BRAVE Lab’s PI, Alberto Greco

The activities of BRAVE Lab follow a coherent scientific and technological pathway.

We begin with the design of innovative experimental protocols, move to the acquisition and modelling of physiological signals, use these signals to infer emotional states, extend the analysis to interpersonal physiological coupling, and ultimately translate these models into adaptive systems and biofeedback technologies.

This integrated approach allows us not only to measure and understand human affective dynamics, but also to design systems that can positively influence social interaction, emotional regulation, and well-being.

Research question

Understanding and recognising human emotion objectively and continuously remains one of the major challenges. Despite advances in psychophysiology and artificial intelligence, we still lack reliable tools to measure how internal emotional states unfold over time and shape human experience.

BRAVE Lab’s activity

At BRAVE Lab, we study emotion as a measurable physiological process. By combining wearable sensing, computational modelling, and adaptive technologies, we develop systems capable of estimating, reproducing, and modulating internal states directly from biosignals.

What we are building

Building on this foundation, we extend our research to how emotional and physiological dynamics unfold during social interaction, investigating how individuals influence each other and how this knowledge can be translated into technologies that enhance well-being and human experience.

Our research pathway is articulated through four tightly connected research areas ↓

Physiological Signal Processing & Modelling

At BRAVE Lab, physiological signals are treated as rich, dynamic representations of the human internal state.

We work with multimodal recordings including ECG (from which heart rate variability is derived), EDA (electrodermal activity), respiration, and EEG, acquired through wearable and unobtrusive sensing systems. Our activity focuses on advanced preprocessing, feature extraction, and time-series modelling to transform raw biosignals into informative variables that reflect autonomic nervous system (ANS) activity. We develop statistical, machine learning, and Bayesian models capable of capturing the temporal structure and variability of physiological processes.

This foundational work enables the translation of biological dynamics into quantitative representations of latent states that can be used for emotion estimation, synchronization analysis, and real-time adaptive technologies.

Here is our most representative paper about this research line

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Affective Computing

BRAVE Lab develops computational AI models to infer emotional states from psychophysiological data.

Rather than relying solely on self-report or behavioral observation, we estimate continuous dimensions such as arousal, valence, stress, and anxiety using probabilistic, machine learning, and deep learning approaches.

Our research focuses on identifying the physiological signatures that underlie emotional dynamics and on building models that can operate also in real time. This enables the reconstruction of an individual’s internal emotional trajectory during interaction or exposure to complex environments.

From our perspective, affective computing represents the bridge between physiological signals and interpretable emotional representations that can inform adaptive systems, clinical tools, and human–AI interaction.

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Adaptive Systems, Virtual Reality and Biofeedback

A central research theme at BRAVE Lab is the translation of computational models of physiology and emotion into adaptive technologies that interact with users in real time.

We develop biofeedback systems and virtual reality environments capable of adapting their behaviour according to the user’s estimated internal state. These systems are applied to scenarios such as social anxiety, phobias, therapeutic support, human–AI interaction, and social training.

By closing the loop between sensing, modelling, and actuation, we create environments that respond dynamically to the individual. This approach allows us not only to measure emotional processes, but also to influence and guide them through immersive, interactive technologies.

Here is our most representative paper about this research line

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Interpersonal Physiological Coupling

During social interaction, human physiological systems tend to align and influence each other in subtle but measurable ways.

At BRAVE Lab, we study this phenomenon as interpersonal physiological coupling, a process through which people affect each other emotionally and biologically. Using hyperscanning approaches and multimodal sensing, we analyse synchrony, directionality, and temporal alignment across heart rate variability, electrodermal activity, respiration, and neural signals. This work supports the idea that social connection can be described as a dynamical coupling between physiological processes.

Our models aim to quantify emotional contagion, bonding, and social coordination, providing an objective framework to study how humans connect beyond observable behaviour.

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Greco Alberto

Research Center "E.Piaggio", School of Engineering, University of Pisa, Italy

Verified email at centropiaggio.unipi.it

Biomedical System and Signal Processing Physiological models Wearable monitoring systems Electrodermal activity

Abel: Integrating Large Language Models into the Cognitive Architecture of a Hyper-Realistic Humanoid for Natural Social Interaction

L Cominelli, F Bossi, FA Galatolo, MGCA Cimino, GA Manduzio, A Greco, ... International Journal of Social Robotics 18 (7), 107, 2026

BackgroundLarge Language Models (LLMs) have greatly expanded the capabilities of natural language processing, but their direct use in real-time social robotics remains difficult because conversational context, timing, and behavioral alignment must be managed continuously. We propose a hybrid architecture that combines dual-LLM inference with an LLM Orchestration Layer to address these requirements.ObjectiveThis study explores how...

Detection and Mitigation of Random Limb Movements in Radar-Based Vital Sign Monitoring

M De Maria, IR Lorato, A Gargano, G Marrara, G Araniti, G D’Arrigo, ... IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, 2026

Radar-based vital. sign monitoring enables non-contact and privacy-preserving assessment of respiratory and cardiac activity. However, its performance degrades in the presence of random body movements (RBMs), which introduce nonlinear distortions in the Doppler phase signal and hinder reliable respiratory rate (RR) and heart rate (HR) estimation. This paper addresses this limitation by introducing a processing...

Adaptive exposure therapy for Social Anxiety Disorder: a preliminary validation in virtual reality

S Frumento, N Paparo, M Briglia, M Martini, M Chessa, A Greco, ... Proceedings of the 2026 European Conference on Cognitive Ergonomics, 98-104, 2026

Social Anxiety Disorder (SAD) represents an increasingly prevalent challenge for healthcare. Exposure therapy is the elective treatment that has undergone significant development over last decades thanks to Virtual Reality (VR): its last frontier is represented by adaptive scenarios changing in real time to maximize efficacy and tolerability of the exposure. In the present study, 15...

Characterizing the behavioral subtypes of phobic avoidance

S Frumento, E Magnavacca, A Iannizzotto, A Gemignani, EP Scilingo, ... Communications Psychology, 2026

Avoidance is a core psychopathological symptom, yet its elective assessment remains overly simplistic. Here, we introduce a standardized Virtual Reality Behavioral Avoidance Test (vr-BAT) for a multidimensional, dynamic characterization of avoidance behavior. In 75 participants with varying arachnophobia levels, we validated the vr-BAT by demonstrating that key avoidance metrics (Distance, Time, and Velocity) correlated robustly...

Mapping the metabolic correlates of working memory in a naturalistic environment: a whole-room indirect calorimetry study

E Cipriani, G Rho, P Piaggi, A Basolo, A Landi, F Santini, A Gemignani, ... Scientific Reports, 2026

Whole-room indirect calorimetry (WRIC) is an advanced platform to assess basal and activity-related metabolic expenditure, but its sensitivity to cognitive activity-induced metabolic fluctuations remains largely unexplored. In this exploratory study, we investigated whether WRIC measurements covary with variations in working memory load. Eighteen healthy adults completed two experimental sessions inside a WRIC chamber: a working...

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