Consent and engagement, security, and authentic living using wearable and mobile health technology

Kreitmair, Karola; Cho, Mildred K.; Magnus, David

doi:10.1038/nbt.3887

Cited by 25 publications

(18 citation statements)

References 26 publications

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“…The inclusion of eye-tracking features in this modeling effort are difficult to scale, and are associated with necessary calibration and restriction of user head movements [10]. Similarly, physiological sensors add cost, are difficult to scale, have varying levels of data quality [42], and may be associated with privacy issues [43].…”

Section: Discussionmentioning

confidence: 99%

Automatic Detection of Learner Engagement Using Machine Learning and Wearable Sensors

Carroll¹,

Ruble²,

Dranias³

et al. 2020

JBBS

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Training can now be delivered on a large scale through mobile and web-based platforms in which the learner is often distanced from the instructor and their peers. In order to optimize learner engagement and maximize learning in these contexts, instructional content and strategies must be engaging. Key to the development and study of such content and strategies, and adaptation of instructional techniques when learners become disengaged, is the ability to objectively assess engagement in real-time. Previous self-reported metrics, or expensive EEG-based engagement measures are not appropriate for large-scale platforms due to their complexity and cost. Here we describe the development and testing of a measurement and classification technique that utilizes non-invasive physiological and behavioral monitoring technology to directly assess engagement in classroom, simulation, and live training environments. An experimental study was conducted with 45 students and first responders in a unmanned aircraft systems (UAS) training program to assess the ability to accurately assess learner engagement and discriminate between levels of learner engagement within classroom, simulation and live environments via physiological and behavioral inputs. A series of engagement classifiers were developed using cardiovascular, respiratory, electrodermal, movement, and eye-tracking features that were able to successfully classify engagement levels at an accuracy level of 85% with eye-tracking features included or 81% without eye-tracking features. This approach is capable of monitoring, assessing, and tracking learner engagement across learning situations and contexts, and providing real-time and after action feedback to support instructors in modulating learner engagement.

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Section: Discussionmentioning

confidence: 99%

Automatic Detection of Learner Engagement Using Machine Learning and Wearable Sensors

Carroll¹,

Ruble²,

Dranias³

et al. 2020

JBBS

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“…For an informed risk-benefit-analysis, it is importantin my view-to appreciate the actual and potential benefits for patients that big data and advanced machine learning may offer in basic and clinical neuroscience. 4 While my observations here focus on the area of neuroscience, we should acknowledge that advanced machine learning has revolutionized basic and clinical research across all areas in biomedicine and turbocharged the emerging field of Bprecision medicine^ [20]. To provide just a few recent examples: such algorithms have been shown to achieve dermatologist-level accuracy in classifying skin lesions as cancerous [21], to be able to predict the outcome of antiepileptic drug treatment [22] or to predict the prognosis of small-cell lung cancer from images of pathological tissue samples [23].…”

Section: Benefits In Using Big Data and Advanced Machine Learning In mentioning

confidence: 99%

“…What we not yet have on a largescale, but what many device and software companies are now actively developing, are consumer-directed wearable devices for recording and uploading our brain activity, mostly based on electroencephalography (EEG) [1,2]. In combination with other wearable sensors for tracking biometric data, these devices will provide particularly rich multivariate data troves for the Bpersonal sensing^of an individual Bphysiome^, for the (online) decoding of person's (neuro)physiological state and behavior [3], and for making predictions on future states or behavior, an application that is studied particularly intensively in the area of mental health [4][5][6][7]. Meanwhile, companies are using powerful algorithms for Bdeep learning^to create facts on the ground 1 and invest heavily in leveraging these methods for consumer and health-care applications, especially in basic and clinical neuroscience [9,10].…”

Section: Introductionmentioning

confidence: 99%

Big Brain Data: On the Responsible Use of Brain Data from Clinical and Consumer-Directed Neurotechnological Devices

Kellmeyer

2018

Neuroethics

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The focus of this paper are the ethical, legal and social challenges for ensuring the responsible use of Bbig brain data^-the recording, collection and analysis of individuals' brain data on a large scale with clinical and consumer-directed neurotechnological devices. First, I highlight the benefits of big data and machine learning analytics in neuroscience for basic and translational research. Then, I describe some of the technological, social and psychological barriers for securing brain data from unwarranted access. In this context, I then examine ways in which safeguards at the hardware and software level, as well as increasing Bdata literacy^in society, may enhance the security of neurotechnological devices and protect the privacy of personal brain data. Regarding ethical and legal ramifications of big brain data, I first discuss effects on the autonomy, the sense of agency and authenticity, as well as the self that may result from the interaction between users and intelligent, particularly closed-loop, neurotechnological devices. I then discuss the impact of the Bdatafication^in basic and clinical neuroscience research on the just distribution of resources and access to these transformative technologies. In the legal realm, I examine possible legal consequences that arises from the increasing abilities to decode brain states and their corresponding subjective phenomenological experiences on the hitherto inaccessible privacy of these information. Finally, I discuss the implications of big brain data for national and international regulatory policies and models of good data governance.

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“…Therefore, any bias that is engrained at the level of data selection, structuring, labeling and so forth, may be reproduced, inflated and disseminated by an AI system that is trained on these biased data. Many examples in recent years show, how this can lead to a perpetuation of social injustices and discriminations that are based on human biases, e. g. with respect to ethnicity, gender and other social markers (Knight, 2017;Baeza-Yates, 2016). Now, there is no easy fix for this deeply entrenched and interlocked problem of human biases and their spillover effects into AI bias.…”

Section: Bias In Human and Artificial Intelligence In Interactionmentioning

confidence: 99%

Artificial Intelligence in Basic and Clinical Neuroscience: Opportunities and Ethical Challenges

Kellmeyer

2019

Neuroforum

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The analysis of large amounts of personal data with artificial neural networks for deep learning is the driving technology behind new artificial intelligence (AI) systems for all areas in science and technology. These AI methods have evolved from applications in computer vision, the automated analysis of images, and now include frameworks and methods for analyzing multimodal datasets that combine data from many different source, including biomedical devices, smartphones and common user behavior in cyberspace. For neuroscience, these widening streams of personal data and machine learning methods provide many opportunities for basic data-driven research as well as for developing new tools for diagnostic, predictive and therapeutic applications for disorders of the nervous system. The increasing automation and autonomy of AI systems, however, also creates substantial ethical challenges for basic research and medical applications. Here, scientific and medical opportunities as well ethical challenges are summarized and discussed.

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Consent and engagement, security, and authentic living using wearable and mobile health technology

Cited by 25 publications

References 26 publications

Automatic Detection of Learner Engagement Using Machine Learning and Wearable Sensors

Automatic Detection of Learner Engagement Using Machine Learning and Wearable Sensors

Big Brain Data: On the Responsible Use of Brain Data from Clinical and Consumer-Directed Neurotechnological Devices

Artificial Intelligence in Basic and Clinical Neuroscience: Opportunities and Ethical Challenges

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