EmoSense: An Ambulatory Device for the Assessment of ANS Activity—Application in the Objective Evaluation of Stress With the Blind

Massot, Bertrand; Baltenneck, Nicolas; Géhin, Claudine; Dittmar, A.; McAdams, Eric

doi:10.1109/jsen.2011.2132703

Cited by 30 publications

(23 citation statements)

References 17 publications

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“…A number of features related to signal power and complexity were extracted using the PyEEG open source Python module [39]. For each of the 14 EEG channels, we computed the relative spectral power [40] in the delta (0.5-4 Hz), theta (4-7 Hz), alpha-1 (7-10 Hz), alpha-2 (10-13 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) Hz), and gamma (30-60 Hz) bands using the Power Spectral Intensity (PSI) and Relative Intensity Ratio (RIR) functions:…”

Section: Feature Extractionmentioning

confidence: 99%

“…In recent years, the advent of ubiquitous mobile and sensing technologies, consumer brain-computer interfaces (BCI), and the quantified self movement has driven the development of wireless wearable multi-sensor systems (from devices to smartphone apps) for easy and reliable automatic collection of brain and peripheral biosignal data streams, making it possible to monitor human affective states in virtually any real-world situation [11], [25]. Massot and colleagues [26] used a custom mobile biosensor to collect EDA from 27 blind pedestrians as they walked through urban environments of varying complexity. Examination of arousalrelevant EDA features showed that VIP experience increased psychological stress when walking on busy shopping streets, passing through large open areas, and crossing junctions.…”

Section: Introductionmentioning

confidence: 99%

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Multimodal Classification of Stressful Environments in Visually Impaired Mobility Using EEG and Peripheral Biosignals

Saitis

Kalimeri

2021

IEEE Trans. Affective Comput.

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In this study, we aim to better understand the cognitive-emotional experience of visually impaired people when navigating in unfamiliar urban environments, both outdoor and indoor. We propose a multimodal framework based on random forest classifiers, which predict the actual environment among predefined generic classes of urban settings, inferring on real-time, non-invasive, ambulatory monitoring of brain and peripheral biosignals. Model performance reached 93% for the outdoor and 87% for the indoor environments (expressed in weighted AUROC), demonstrating the potential of the approach. Estimating the density distributions of the most predictive biomarkers, we present a series of geographic and temporal visualizations depicting the environmental contexts in which the most intense affective and cognitive reactions take place. A linear mixed model analysis revealed significant differences between categories of vision impairment, but not between normal and impaired vision. Despite the limited size of our cohort, these findings pave the way to emotionally intelligent mobility-enhancing systems, capable of implicit adaptation not only to changing environments but also to shifts in the affective state of the user in relation to different environmental and situational factors.

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Section: Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multimodal Classification of Stressful Environments in Visually Impaired Mobility Using EEG and Peripheral Biosignals

Saitis

Kalimeri

2021

IEEE Trans. Affective Comput.

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“…There exist different studies which try to detect with subject emotions in different manner. Study for blind people [5] Analyze autonomic nervous system activity is a subject. Estimation of emotion on one modality (GSR) [6] has shown possibilities to achieve outcome.…”

Section: State Of Artmentioning

confidence: 99%

Data Analysis By Using Machine Learning Algorithm On Controller for Estimating Emotions

Sharma¹,

Kapoor²

2014

IJCSA

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Emotions are an unstoppable and uncontrollable aspect of mental state of human. Some bad situations give stress and leads to different sufferings. One can't avoid situation but can have awareness when body feel stress or any other emotion. It becomes easy for doctors whose patient is not in condition to speak. In that case person's physiological parameters are measured to decide emotional status. While experiencing different emotion, there are also physiological changes taking place in the human body, like variations in the heart rate (ECG/HRV), skin conductance (GSR), breathing rate(BR), blood volume pulse(BVP),brain waves (EEG), temperature and muscle tension. These were some of the metrics to sense emotive coefficient. This research paper objective is to design and develop a portable, cost effective and low power embedded system that can predict different emotions by using Naïve Bayes classifiers which are based on probability models that incorporate class conditional independence assumptions. Inputs to this system are various physiological signals and are extracted by using different sensors. Portable microcontroller used in this embedded system is MSP430F2013 to automatically monitor the level of stress in computer. This paper reports on the hardware and software instrumentation development and signal processing approach used to detect the stress level of a subject.To check the device's performance, few experiments were done in which 20 adults (ten women and ten men) who completed different tests requiring a certain degree of effort, such as showing facing intense interviews in office.

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“…the sensors) available. There are already many applications which could be integrated into this platform: HRV and EDA analysis can be used for the monitoring of mental stress [24], combined with accelerometers to derive information on physical state. There are, for example, Polar devices and mobile applications integrating physical training with virtual coaches.…”

Section: A Mobile Applicationsmentioning

confidence: 99%

On designing an ubiquitous sensor network for health monitoring

Massot

Noury

Géhin

et al. 2013

2013 IEEE 15th International Conference on E-Health Networking, Applications and Services (Healthcom 2013)

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Managing chronic health conditions at home, through the early detection of symptoms and subsequent rapid remedial action will be therapeutically and financially more effective. Unfortunately, this widely agreed upon statement implies a significant adaptation and/or evolution of present healthcare delivery, from a technological and social point of view. Designing reliable, embedded, non-intrusive sensors adapted to long-term monitoring; processing and collecting health indicators from multiple data sources; and the development of a connective healthcare delivery spanning the clinical domain and the daily environment of a given individual, are undoubted challenges facing the shift from an historical model based on the treatment of acute diseases. In this paper, we present the key possibilities capable of enabling successful implementation of health monitoring systems at home and on the move, as well as the remaining challenges to be addressed to implement these promising solutions.

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EmoSense: An Ambulatory Device for the Assessment of ANS Activity—Application in the Objective Evaluation of Stress With the Blind

Cited by 30 publications

References 17 publications

Multimodal Classification of Stressful Environments in Visually Impaired Mobility Using EEG and Peripheral Biosignals

Multimodal Classification of Stressful Environments in Visually Impaired Mobility Using EEG and Peripheral Biosignals

Data Analysis By Using Machine Learning Algorithm On Controller for Estimating Emotions

On designing an ubiquitous sensor network for health monitoring

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