Analysis of physiological signals in response to stress using ECG and respiratory signals of automobile drivers

Soman, Karthik; Alex, V.; Srinivas, Chodagam

doi:10.1109/imac4s.2013.6526476

Cited by 24 publications

(17 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such a signal may be electrocardiographic (ECG) data which can be used to infer a user's stress and emotion level [6], [7]. This type of data can serve as an indicator for capturing driving stress experienced by users.…”

Section: B Physiological Metricsmentioning

confidence: 99%

Understanding user daily mobility using mobile and wearable sensing systems

Faye

Engel

2016

2016 International Conference on Information and Communication Technology Convergence (ICTC)

View full text Add to dashboard Cite

Abstract-Recent technological advances and the ever-greater developments in sensing and computing continue to provide new ways of understanding our daily mobility. Smart devices such as smartphones or smartwatches can, for instance, provide an enhanced user experience based on different sets of built-in sensors that follow every user action and identify its environment. Monitoring solutions such as these, which are becoming more and more common, allow us to assess human behavior and movement at different levels. In this article, we focus on the concept of human mobility. With the participation of 13 individuals, we carried out an experiment to discover how groups of sensors currently available in smartphones and smartwatches can help to distinguish different profiles and patterns of human mobility. We show that it is possible to use not only motion sensors but also physiological sensors and environmental data provided, for instance, by Wi-Fi. Finally, detailed study of these categories enables us to offer a way of representing the mobility of individual users, based on anonymized traces and graph theory.

show abstract

Section: B Physiological Metricsmentioning

confidence: 99%

Understanding user daily mobility using mobile and wearable sensing systems

Faye

Engel

2016

2016 International Conference on Information and Communication Technology Convergence (ICTC)

View full text Add to dashboard Cite

show abstract

“…and increase when there is a higher demanding task to accomplish, such as performing a sprint. Not only physical tasks will cause the heart rate to adapt, but also mental challenges and, as shown by [6], also stress. It is known that the sympathetic and parasympathetic nervous systems both have major and significantly different influence on the HRV [5].…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Driver Stress while Transiting Road Tunnels

Manseer

Riener

2014

Adjunct Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

View full text Add to dashboard Cite

According to the reported numbers of fatal crashes, road tunnels on highways pose a severe safety risk. Based on our own experience and observations, it is supposed that one substantial reason is an increased level of anger or stress, observable through elevated levels of cognitive demand. To confirm these assumptions, we conducted an on-road experiment aimed at revealing how the human mind is affected (in terms of stress levels) when traveling through tunnels compared to driving on the open road. Test subjects were continuously monitored with an ECG device and had to steer the vehicle on a 100km long highway segment with 24 tunnel passages. Qualitative and quantitative evaluation reveals that individuals in fact experience an elevated stress level, while feeling uncomfortable with the situation. A combination of these two circumstances suggests that, in critical situations, the body might react with tonical immobility as part of its acute stress response, having devastating consequences on the road. Future work is therefore strongly encouraged to identify major stressors, so to alleviate increased stress and, thus, to increase traffic safety.

show abstract

“…The studies related to stress recognition analyze the stress responses on sympathetic nervous system and parasympathetic nervous system based on the fact that SNS is stimulated in a stressed state, and PNS in a relaxed state [2]. These analyses are widely realized on ECG [7][8][9][10][11][12][13][14][15][16][17][18][20][21][22][23][24][25][26][27], GSR [7-14, 16, 17, 19, 20, 23, 25-27], RESP [7, 9-17, 20, 23, 25, 26], EMG [7, 9-11, 14, 16, 17, 20, 21, 26], and PPG [19] signals.…”

Section: Introductionmentioning

confidence: 99%

“…The variations in the heart beats are analyzed by Heart Rate (HR) and Heart Rate Variability (HRV) measurements using ECG [7][8][9][10][11][12][13][14][15][16][17][18][20][21][22][23][24][25][26][27] and PPG [19] signals. Heart rate is the frequency of heart beats per minute, and HRV indicates the variations between heart beats.…”

Section: Introductionmentioning

confidence: 99%

“…The total spectral power (TP) [19], the spectral powers of very low frequency band (VLF) [18,19,23], the low frequency band (LF) [9, 10, 14, 17-19, 23, 25, 26], the high frequency band (HF) [9,10,18,19,23,25,26], and the LF/HF ratio [9,10,12,13,18,19,[23][24][25][26] are the frequently used HRV measurements in Fourier domain. Besides, the VLF/TP ratio [12,13], the LF/VLF ratio [12,13], the HF/VLF ratio [12,13], the spectrum entropy [12]; the QRS power spectrum [15], the power samples of the R-peaks [21], the mean and the standard deviation in Wavelet domain [25] are also used for spectral HRV analysis.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sürücü Stres Seviyesi Tanıma için Stres-Seviyesine-Özgü Yeni Bir Öznitelik Topluluğu

Esener

2019

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

View full text Add to dashboard Cite

This paper proposes a novel feature set for drivers' stress level recognition. The proposed feature set consists of data-independent and almost uncorrelated feature pairs for each stress level with very strong intra-class and relatively weak inter-class correlations, constructed by realizing a correlation analysis on the popular features studied in the literature. By using the proposed feature set, a maximum of 100% stress level recognition accuracy is achieved with an average increment of 24.85% while a mean reduction rate of 88.01% is satisfied in false positive rate compared to the full feature set. These outcomes clearly show that the proposed feature set can confidently be integrated into the driving assistance systems.

show abstract

Analysis of physiological signals in response to stress using ECG and respiratory signals of automobile drivers

Cited by 24 publications

References 3 publications

Understanding user daily mobility using mobile and wearable sensing systems

Understanding user daily mobility using mobile and wearable sensing systems

Evaluation of Driver Stress while Transiting Road Tunnels

Sürücü Stres Seviyesi Tanıma için Stres-Seviyesine-Özgü Yeni Bir Öznitelik Topluluğu

Contact Info

Product

Resources

About