AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can expand the Abstract, Links and BibTex record for each paper.
2025
Chang, K. -Y.; Lee, C. -F.
Enhancing Virtual Restorative Environment with Generative AI: Personalized Immersive Stress-Relief Experiences Proceedings Article
In: V.G., Duffy (Ed.): Lect. Notes Comput. Sci., pp. 132–144, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 03029743 (ISSN); 978-303193501-5 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence generated content, Artificial Intelligence Generated Content (AIGC), Electroencephalography, Electroencephalography (EEG), Generative AI, Immersive, Immersive environment, Mental health, Physical limitations, Restorative environment, Stress relief, Virtual reality exposure therapies, Virtual reality exposure therapy, Virtual Reality Exposure Therapy (VRET), Virtualization
@inproceedings{chang_enhancing_2025,
title = {Enhancing Virtual Restorative Environment with Generative AI: Personalized Immersive Stress-Relief Experiences},
author = {K. -Y. Chang and C. -F. Lee},
editor = {Duffy V.G.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105007759157&doi=10.1007%2f978-3-031-93502-2_9&partnerID=40&md5=ee620a5da9b65e90ccb1eaa75ec8b724},
doi = {10.1007/978-3-031-93502-2_9},
isbn = {03029743 (ISSN); 978-303193501-5 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {15791 LNCS},
pages = {132–144},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {In today’s fast-paced world, stress and mental health challenges are becoming more common. Restorative environments help people relax and recover emotionally, and Virtual Reality Exposure Therapy (VRET) offers a way to experience these benefits beyond physical limitations. However, most VRET applications rely on pre-designed content, limiting their adaptability to individual needs. This study explores how Generative AI can enhance VRET by creating personalized, immersive environments that better match users’ preferences and improve relaxation. To evaluate the impact of AI-generated restorative environments, we combined EEG measurements with user interviews. Thirty university students participated in the study, experiencing two different modes: static mode and walking mode. The EEG results showed an increase in Theta (θ) and High Beta (β) brain waves, suggesting a state of deep immersion accompanied by heightened cognitive engagement and mental effort. While participants found the experience enjoyable and engaging, the AI-generated environments tended to create excitement and focus rather than conventional relaxation. These findings suggest that for AI-generated environments in VRET to be more effective for stress relief, future designs should reduce cognitive load while maintaining immersion. This study provides insights into how AI can enhance relaxation experiences and introduces a new perspective on personalized digital stress-relief solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},
keywords = {Artificial intelligence generated content, Artificial Intelligence Generated Content (AIGC), Electroencephalography, Electroencephalography (EEG), Generative AI, Immersive, Immersive environment, Mental health, Physical limitations, Restorative environment, Stress relief, Virtual reality exposure therapies, Virtual reality exposure therapy, Virtual Reality Exposure Therapy (VRET), Virtualization},
pubstate = {published},
tppubtype = {inproceedings}
}
In today’s fast-paced world, stress and mental health challenges are becoming more common. Restorative environments help people relax and recover emotionally, and Virtual Reality Exposure Therapy (VRET) offers a way to experience these benefits beyond physical limitations. However, most VRET applications rely on pre-designed content, limiting their adaptability to individual needs. This study explores how Generative AI can enhance VRET by creating personalized, immersive environments that better match users’ preferences and improve relaxation. To evaluate the impact of AI-generated restorative environments, we combined EEG measurements with user interviews. Thirty university students participated in the study, experiencing two different modes: static mode and walking mode. The EEG results showed an increase in Theta (θ) and High Beta (β) brain waves, suggesting a state of deep immersion accompanied by heightened cognitive engagement and mental effort. While participants found the experience enjoyable and engaging, the AI-generated environments tended to create excitement and focus rather than conventional relaxation. These findings suggest that for AI-generated environments in VRET to be more effective for stress relief, future designs should reduce cognitive load while maintaining immersion. This study provides insights into how AI can enhance relaxation experiences and introduces a new perspective on personalized digital stress-relief solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Koizumi, M.; Ohsuga, M.; Corchado, J. M.
