Leader: Raffaele Gravina (UNICAL); Other collaborator(s): UNIPD
The task focuses on smart wearable technologies to record physiological signals and physical activity data outside strict lab-controlled environments and on AI/machine learning-based analysis to support active and healthy ageing. Specifically, the interest will be focused on the recognition of personality traits in ageing adults. Previous studies established the link between personality and healthy aging but an accurate, prognostic, and predictive personalized model for active and healthy aging is yet to be developed. In this task, we will recognize personality traits during daily life activities and we will design, develop, and evaluate a smart-wearable multi sensor platform able to track behaviors and habits and detect subtle changes potentially associated to personality/cognitive decline. Testing and evaluation will be carried out through a research experimental setting in a realistic instrumented “domestic-like” environment.
During the reference period, the work on this task has been focused on:
Please see the next reporting period.
During the reference period (II quarter 2024), the work on this task has been focused on:
1. Bootstrapping research collaboration: with Prof. Pedro Guerra at the University of Granada in the Mind, Brain and Behavior (CIMCYC) research center. The PhD student Majid Riaz, funded by AgeIT, started his research visit at CIMCYC.
2. Experimental Protocol Development: for data collection, a completed experimental procedure was finalized and required material organized.
3. Data Acquisition: participants were contacted for data acquisition purposes. At the end of June 2024, 8 subjects were recruited and participated in the experiments.
In this reference period:
- We completed experiment and data collection and started the analysis phase (feature extraction, and machine learning model training) over EEG traces.
- we conducted EEG data analysis, feature extraction, and machine learning model training based on previously collected dataset.
- We also began drafting a paper titled “EEG-Based Personality Traits Recognition for Cognitive Health applications” expected to be submitted by next reference period to the IEEE Transactions on Affective Computing journal.
In this reference period:
Jan25 - Completed a research paper for IEEE Transactions on Affective Computing and currently under internal review by an international professor from University of Granada we are collaborating with; Finalized results for a paper submitted to the IEEE International Conference on Human-Machine Systems (ICHMS 2025) in Dubai.
Feb25 - The manuscript submitted to ICHMS2025 has been accepted for publication and the reviewers comments are being addressed. This work analyzes the brain activity of 21 subjects to identify the mental fatigue across different personality traits during the social stressors
Mar25 - Finalized the results and writeup of a paper to be submitted in April 2025 for the AIoT-HMO conference; Started a new research task focused on the classification of EEG signals for Personality traits recognition based on regression and its correlation along with additional extracted EEG features as marker of stress
Research Activities and Dissemination
During the reference period, we studied and analyzed EEG data from 21 participants to investigate mental fatigue across different personality traits under social stress conditions. The analysis was conducted using a proprietary dataset, developed from an extended recording period in controlled settings. This activity led to a research paper titled “EEG Sensing to Assess Mental Fatigue Across Personality Traits Under Cognitive Load”, accepted for publication and presented at the IEEE International Conference on Human and Machine Systems, held in Abu Dhabi in May 2025.
A second study has been devoted to defining a machine learning framework for chronic stress classification, based on the integration of EEG features and standardized personality trait scores. The combined use of psychophysiological and psychological metrics has been validated as a reliable approach for stress detection. The results are detailed in a paper titled “Chronic Stress Recognition Through Multimodal Fusion of EEG Data and Personality Metrics”, accepted for publication to the IEEE International Conference on Systems, Man, and Cybernetics 2025
Ongoing Research and Experimental Activities
Efforts have been devoted to improving a work titled “EEG-Based Computational Model to Identify Personality Traits and Neurocognitive Health Markers in Dynamic Social Stress” previously submitted to the IEEE Transactions on Affective Computing. Specifically, the manuscript has been significantly revised according to reviewers' feedback, with particular emphasis on improving the analysis of personality trait recognition and associated cognitive markers in dynamic social stress contexts.
In addition, ongoing experiments are being conducted using Bitbrain wearable biosensing devices, which include EEG, GSR, temperature, airflow, respiration, and IMU sensors. The current protocol aims to extend data collection to a larger number of participants and real-world scenarios, contributing to the development of a robust multimodal healthcare monitoring system. To this aim, hands-on training with Bitbrain equipment was completed as part of a specialized program led by Bitbrain professionals. The training covered sensor calibration, real-time data acquisition, and validation processes, providing practical experience with the complete data collection pipeline.
Scientific publications