The use of electrodermal activity (EDA) measurement to understand consumer emotions – A literature review and a call for action

Caruelle, Delphine; Gustafsson, Anders; Shams, Poja; Lervik-Olsen, Line

doi:10.1016/j.jbusres.2019.06.041

Cited by 130 publications

(91 citation statements)

References 77 publications

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“…Electrode adhesiveness with the skin can decline because of oil and perspiration [ 38 ] that might vary during the day. Intermittent physical disconnection between electrodes and the skin leads to a sudden drop and rise in EDA signal [ 39 , 40 ]. Kleckner and colleagues suggested that abrupt drops below 0.05

S, which is the generally accepted minimal amplitude criterion of SCR [ 25 ], are likely caused by signal loss due to the electrode detachment when recording from wrist-worn dry-electrode EDA sensor and should be discarded from the analysis [ 41 ].…”

Section: Resultsmentioning

confidence: 99%

“…Quantization noise in wearable EDA had been previously reported [ 50 , 51 , 52 ] but gained little attention. A possible solution to deal with this would be to remove all high-frequency noise by using a moving average or by applying a low-pass filter on the EDA signal, as has been recommended in earlier works [ 32 , 40 ], before extracting SCRs using Ledalab. However, there is no consensus on the smoothing parameter (window size), filter type (finite impulse response (FIR)/infinite impulse response), order (2nd- to 32nd-order), and cutoff frequency (0.4–3 Hz) [ 46 ].…”

Section: Resultsmentioning

confidence: 99%

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A Usability Study of Physiological Measurement in School Using Wearable Sensors

Thammasan

Stuldreher

Schreuders

et al. 2020

Sensors

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Measuring psychophysiological signals of adolescents using unobtrusive wearable sensors may contribute to understanding the development of emotional disorders. This study investigated the feasibility of measuring high quality physiological data and examined the validity of signal processing in a school setting. Among 86 adolescents, a total of more than 410 h of electrodermal activity (EDA) data were recorded using a wrist-worn sensor with gelled electrodes and over 370 h of heart rate data were recorded using a chest-strap sensor. The results support the feasibility of monitoring physiological signals at school. We describe specific challenges and provide recommendations for signal analysis, including dealing with invalid signals due to loose sensors, and quantization noise that can be caused by limitations in analog-to-digital conversion in wearable devices and be mistaken as physiological responses. Importantly, our results show that using toolboxes for automatic signal preprocessing, decomposition, and artifact detection with default parameters while neglecting differences between devices and measurement contexts yield misleading results. Time courses of students’ physiological signals throughout the course of a class were found to be clearer after applying our proposed preprocessing steps.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A Usability Study of Physiological Measurement in School Using Wearable Sensors

Thammasan

Stuldreher

Schreuders

et al. 2020

Sensors

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“…As a result, the reporting of neuro-studies' procedures, design, and data analysis needs to be as complete and transparent as possible. Researchers need to include detailed information on missing data, data cleaning or filtering, thinkable confounding variables, software and hardware used, and baseline measures, allowing other researchers replicate the study and to assess the validity of the data and interpretations (e.g., Caruelle et al, 2019;Lim, 2018;Stanton et al, 2017). This transparency is particularly necessary as different hardware (i.e., tools), software, and data processing may produce different results (e.g., Caruelle et al, 2019;Kennedy and Northover, 2016).…”

Section: Data-analysis and Interpretation Neuro-data Creates New Chamentioning

confidence: 99%

“…The most prevalent neurophysiological tools are galvanic skin response (GSR), cardiovascular measures, electromyography (EMG), and eye tracking (Harris et al, 2018;Mauss and Robinson, 2009;Poels and Dewitte, 2006). GSR captures activity in the sweat glands, which indicates physiological arousal and is measured by electrodes or sensors placed on the skin (Caruelle et al, 2019;Christopoulos et al, 2019;Ohme et al, 2009). GSR is suitable to, amongst other things, investigate attentional and emotional processes (Dawson et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Neuroscience in service research: an overview and discussion of its possibilities

Verhulst¹,

Keyser²,

Gustafsson³

et al. 2019

JOSM

Self Cite

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2 NEUROSCIENCE IN SERVICE RESEARCH: AN OVERVIEW AND DISCUSSION OF ITS POSSIBILITIES STRUCTURED ABSTRACTPurpose: The paper discusses recent developments in neuroscientific methods and demonstrates its potential for the service field. This work is a call to action for more service researchers to adopt promising and increasingly accessible neuro-tools that allow the service field to benefit from neuroscience theories and insights.Design/methodology/approach: The paper synthesizes key literature from a variety of domains (e.g., neuroscience, consumer neuroscience, organizational neuroscience) to provide an in-depth background to start applying neuro-tools. Specifically, this paper outlines the most important neuro-tools today and discusses their theoretical and empirical value.Findings: To date, the use of neuro-tools in the service field is limited. This is surprising given the great potential they hold to advance service research. To stimulate the use of neurotools in the service area, the authors provide a roadmap to enable neuroscientific service studies and conclude with a discussion on promising areas (e.g., service experience, servicescape) ripe for neuroscientific input.Originality/value: The paper offers service researchers a starting point to understand the potential benefits of adopting the neuroscientific method and shows their complementarity with traditional service research methods like surveys, experiments, and qualitative research.In addition, this paper may also help reviewers and editors to better assess the quality of neuro-studies in service.

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“…Finally, the EDA signal is studied in several papers, [26,42,49,53,55,57,58]. A complete study about the EDA signal is shown in [41,59].…”

mentioning

confidence: 99%

Activity Recognition Using Wearable Physiological Measurements: Selection of Features from a Comprehensive Literature Study

Mohíno-Herranz

Gil-Pita

Rosa-Zurera

et al. 2019

Sensors

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Activity and emotion recognition based on physiological signal processing in health care applications is a relevant research field, with promising future and relevant applications, such as health at work or preventive care. This paper carries out a deep analysis of features proposed to extract information from the electrocardiogram, thoracic electrical bioimpedance, and electrodermal activity signals. The activities analyzed are: neutral, emotional, mental and physical. A total number of 533 features are tested for activity recognition, performing a comprehensive study taking into consideration the prediction accuracy, feature calculation, window length, and type of classifier. Feature selection to know the most relevant features from the complete set is implemented using a genetic algorithm, with a different number of features. This study has allowed us to determine the best number of features to obtain a good error probability avoiding over-fitting, and the best subset of features among those proposed in the literature. The lowest error probability that is obtained is 22.2%, with 40 features, a least squares error classifier, and 40 s window length.

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The use of electrodermal activity (EDA) measurement to understand consumer emotions – A literature review and a call for action

Cited by 130 publications

References 77 publications

A Usability Study of Physiological Measurement in School Using Wearable Sensors

A Usability Study of Physiological Measurement in School Using Wearable Sensors

Neuroscience in service research: an overview and discussion of its possibilities

Activity Recognition Using Wearable Physiological Measurements: Selection of Features from a Comprehensive Literature Study

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