Development and Assessment of a System to Help Students Improve Self-compassion Proceedings Article
In: R., Chinthaginjala; P., Sitek; N., Min-Allah; K., Matsui; S., Ossowski; S., Rodríguez (Ed.): Lect. Notes Networks Syst., pp. 43–52, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 23673370 (ISSN); 978-303182072-4 (ISBN).
Abstract | Links | BibTeX | Tags: Avatar, Generative adversarial networks, Generative AI, Health issues, Mental health, Self-compassion, Students, Training program, University students, Virtual avatar, Virtual environments, Virtual Reality, Virtual Space, Virtual spaces, Visual imagery
@inproceedings{koizumi_development_2025,
title = {Development and Assessment of a System to Help Students Improve Self-compassion},
author = {M. Koizumi and M. Ohsuga and J. M. Corchado},
editor = {Chinthaginjala R. and Sitek P. and Min-Allah N. and Matsui K. and Ossowski S. and Rodríguez S.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218979175&doi=10.1007%2f978-3-031-82073-1_5&partnerID=40&md5=b136d4a114ce5acfa89f907ccecc145f},
doi = {10.1007/978-3-031-82073-1_5},
isbn = {23673370 (ISSN); 978-303182072-4 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lect. Notes Networks Syst.},
volume = {1259},
pages = {43–52},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Mental health issues are becoming more prevalent among university students. The mindful self-compassion (MSC) training program, which was introduced to address this issue, has shown some efficacy. However, many people, particularly Japanese people, have difficulty recalling visual imagery or feel uncomfortable or resistant to treating themselves with compassion. This study proposes and develops a system that uses virtual space and avatars to help individuals improve their self-compassion. In the proposed system, the user first selects an avatar of a person with whom to talk (hereafter referred to as “partner”), and then talks about the problem to the avatar of his/her choice. Next, the user changes viewpoints and listens to the problem as the partner’s avatar and responds with compassion. Finally, the user returns to his/her own avatar and listens to the compassionate response spoken as the partner avatar. We first conducted surveys to understand the important system components, and then developed prototypes. In light of the results of the experiments, we improved the prototype by introducing a generative AI. The first prototype used the user’s spoken voice as it was, but the improved system uses the generative AI to organize and convert the voice and present it. In addition, we added a function to generate and add advice with compression. The proposed system is expected to contribute to the improvement of students’ self-compassion. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},
keywords = {Avatar, Generative adversarial networks, Generative AI, Health issues, Mental health, Self-compassion, Students, Training program, University students, Virtual avatar, Virtual environments, Virtual Reality, Virtual Space, Virtual spaces, Visual imagery},
pubstate = {published},
tppubtype = {inproceedings}
}
Mental health issues are becoming more prevalent among university students. The mindful self-compassion (MSC) training program, which was introduced to address this issue, has shown some efficacy. However, many people, particularly Japanese people, have difficulty recalling visual imagery or feel uncomfortable or resistant to treating themselves with compassion. This study proposes and develops a system that uses virtual space and avatars to help individuals improve their self-compassion. In the proposed system, the user first selects an avatar of a person with whom to talk (hereafter referred to as “partner”), and then talks about the problem to the avatar of his/her choice. Next, the user changes viewpoints and listens to the problem as the partner’s avatar and responds with compassion. Finally, the user returns to his/her own avatar and listens to the compassionate response spoken as the partner avatar. We first conducted surveys to understand the important system components, and then developed prototypes. In light of the results of the experiments, we improved the prototype by introducing a generative AI. The first prototype used the user’s spoken voice as it was, but the improved system uses the generative AI to organize and convert the voice and present it. In addition, we added a function to generate and add advice with compression. The proposed system is expected to contribute to the improvement of students’ self-compassion. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Fang, A.; Chhabria, H.; Maram, A.; Zhu, H.
Social Simulation for Everyday Self-Care: Design Insights from Leveraging VR, AR, and LLMs for Practicing Stress Relief Proceedings Article
In: Conf Hum Fact Comput Syst Proc, Association for Computing Machinery, 2025, ISBN: 979-840071394-1 (ISBN).
