Behind the Mask: Emotion Recognition in Healthcare Students

Bani, Marco; Russo, Selena; Ardenghi, S; Rampoldi, Giulia; Wickline, Virginia B.; Nowicki, Stephen; Strepparava, M

doi:10.1007/s40670-021-01317-8

Cited by 35 publications

(68 citation statements)

References 14 publications

(14 reference statements)

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“…Drastic changes also included the adoption of facial masks. The article by Bani et al explains how facial masks impede our ability to discriminate between different emotions and this poses as a potential barrier to patient-centred care [1]. Facial masks are most likely to remain mandatory in all clinical settings and we support the author's stance in recommending interventions to benefit emotion recognition as part of medical school training.…”

Section: Dear Editormentioning

confidence: 97%

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Response to “Behind the Mask: Emotion Recognition in Healthcare Students”

Lockhart

Sivanandarajah

2021

Med.Sci.Educ.

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Section: Dear Editormentioning

confidence: 97%

“…We read with great interest the article by Bani et al on 'Behind the Mask: Emotion Recognition in Healthcare Students' [1]. This study explores how recognition of emotion is affected by facial masks and its subsequent implications on medical education and practice.…”

Section: Dear Editormentioning

confidence: 99%

Response to “Behind the Mask: Emotion Recognition in Healthcare Students”

Lockhart

Sivanandarajah

2021

Med.Sci.Educ.

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“…A fairly recent study was conducted in detecting the emotional state of patients during the spread of the virus SARS-COV-2, where face masks are mandatory [125]. A facial emotion recognition study was conducted with masked and unmasked versions of data.…”

Section: General Healthcare Application Of Emotion Recogntion Systemsmentioning

confidence: 99%

Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare—A Review

Hasnul

Aziz

Alelyani

et al. 2021

Sensors

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Affective computing is a field of study that integrates human affects and emotions with artificial intelligence into systems or devices. A system or device with affective computing is beneficial for the mental health and wellbeing of individuals that are stressed, anguished, or depressed. Emotion recognition systems are an important technology that enables affective computing. Currently, there are a lot of ways to build an emotion recognition system using various techniques and algorithms. This review paper focuses on emotion recognition research that adopted electrocardiograms (ECGs) as a unimodal approach as well as part of a multimodal approach for emotion recognition systems. Critical observations of data collection, pre-processing, feature extraction, feature selection and dimensionality reduction, classification, and validation are conducted. This paper also highlights the architectures with accuracy of above 90%. The available ECG-inclusive affective databases are also reviewed, and a popularity analysis is presented. Additionally, the benefit of emotion recognition systems towards healthcare systems is also reviewed here. Based on the literature reviewed, a thorough discussion on the subject matter and future works is suggested and concluded. The findings presented here are beneficial for prospective researchers to look into the summary of previous works conducted in the field of ECG-based emotion recognition systems, and for identifying gaps in the area, as well as in developing and designing future applications of emotion recognition systems, especially in improving healthcare.

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“…We read with great interest the recent article "Behind the Mask: Emotion Recognition in Healthcare Students" [1], which found an increased number of errors in emotion recognition of masked versus unmasked individuals by healthcare students. We would like to discuss how this aspect has affected our interactions with patients as current medical students and propose ways to develop relevant communication skills.…”

Section: Dear Editormentioning

confidence: 99%