Portable System for Real-Time Detection of Stress Level

Minguillón, Jesús; Pérez, Eduardo; López-Gordo, M. A.; Pelayo, Francisco J.; Sanchez-Carrion, Maria Jose

doi:10.3390/s18082504

Cited by 74 publications

(44 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Although Fp1 is known as a suboptimal location for detecting human drowsiness, EEG measurements taken at Fp1 have been demonstrated to be correlated with drowsiness [40]. Furthermore, it is well-established that frontal area activity correlates with human emotion and stress [41,42]. When drowsiness can be estimated using the measurements from Fp1, the EEG can be used to infer other mental states of the user.…”

Section: Model Developmentmentioning

confidence: 99%

Portable Drowsiness Detection through Use of a Prefrontal Single-Channel Electroencephalogram

Ogino¹,

Mitsukura

2018

Sensors

View full text Add to dashboard Cite

Drowsiness detection has been studied in the context of evaluating products, assessing driver alertness, and managing office environments. Drowsiness level can be readily detected through measurement of human brain activity. The electroencephalogram (EEG), a device whose application relies on adhering electrodes to the scalp, is the primary method used to monitor brain activity. The many electrodes and wires required to perform an EEG place considerable constraints on the movement of users, and the cost of the device limits its availability. For these reasons, conventional EEG devices are not used in practical studies and businesses. Many potential practical applications could benefit from the development of a wire-free, low-priced device; however, it remains to be elucidated whether portable EEG devices can be used to estimate human drowsiness levels and applied within practical research settings and businesses. In this study, we outline the development of a drowsiness detection system that makes use of a low-priced, prefrontal single-channel EEG device and evaluate its performance in an offline analysis and a practical experiment. Firstly, for the development of the system, we compared three feature extraction methods: power spectral density (PSD), autoregressive (AR) modeling, and multiscale entropy (MSE) for detecting characteristics of an EEG. In order to efficiently select a meaningful PSD, we utilized step-wise linear discriminant analysis (SWLDA). Time-averaging and robust-scaling were used to fit the data for pattern recognition. Pattern recognition was performed by a support vector machine (SVM) with a radial basis function (RBF) kernel. The optimal hyperparameters for the SVM were selected by the grind search method so as to increase drowsiness detection accuracy. To evaluate the performance of the detections, we calculated classification accuracy using the SVM through 10-fold cross-validation. Our model achieved a classification accuracy of 72.7% using the PSD with SWLDA and the SVM. Secondly, we conducted a practical study using the system and evaluated its performance in a practical situation. There was a significant difference (* p < 0.05) between the drowsiness-evoked task and concentration-needed task. Our results demonstrate the efficacy of our low-priced portable drowsiness detection system in quantifying drowsy states. We anticipate that our system will be useful to practical studies with aims as diverse as measurement of classroom mental engagement, evaluation of movies, and office environment evaluation.

show abstract

Section: Model Developmentmentioning

confidence: 99%

Portable Drowsiness Detection through Use of a Prefrontal Single-Channel Electroencephalogram

Ogino¹,

Mitsukura

2018

Sensors

View full text Add to dashboard Cite

show abstract

“…After a stress inducing experiment, they concluded that the selected biosignals, their processing methods and the machine learning algorithm gave optimistic results about the precise detection and classification of stress levels. Minguillon et al (2018) [75] proposed the collection and statistical analysis of a multitude of signals for stress level characterization (EEG, ECG, EMG, GSR) summarizing the existing literature.…”

