The field of physiological and behavioral sensing is rapidly advancing, with a focus on developing innovative platforms and systems for continuous monitoring and analysis. Recent developments have led to the creation of open-source platforms, such as smart rings, that enable reproducible acquisition of rich physiological and behavioral datasets. Additionally, there has been a surge in research on multimodal sensing, including the integration of EEG, EMG, and other modalities to enhance emotion recognition, gesture classification, and mental health monitoring. Noteworthy papers in this area include the introduction of MiSTR, a deep-learning framework for speech synthesis from intracranial EEG signals, and the development of FDC-Net, a novel framework that deeply couples denoising and emotion recognition tasks for end-to-end noise-robust emotion recognition. These advancements have the potential to revolutionize various applications, including healthcare, human-computer interfaces, and affective computing.
Advancements in Physiological and Behavioral Sensing
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{\tau}-Ring: A Smart Ring Platform for Multimodal Physiological and Behavioral Sensing
Contact Sensors to Remote Cameras: Quantifying Cardiorespiratory Coupling in High-Altitude Exercise Recovery
AnnoSense: A Framework for Physiological Emotion Data Collection in Everyday Settings for AI
Real-World Receptivity to Adaptive Mental Health Interventions: Findings from an In-the-Wild Study
MiSTR: Multi-Modal iEEG-to-Speech Synthesis with Transformer-Based Prosody Prediction and Neural Phase Reconstruction
AI on the Pulse: Real-Time Health Anomaly Detection with Wearable and Ambient Intelligence
Spatial Imputation Drives Cross-Domain Alignment for EEG Classification
SocialPulse: An On-Smartwatch System for Detecting Real-World Social Interactions
SCOUT: An in-vivo Methane Sensing System for Real-time Monitoring of Enteric Emissions in Cattle with ex-vivo Validation
Wearable Music2Emotion : Assessing Emotions Induced by AI-Generated Music through Portable EEG-fNIRS Fusion
MENDR: Manifold Explainable Neural Data Representations
SparseEMG: Computational Design of Sparse EMG Layouts for Sensing Gestures
CWEFS: Brain volume conduction effects inspired channel-wise EEG feature selection for multi-dimensional emotion recognition
ADSEL: Adaptive dual self-expression learning for EEG feature selection via incomplete multi-dimensional emotional tagging
FDC-Net: Rethinking the association between EEG artifact removal and multi-dimensional affective computing