Meditation Detection Using Sensors from Wearable Devices

Casado, Constantino Álvarez; Paananen, Petteri; Siirtola, Pekka; Pirttikangas, Susanna; López, Miguel Bordallo

doi:10.1145/3460418.3479318

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…Our study demonstrates excellent mix-subject and intra-subject classification performance as well as reasonably good inter-subject classification. When our mindfulness state classification methods are integrated with wearable EEG sensors ( Anwar et al, 2018 ; Álvarez Casado et al, 2021 ) and virtual reality ( Mistry et al, 2020 ; Viczko et al, 2021 ) in future research, a neurofeedback assisted MBSR training system may be developed and can help make MBSR more effective and accessible to the general populations.…”

Section: Discussionmentioning

confidence: 99%

EEG-based investigation of effects of mindfulness meditation training on state and trait by deep learning and traditional machine learning

Shang,

Duan,

et al. 2023

Front. Hum. Neurosci.

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IntroductionThis study examines the state and trait effects of short-term mindfulness-based stress reduction (MBSR) training using convolutional neural networks (CNN) based deep learning methods and traditional machine learning methods, including shallow and deep ConvNets as well as support vector machine (SVM) with features extracted from common spatial pattern (CSP) and filter bank CSP (FBCSP).MethodsWe investigated the electroencephalogram (EEG) measurements of 11 novice MBSR practitioners (6 males, 5 females; mean age 35.7 years; 7 Asians and 4 Caucasians) during resting and meditation at early and late training stages. The classifiers are trained and evaluated using inter-subject, mix-subject, intra-subject, and subject-transfer classification strategies, each according to a specific application scenario.ResultsFor MBSR state effect recognition, trait effect recognition using meditation EEG, and trait effect recognition using resting EEG, from shallow ConvNet classifier we get mix-subject/intra-subject classification accuracies superior to related previous studies for both novice and expert meditators with a variety of meditation types including yoga, Tibetan, and mindfulness, whereas from FBSCP + SVM classifier we get inter-subject classification accuracies of 68.50, 85.00, and 78.96%, respectively.ConclusionDeep learning is superior for state effect recognition of novice meditators and slightly inferior but still comparable for both state and trait effects recognition of expert meditators when compared to the literatures. This study supports previous findings that short-term meditation training has EEG-recognizable state and trait effects.

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Section: Discussionmentioning

confidence: 99%

EEG-based investigation of effects of mindfulness meditation training on state and trait by deep learning and traditional machine learning

Shang,

Duan,

et al. 2023

Front. Hum. Neurosci.

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“…In medicine, PPG analysis is a basic and common tool in healthcare services to monitor vital signs such as heart rate (HR) or oxygen saturation (SpO 2 ) [4]. In well-being, it became increasingly important thanks to the success of wearable devices that analyze sleep disorders [5], cardiovascular diseases [6], or detection of stress and meditation [7]. In sports, PPG analysis became an important tool to improve the intrinsic and extrinsic athletes' performance [8].…”

Section: Introductionmentioning

confidence: 99%

Face2PPG: An Unsupervised Pipeline for Blood Volume Pulse Extraction From Faces

Casado,

López

2023

IEEE J. Biomed. Health Inform.

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Photoplethysmography (PPG) signals have become a key technology in many fields, such as medicine, well-being, or sports. Our work proposes a set of pipelines to extract remote PPG signals (rPPG) from the face robustly, reliably, and configurable. We identify and evaluate the possible choices in the critical steps of unsupervised rPPG methodologies. We assess a state-of-the-art processing pipeline in six different datasets, incorporating important corrections in the methodology that ensure reproducible and fair comparisons. In addition, we extend the pipeline by proposing three novel ideas; 1) a new method to stabilize the detected face based on a rigid mesh normalization; 2) a new method to dynamically select the different regions in the face that provide the best raw signals, and 3) a new RGB to rPPG transformation method, called Orthogonal Matrix Image Transformation (OMIT) based on QR decomposition, that increases robustness against compression artifacts. We show that all three changes introduce noticeable improvements in retrieving rPPG signals from faces, obtaining state-of-the-art results compared with unsupervised, non-learning-based methodologies and, in some databases, very close to supervised, learning-based methods. We perform a comparative study to quantify the contribution of each proposed idea. In addition, we depict a series of observations that could help in future implementations.

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Evaluation of video-based rPPG in challenging environments: Artifact mitigation and network resilience

Nguyen,

et al. 2024

Computers in Biology and Medicine

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Meditation Detection Using Sensors from Wearable Devices

Cited by 7 publications

References 16 publications

EEG-based investigation of effects of mindfulness meditation training on state and trait by deep learning and traditional machine learning

EEG-based investigation of effects of mindfulness meditation training on state and trait by deep learning and traditional machine learning

Face2PPG: An Unsupervised Pipeline for Blood Volume Pulse Extraction From Faces

Evaluation of video-based rPPG in challenging environments: Artifact mitigation and network resilience

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