Digital biomarkers reflect stress reduction after augmented reality guided meditation

Jiang, Yihang; Wang, Will; Scargill, Tim; Rothman, Max; Dunn, Jessilyn; Gorlatova, Maria

doi:10.1145/3539494.3542754

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…As the problem formulated suggests it is a multimodal classification problem, Equation (8) provides the class prediction with output layer activation of softmax (σ o ), which is shown in Equation (6). Other layers have activation of ReLu (σ h ), as shown in Equation (7). The entropy, i.e., the loss to optimize, is chosen to be the categorical crossentropy loss shown in Equation (9).…”

Section: Fully Connected Neural Networkmentioning

confidence: 99%

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Utilizing Machine Learning for Context-Aware Digital Biomarker of Stress in Older Adults

Onim,

Thapliyal,

Rhodus

2024

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Identifying stress in older adults is a crucial field of research in health and well-being. This allows us to take timely preventive measures that can help save lives. That is why a nonobtrusive way of accurate and precise stress detection is necessary. Researchers have proposed many statistical measurements to associate stress with sensor readings from digital biomarkers. With the recent progress of Artificial Intelligence in the healthcare domain, the application of machine learning is showing promising results in stress detection. Still, the viability of machine learning for digital biomarkers of stress is under-explored. In this work, we first investigate the performance of a supervised machine learning algorithm (Random Forest) with manual feature engineering for stress detection with contextual information. The concentration of salivary cortisol was used as the golden standard here. Our framework categorizes stress into No Stress, Low Stress, and High Stress by analyzing digital biomarkers gathered from wearable sensors. We also provide a thorough knowledge of stress in older adults by combining physiological data obtained from wearable sensors with contextual clues from a stress protocol. Our context-aware machine learning model, using sensor fusion, achieved a macroaverage F-1 score of 0.937 and an accuracy of 92.48% in identifying three stress levels. We further extend our work to get rid of the burden of manual feature engineering. We explore Convolutional Neural Network (CNN)-based feature encoder and cortisol biomarkers to detect stress using contextual information. We provide an in-depth look at the CNN-based feature encoder, which effectively separates useful features from physiological inputs. Both of our proposed frameworks, i.e., Random Forest with engineered features and a Fully Connected Network with CNN-based features validate that the integration of digital biomarkers of stress can provide more insight into the stress response even without any self-reporting or caregiver labels. Our method with sensor fusion shows an accuracy and F-1 score of 83.7797% and 0.7552, respectively, without context and 96.7525% accuracy and 0.9745 F-1 score with context, which also constitutes a 4% increase in accuracy and a 0.4 increase in F-1 score from RF.

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Section: Fully Connected Neural Networkmentioning

confidence: 99%

“…Commonly employed digital biomarkers for detecting stress include Electrodermal Activity (EDA), Photoplethysmography (PPG), respiration, Electroencephalogram (EEG), and Skin Temperature (ST) [2]. Multiple studies are showing the differences in digital biomarker responses in different moods or stress levels [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Utilizing Machine Learning for Context-Aware Digital Biomarker of Stress in Older Adults

Onim,

Thapliyal,

Rhodus

2024

Information

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“…Moreover, self-satisfaction was elevated. Jiang et al [133] explored the feasibility of an AR mindfulness intervention on stress management using a sample of 10 healthy adults. The results of physiological measurements and the perceived stress questionnaire showed improvements in the regulation of stress.…”

Section: Decenteringmentioning

confidence: 99%

Digitally Assisted Mindfulness in Training Self-Regulation Skills for Sustainable Mental Health: A Systematic Review

Mitsea,

Drigas,

Skianis

2023

Behavioral Sciences

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The onset of the COVID-19 pandemic has led to an increased demand for mental health interventions, with a special focus on digitally assisted ones. Self-regulation describes a set of meta-skills that enable one to take control over his/her mental health and it is recognized as a vital indicator of well-being. Mindfulness training is a promising training strategy for promoting self-regulation, behavioral change, and mental well-being. A growing body of research outlines that smart technologies are ready to revolutionize the way mental health training programs take place. Artificial intelligence (AI); extended reality (XR) including virtual reality (VR), augmented reality (AR), and mixed reality (MR); as well as the advancements in brain computer interfaces (BCIs) are ready to transform these mental health training programs. Mindfulness-based interventions assisted by smart technologies for mental, emotional, and behavioral regulation seem to be a crucial yet under-investigated issue. The current systematic review paper aims to explore whether and how smart technologies can assist mindfulness training for the development of self-regulation skills among people at risk of mental health issues as well as populations with various clinical characteristics. The PRISMA 2020 methodology was utilized to respond to the objectives and research questions using a total of sixty-six experimental studies that met the inclusion criteria. The results showed that digitally assisted mindfulness interventions supported by smart technologies, including AI-based applications, chatbots, virtual coaches, immersive technologies, and brain-sensing headbands, can effectively assist trainees in developing a wide range of cognitive, emotional, and behavioral self-regulation skills, leading to a greater satisfaction of their psychological needs, and thus mental wellness. These results may provide positive feedback for developing smarter and more inclusive training environments, with a special focus on people with special training needs or disabilities.

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“…In addition, there have been studies on various embodiments to find out how it affects meditation. Jiang et al [16] created an interactive embodiment that visualizes the shaking tree in an AR headset according to the participant's breathing which is biological feedback. At the same time, they meditated while listening to the 5-minute meditation guide.…”

Section: Related Workmentioning

confidence: 99%

Dis/Immersion in Mindfulness Meditation with a Wandering Voice Assistant

Itagaki

Seaborn

2023

Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems

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Mindfulness meditation is a validated means of helping people manage stress. Voice-based virtual assistants (VAs) in smart speakers, smartphones, and smart environments can assist people in carrying out mindfulness meditation through guided experiences. However, the common fixed location embodiment of VAs makes it difficult to provide intuitive support. In this work, we explored the novel embodiment of a "wandering voice" that is co-located with the user and "moves" with the task. We developed a multispeaker VA embedded in a yoga mat that changes location along the body according to the meditation experience. We conducted a qualitative user study in two sessions, comparing a typical fixed smart speaker to the wandering VA embodiment. Thick descriptions from interviews with twelve people revealed sometimes simultaneous experiences of immersion and dis-immersion. We offer design implications for "wandering voices" and a new paradigm for VA embodiment that may extend to guidance tasks in other contexts.

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Digital biomarkers reflect stress reduction after augmented reality guided meditation

Cited by 5 publications

References 23 publications

Utilizing Machine Learning for Context-Aware Digital Biomarker of Stress in Older Adults

Utilizing Machine Learning for Context-Aware Digital Biomarker of Stress in Older Adults

Digitally Assisted Mindfulness in Training Self-Regulation Skills for Sustainable Mental Health: A Systematic Review

Dis/Immersion in Mindfulness Meditation with a Wandering Voice Assistant

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