out which one has the best efficacy using the STFs as feature inputs and a training strategy; thus, information about model explain ability can be determined. The explainability of the machine learning algorithm allows generating a deeper comprehension about the model efficacy and what model should be selected depending on the features used to its development.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Explainable Machine Learning Approach Based on Statistical Indexes and SVM for Stress Detection in Automobile Drivers Using Electromyographic Signals

Vargas-Lopez

Perez-Ramirez

Valtierra-Rodríguez

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

Hybrid Systems for Analyzing the Movements during a Temporary Breath Inability Episode

Álvarez

González

Sedano

et al. 2014

Lecture Notes in Computer Science

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Soft Computing for the Analysis of People Movement Classification

Sedano

González

Baruque

et al. 2013

Advances in Intelligent Systems and Computing

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This article presents a study of the best data acquisition conditions regarding movements of extremities in people. By using an accelerometer, there exist different ways of collecting and storing the data captured while people moving. To know which one of these options is the best one, in terms of classification, an empirical study is presented in this paper. As a soft computing technique for validation, Self-Organizing maps have been chosen due to their visualization capability. Empirical verification and comparison of the proposed classification methods are performed in a real domain, where three similar movements in the real-life are analyzed.

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Sistema Para La Deteccion De Patologias Psicologicas: Stresstic

Cited by 4 publications

References 0 publications

An Explainable Machine Learning Approach Based on Statistical Indexes and SVM for Stress Detection in Automobile Drivers Using Electromyographic Signals

An Explainable Machine Learning Approach Based on Statistical Indexes and SVM for Stress Detection in Automobile Drivers Using Electromyographic Signals

Hybrid Systems for Analyzing the Movements during a Temporary Breath Inability Episode

Soft Computing for the Analysis of People Movement Classification

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