Self-supervised ECG representation learning for emotion recognition
We exploit a self-supervised deep multi-task learning framework for electrocardiogram
(ECG)-based emotion recognition. The proposed solution consists of two stages of learning …
(ECG)-based emotion recognition. The proposed solution consists of two stages of learning …
Measuring cognitive load in augmented reality with physiological methods: A systematic review
Background Cognitive load during AR use has been measured conventionally by
performance tests and subjective rating. With the growing interest in physiological …
performance tests and subjective rating. With the growing interest in physiological …
Self-supervised learning for ecg-based emotion recognition
We present an electrocardiogram (ECG)-based emotion recognition system using self-
supervised learning. Our proposed architecture consists of two main networks, a signal …
supervised learning. Our proposed architecture consists of two main networks, a signal …
[HTML][HTML] Advancements in AI for cardiac arrhythmia detection: A comprehensive overview
Cardiovascular diseases (CVDs) are a global health concern, demanding advanced
healthcare solutions. Accurate identification of CVDs via electrocardiogram (ECG) analysis …
healthcare solutions. Accurate identification of CVDs via electrocardiogram (ECG) analysis …
A transformer architecture for stress detection from ecg
B Behinaein, A Bhatti, D Rodenburg… - Proceedings of the …, 2021 - dl.acm.org
Electrocardiogram (ECG) has been widely used for emotion recognition. This paper
presents a deep neural network based on convolutional layers and a transformer …
presents a deep neural network based on convolutional layers and a transformer …
Cardiogan: Attentive generative adversarial network with dual discriminators for synthesis of ecg from ppg
Electrocardiogram (ECG) is the electrical measurement of cardiac activity, whereas
Photoplethysmogram (PPG) is the optical measurement of volumetric changes in blood …
Photoplethysmogram (PPG) is the optical measurement of volumetric changes in blood …
SIM-CNN: self-supervised individualized multimodal learning for stress prediction on nurses using biosignals
S Eom, S Eom, P Washington - Workshop on Machine Learning for …, 2023 - Springer
Precise stress recognition from biosignals is inherently challenging due to the
heterogeneous nature of stress, individual physiological differences, and scarcity of labeled …
heterogeneous nature of stress, individual physiological differences, and scarcity of labeled …
Attx: Attentive cross-connections for fusion of wearable signals in emotion recognition
We propose cross-modal attentive connections, a new dynamic and effective technique for
multimodal representation learning from wearable data. Our solution can be integrated into …
multimodal representation learning from wearable data. Our solution can be integrated into …
Cognitive load monitoring with wearables–lessons learned from a machine learning challenge
To further extend the applicability of wearable sensors, methods for accurately extracting
subtle psychological information from the sensor data are required. However, accessing …
subtle psychological information from the sensor data are required. However, accessing …
Psychophysiologic measures of cognitive load in physician team leaders during trauma resuscitation
E Johannessen, A Szulewski, N Radulovic… - Computers in Human …, 2020 - Elsevier
In the high-paced and dynamic clinical setting of an emergency department, a physician's
ability to manage mental resources and mitigate the effects of cognitive overload is critical to …
ability to manage mental resources and mitigate the effects of cognitive overload is critical to …