Ethical Considerations and Checklist for Affective Research With Wearables

Behnke, Maciej; Saganowski, Stanisław; Kunc, Dominika; Kazienko, Przemysław

doi:10.1109/taffc.2022.3222524

Cited by 9 publications

(2 citation statements)

References 46 publications

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“…To date, the research and development of the Digital Health Checklist has been applied to digital health protocols in adult samples, including for use in cardiovascular disease prevention [24]; studies of human emotion [25]; and improvement of informed consent communications [26]. The current investigation extended the use of the Digital Health Checklist to research involving parents of infants and children.…”

Section: The "Digital Health Checklist" For Use In Child Development ...mentioning

confidence: 99%

Evaluating Users’ Experiences of a Child Multimodal Wearable Device: Mixed Methods Approach

McElwain,

Fisher,

Nebeker

et al. 2024

JMIR Hum Factors

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Background Wearable devices permit the continuous, unobtrusive collection of data from children in their natural environments and can transform our understanding of child development. Although the use of wearable devices has begun to emerge in research involving children, few studies have considered families’ experiences and perspectives of participating in research of this kind. Objective Through a mixed methods approach, we assessed parents’ and children’s experiences of using a new wearable device in the home environment. The wearable device was designed specifically for use with infants and young children, and it integrates audio, electrocardiogram, and motion sensors. Methods In study 1, semistructured phone interviews were conducted with 42 parents of children aged 1 month to 9.5 years who completed 2 day-long recordings using the device, which the children wore on a specially designed shirt. In study 2, a total of 110 parents of children aged 2 months to 5.5 years responded to a questionnaire assessing their experience of completing 3 day-long device recordings in the home. Guided by the Digital Health Checklist, we assessed parental responses from both studies in relation to the following three key domains: (1) access and usability, (2) privacy, and (3) risks and benefits. Results In study 1, most parents viewed the device as easy to use and safe and remote visits as convenient. Parents’ views on privacy related to the audio recordings were more varied. The use of machine learning algorithms (vs human annotators) in the analysis of the audio data, the ability to stop recordings at any time, and the view that the recordings reflected ordinary family life were some reasons cited by parents who expressed minimal, if any, privacy concerns. Varied risks and benefits were also reported, including perceived child comfort or discomfort, the need to adjust routines to accommodate the study, the understanding gained from the study procedures, and the parent’s and child’s enjoyment of study participation. In study 2, parents’ ratings on 5 close-ended items yielded a similar pattern of findings. Compared with a “neutral” rating, parents were significantly more likely to agree that (1) device instructions were helpful and clear (t109=−45.98; P<.001), (2) they felt comfortable putting the device on their child (t109=−22.22; P<.001), and (3) they felt their child was safe while wearing the device (t109=−34.48; P<.001). They were also less likely to worry about the audio recordings gathered by the device (t108=6.14; P<.001), whereas parents’ rating of the burden of the study procedures did not differ significantly from a “neutral” rating (t109=−0.16; P=.87). Conclusions On the basis of parents’ feedback, several concrete changes can be implemented to improve this new wearable platform and, ultimately, parents’ and children’s experiences of using child wearable devices in the home setting.

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Section: The "Digital Health Checklist" For Use In Child Development ...mentioning

confidence: 99%

Evaluating Users’ Experiences of a Child Multimodal Wearable Device: Mixed Methods Approach

McElwain,

Fisher,

Nebeker

et al. 2024

JMIR Hum Factors

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“…Following these recommendations can help researchers ensure that their work is conducted with the utmost rigor, thereby enhancing its credibility and facilitating meaningful advances in the field. For additional guidance, particularly on physiological measures, researchers should consult Behnke et al (2022), who provide a checklist for responsible wearable use in affective research. Taken together, these guidelines can serve as a valuable resource for those already engaged in affective computing research and for those considering entering this burgeoning field.…”

Section: Recommendationsmentioning

confidence: 99%

Exploring the Underlying Emotional Models in Emotion Recognition Systems with Electrodermal Activity

D'Amelio,

Galán,

Maldonado

et al. 2023

Preprint

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Affective computing is an interdisciplinary field that aims to automatically recognize and interpret emotions. Recent research has focused on using physiological signals (e.g., electrodermal activity) to improve emotion recognition. However, little attention has been paid to the theoretical emotion models underlying these systems. Here, we conducted a systematic review and meta-analysis of the literature on automatic emotion recognition systems using electrodermal activity. We found that models predicting arousal generally outperformed those predicting valence, which is consistent with our pre-registered hypothesis. This finding aligns well with the conceptual framework that views arousal as a psychological and physiological state linked to autonomic nervous system activity, making it more directly related to electrodermal activity. Furthermore, we observed a discrepancy between the types of machine learning models used, mainly classification models, and the emotional models adopted, often of a dimensional nature. Specifically, despite the increased use of dimensional affective models, there has been no corresponding increase in the use of regression models, which would be consistent with the continuous nature of these data. We conclude that a comprehensive understanding of affective states requires consideration of both psychological and computational perspectives in affective computing research.

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Exploring the Ethical Dimensions and Societal Consequences of Affective Computing

Mishra,

Deshpande,

Anna

et al. 2024

The Springer Series in Applied Machine Learning

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Ethical Considerations and Checklist for Affective Research With Wearables

Cited by 9 publications

References 46 publications

Evaluating Users’ Experiences of a Child Multimodal Wearable Device: Mixed Methods Approach

Evaluating Users’ Experiences of a Child Multimodal Wearable Device: Mixed Methods Approach

Exploring the Underlying Emotional Models in Emotion Recognition Systems with Electrodermal Activity

Exploring the Ethical Dimensions and Societal Consequences of Affective Computing

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