Section: -Implementations Of the Analysis Methodsmentioning

confidence: 99%

Overview of Biosignal Analysis Methods for the Assessment of Stress

Ladakis

Chouvarda

2021

Emerg Sci J

View full text Add to dashboard Cite

Objectives: Stress is a normal reaction of the human organism induced in situations that demand a level of activation. This reaction has both positive and negative impact on the life of each individual. Thus, the problem of stress management is vital for the maintenance of a person’s psychological balance. This paper aims at the brief presentation of stress definition and various factors that can lead to augmented stress levels. Moreover, a brief synopsis of biosignals that are used for the detection and categorization of stress and their analysis is presented. Methods: Several studies, articles and reviews were included after literature research. The main questions of the research were: the most important and widely used physiological signals for stress detection/assessment, the analysis methods for their manipulation and the implementation of signal analysis for stress detection/assessment in various developed systems. Findings: The main conclusion is that current researching approaches lead to more sophisticated methods of analysis and more accurate systems of stress detection and assessment. However, the lack of a concrete framework towards stress detection and assessment remains a great challenge for the research community. Doi: 10.28991/esj-2021-01267 Full Text: PDF

show abstract

“…Secara umum, kami menganggap bahwa, kenaikan nilai relative power beta menunjukkan keadaan tubuh subjek yang terkesan gelisah dan menimbulkan peningkatan gelombang otak sehingga manifestasi dari stres seperti gelisah, frustasi, ataupun marah yang muncul saat distimulasi oleh permainan ini. Hal ini juga ditunjukkan dari studi terdahulu yang menggunakan gelombang beta sebagai salah satu fitur parameter EEG dalam mendeteksi stres [15].…”

Section: Hasil Dan Analisa Pembahasanunclassified

Objective Stress Measurement: Studi Korelasi Parameter Saliva Amylase Dan Aktivitas Gelombang Otak Menggunakan Electroencephalograph (Eeg)

et al. 2020

View full text Add to dashboard Cite

Stres menjadi suatu permasalahan sosial yang berdampak luas, seperti ekonomi, sosial, agama, dan aspek lainnya di masyarakat. Dengan dampak tersebut, instrumen stress measurement diyakini memiliki urgensi dalam menyelesaikan permasalahan tersebut. Hingga kini, mayoritas studi pengukuran tingkat stres masih menggunakan metode personal assessment (kuesioner) yang memungkinkan terjadinya inkonsistensi dalam proses evaluasinya. Metode pengukur stres lain dengan menggunakan sampel saliva pada manusia yang diyakini mengandung hormon cortisol yang dilepaskan saat stres/manifestasinya muncul. Namun, waktu handling cukup lama dan hasil yang diperoleh tidak konsisten jika dilakukan pengukuran berulang. Penelitian ini bertujuan menginvestigasi bagian otak yang memiliki korelasi antara parameter gelombang otak menggunakan Electroencephalograph (EEG) dengan perangkat saliva amylase chip monitor sebagai salah satu upaya mengestimasi tingkat stres pada seseorang secara objektif menggunakan sampel saliva. Sepuluh orang subjek berpartisipasi dalam penelitian ini. Dengan memberikan stimulasi untuk meningkatkan tingkat stres pada seseorang menggunakan sebuah permainan komputer, respon otak menunjukkan korelasi yang kuat pada bagian frontal/otak depan terhadap hasil pengukuran saliva amylase, terutama pada aktivitas gelombang beta pada bagian F8 dengan tujuh dari sepuluh subjek memiliki nilai korelasi ≥ 0.7. Hal ini menunjukkan bahwa aktivitas gelombang otak pada bagian depan merupakan bagian terpenting untuk mengukur tingkat stres secara objektif menggunakan sinyal biologis secara real time.

show abstract

Portable System for Real-Time Detection of Stress Level

Cited by 74 publications

References 53 publications

Portable Drowsiness Detection through Use of a Prefrontal Single-Channel Electroencephalogram

Portable Drowsiness Detection through Use of a Prefrontal Single-Channel Electroencephalogram

Overview of Biosignal Analysis Methods for the Assessment of Stress

Objective Stress Measurement: Studi Korelasi Parameter Saliva Amylase Dan Aktivitas Gelombang Otak Menggunakan Electroencephalograph (Eeg)

Contact Info

Product

Resources

About