Abstract | Links | BibTeX | Tags: design, Design insights, Language Model, Large language model, large language models, Mental health, Peer support, Professional supports, Self-care, Social simulations, Speed dating, Virtual environments, Virtual Reality, Well being
@inproceedings{fang_social_2025,
title = {Social Simulation for Everyday Self-Care: Design Insights from Leveraging VR, AR, and LLMs for Practicing Stress Relief},
author = {A. Fang and H. Chhabria and A. Maram and H. Zhu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005770377&doi=10.1145%2f3706598.3713115&partnerID=40&md5=87d43f04dfd3231cb189fa89570824c5},
doi = {10.1145/3706598.3713115},
isbn = {979-840071394-1 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Conf Hum Fact Comput Syst Proc},
publisher = {Association for Computing Machinery},
abstract = {Stress is an inevitable part of day-to-day life yet many find themselves unable to manage it themselves, particularly when professional or peer support are not always readily available. As self-care becomes increasingly vital for mental well-being, this paper explores the potential of social simulation as a safe, virtual environment for practicing in-the-moment stress relief for everyday social situations. Leveraging the immersive capabilities of VR, AR, and LLMs to create realistic interactions and environments, we developed eight interactive prototypes for various social stress related scenarios (e.g. public speaking, interpersonal conflict) across design dimensions of modality, interactivity, and mental health guidance in order to conduct prototype-driven semi-structured interviews with 19 participants. Our qualitative findings reveal that people currently lack effective means to support themselves through everyday stress and perceive social simulation - even at low immersion and interaction levels - to fill a gap for practical, controlled training of mental health practices. We outline key design needs for developing social simulation for self-care needs, and identify important considerations including risks of trauma from hyper-realism, distrust of LLM-recommended timing for mental health recommendations, and the value of accessibility for self-care interventions. © 2025 Copyright held by the owner/author(s).},
keywords = {design, Design insights, Language Model, Large language model, large language models, Mental health, Peer support, Professional supports, Self-care, Social simulations, Speed dating, Virtual environments, Virtual Reality, Well being},
pubstate = {published},
tppubtype = {inproceedings}
}
Stress is an inevitable part of day-to-day life yet many find themselves unable to manage it themselves, particularly when professional or peer support are not always readily available. As self-care becomes increasingly vital for mental well-being, this paper explores the potential of social simulation as a safe, virtual environment for practicing in-the-moment stress relief for everyday social situations. Leveraging the immersive capabilities of VR, AR, and LLMs to create realistic interactions and environments, we developed eight interactive prototypes for various social stress related scenarios (e.g. public speaking, interpersonal conflict) across design dimensions of modality, interactivity, and mental health guidance in order to conduct prototype-driven semi-structured interviews with 19 participants. Our qualitative findings reveal that people currently lack effective means to support themselves through everyday stress and perceive social simulation - even at low immersion and interaction levels - to fill a gap for practical, controlled training of mental health practices. We outline key design needs for developing social simulation for self-care needs, and identify important considerations including risks of trauma from hyper-realism, distrust of LLM-recommended timing for mental health recommendations, and the value of accessibility for self-care interventions. © 2025 Copyright held by the owner/author(s).
2024
Greca, A. D.; Amaro, I.; Barra, P.; Rosapepe, E.; Tortora, G.
Enhancing therapeutic engagement in Mental Health through Virtual Reality and Generative AI: A co-creation approach to trust building Proceedings Article
In: M., Cannataro; H., Zheng; L., Gao; J., Cheng; J.L., Miranda; E., Zumpano; X., Hu; Y.-R., Cho; T., Park (Ed.): Proc. - IEEE Int. Conf. Bioinform. Biomed., BIBM, pp. 6805–6811, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835038622-6 (ISBN).
