Detecting User Emotions with the True-Depth Camera to Support Mobile App Quality Assurance

Scherr, Simon André; Kammler, Christian; Elberzhager, Frank

doi:10.1109/seaa.2019.00034

Cited by 8 publications

(3 citation statements)

References 9 publications

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“…A total of 30,000 infrared dots are projected onto the target object and captured by an infrared-sensitive camera, allowing fine details such as contours and recesses to be detected by the camera [65]. This granularity has enabled its use in a wide range of applications, ranging from the analysis of human facial emotions to the customization of prosthetics and medical equipment requiring precise fit [63,[66][67][68][69][70][71].…”

Section: Facial Morphometrics and Malnutrition Screeningmentioning

confidence: 99%

Use of Facial Morphology to Determine Nutritional Status in Older Adults: Opportunities and Challenges

Tay¹,

Quek²,

Kaur³

et al. 2022

JMIR Public Health Surveill

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Undiagnosed malnutrition is a significant problem in high-income countries, which can reduce the quality of life of many individuals, particularly of older adults. Moreover, it can also inflate the costs of existing health care systems because of the many metabolic complications that it can cause. The current methods for assessing malnutrition can be cumbersome. A trained practitioner must be present to conduct an assessment, or patients must travel to facilities with specialized equipment to obtain their measurements. Therefore, digital health care is a possible way of closing this gap as it is rapidly gaining traction as a scalable means of improving efficiency in the health care system. It allows for the remote monitoring of nutritional status without requiring the physical presence of practitioners or the use of advanced medical equipment. As such, there is an increasing interest in expanding the range of digital applications to facilitate remote monitoring and management of health issues. In this study, we discuss the feasibility of a novel digital remote method for diagnosing malnutrition using facial morphometrics. Many malnutrition screening assessments include subjective assessments of the head and the face. Facial appearance is often used by clinicians as the first point of qualitative indication of health status. Hence, there may be merit in quantifying these subtle but observable changes using facial morphometrics. Modern advancements in artificial intelligence, data science, sensors, and computing technologies allow facial features to be accurately digitized, which could potentially allow these previously intuitive assessments to be quantified. This study aims to stimulate further discussion and discourse on how this emerging technology can be used to provide real-time access to nutritional status. The use of facial morphometrics extends the use of currently available technology and may provide a scalable, easily deployable solution for nutritional status to be monitored in real time. This will enable clinicians and dietitians to keep track of patients remotely and provide the necessary intervention measures as required, as well as providing health care institutions and policy makers with essential information that can be used to inform and enable targeted public health approaches within affected populations.

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Section: Facial Morphometrics and Malnutrition Screeningmentioning

confidence: 99%

Use of Facial Morphology to Determine Nutritional Status in Older Adults: Opportunities and Challenges

Tay¹,

Quek²,

Kaur³

et al. 2022

JMIR Public Health Surveill

View full text Add to dashboard Cite

show abstract

“…For example, Scherr et al (2019b) and Mennig et al (2019) uses the mobile phone cameras to recognize facial muscle movements and associate them to the users' emotions when using different features of an app. This methodology was recently applied to enable user validation of new requirements (Scherr et al, 2019a) and to for identifying usability issues (Johanssen et al, 2019a) with minimal privacy concerns (Stade et al, 2019). Part of the authors of the current paper previously proposed using biometrics in requirements elicitation interviews (Spoletini et al, 2016).…”

Section: Sentiment and Emotions In Requirements Engineeringmentioning

confidence: 99%

“…The field of affective RE recognised the role of users' emotions and studied it extensively. Contributions include applications of sentiment analysis to app reviews (Guzman and Maalej, 2014;Kurtanović and Maalej, 2018), analysis of users' facial expressions (Scherr et al, 2019a;Mennig et al, 2019), the study of physiological reactions to ambiguity (Spoletini et al, 2016), and the augmentation of goal models with user emotions elicited through psychometric surveys (Taveter et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Using Voice and Biofeedback to Predict User Engagement during Requirements Interviews

Ferrari¹,

Huichapa²,

Spoletini³

et al. 2021

Preprint

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Capturing users' engagement is crucial for gathering feedback about the features of a software product. In a market-driven context, current approaches to collect and analyze users' feedback are based on techniques leveraging information extracted from product reviews and social media. These approaches are hardly applicable in bespoke software development, or in contexts in which one needs to gather information from specific users. In such cases, companies need to resort to face-to-face interviews to get feedback on their products. In this paper, we propose to utilize biometric data, in terms of physiological and voice features, to complement interviews with information

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A Descriptive Model of Passive and Natural Passive Human-Computer Interaction

Mallas,

Xenos,

Katsanos

2022

Human-Computer Interaction. Theoretical Approaches and Design Methods

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Detecting User Emotions with the True-Depth Camera to Support Mobile App Quality Assurance

Cited by 8 publications

References 9 publications

Use of Facial Morphology to Determine Nutritional Status in Older Adults: Opportunities and Challenges

Use of Facial Morphology to Determine Nutritional Status in Older Adults: Opportunities and Challenges

Using Voice and Biofeedback to Predict User Engagement during Requirements Interviews

A Descriptive Model of Passive and Natural Passive Human-Computer Interaction

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