Abstract | Links | BibTeX | Tags: Co-creation, Electronic health record, Fundamental component, Generative adversarial networks, Generative AI, generative artificial intelligence, Immersive, Mental health, Personalized therapies, Personalized Therapy, Three-dimensional object, Trust, Trust building, Virtual environments, Virtual Reality, Virtual Reality (VR)
@inproceedings{greca_enhancing_2024,
title = {Enhancing therapeutic engagement in Mental Health through Virtual Reality and Generative AI: A co-creation approach to trust building},
author = {A. D. Greca and I. Amaro and P. Barra and E. Rosapepe and G. Tortora},
editor = {Cannataro M. and Zheng H. and Gao L. and Cheng J. and Miranda J.L. and Zumpano E. and Hu X. and Cho Y.-R. and Park T.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217278235&doi=10.1109%2fBIBM62325.2024.10822177&partnerID=40&md5=ed42f7ca6a0e52e9945402e2c439a7f0},
doi = {10.1109/BIBM62325.2024.10822177},
isbn = {979-835038622-6 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. - IEEE Int. Conf. Bioinform. Biomed., BIBM},
pages = {6805–6811},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Trust is a fundamental component of effective therapeutic relationships, significantly influencing patient engagement and treatment outcomes in mental health care. This paper presents a preliminary study aimed at enhancing trust through the co-creation of virtual therapeutic environments using generative artificial intelligence (AI). We propose a multimodal AI model, integrated into a virtual reality (VR) platform developed in Unity, which generates three-dimensional (3D) objects from textual descriptions. This approach allows patients to actively participate in shaping their therapeutic environment, fostering a collaborative atmosphere that enhances trust between patients and therapists. The methodology is structured into four phases, combining non-immersive and immersive experiences to co-create personalized therapeutic spaces and 3D objects symbolizing emotional or psychological states. Preliminary results demonstrate the system's potential in improving the therapeutic process through the real-time creation of virtual objects that reflect patient needs, with high-quality mesh generation and semantic coherence. This work offers new possibilities for patient-centered care in mental health services, suggesting that virtual co-creation can improve therapeutic efficacy by promoting trust and emotional engagement. © 2024 IEEE.},
keywords = {Co-creation, Electronic health record, Fundamental component, Generative adversarial networks, Generative AI, generative artificial intelligence, Immersive, Mental health, Personalized therapies, Personalized Therapy, Three-dimensional object, Trust, Trust building, Virtual environments, Virtual Reality, Virtual Reality (VR)},
pubstate = {published},
tppubtype = {inproceedings}
}
Trust is a fundamental component of effective therapeutic relationships, significantly influencing patient engagement and treatment outcomes in mental health care. This paper presents a preliminary study aimed at enhancing trust through the co-creation of virtual therapeutic environments using generative artificial intelligence (AI). We propose a multimodal AI model, integrated into a virtual reality (VR) platform developed in Unity, which generates three-dimensional (3D) objects from textual descriptions. This approach allows patients to actively participate in shaping their therapeutic environment, fostering a collaborative atmosphere that enhances trust between patients and therapists. The methodology is structured into four phases, combining non-immersive and immersive experiences to co-create personalized therapeutic spaces and 3D objects symbolizing emotional or psychological states. Preliminary results demonstrate the system's potential in improving the therapeutic process through the real-time creation of virtual objects that reflect patient needs, with high-quality mesh generation and semantic coherence. This work offers new possibilities for patient-centered care in mental health services, suggesting that virtual co-creation can improve therapeutic efficacy by promoting trust and emotional engagement. © 2024 IEEE.
Wong, A.; Zhao, Y.; Baghaei, N.
Effects of Customizable Intelligent VR Shopping Assistant on Shopping for Stress Relief Proceedings Article
In: U., Eck; M., Sra; J., Stefanucci; M., Sugimoto; M., Tatzgern; I., Williams (Ed.): Proc. - IEEE Int. Symp. Mixed Augment. Real. Adjunct, ISMAR-Adjunct, pp. 304–308, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-833150691-9 (ISBN).
Abstract | Links | BibTeX | Tags: Customisation, Customizable, generative artificial intelligence, Head-mounted-displays, Helmet mounted displays, Immersive, Mental health, mHealth, Realistic rendering, stress, Stress relief, Users' experiences, Virtual environments, Virtual Reality, Virtual shopping, Virtual shopping assistant
@inproceedings{wong_effects_2024,
title = {Effects of Customizable Intelligent VR Shopping Assistant on Shopping for Stress Relief},
author = {A. Wong and Y. Zhao and N. Baghaei},
editor = {Eck U. and Sra M. and Stefanucci J. and Sugimoto M. and Tatzgern M. and Williams I.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214427097&doi=10.1109%2fISMAR-Adjunct64951.2024.00069&partnerID=40&md5=1530bc0a2139fb33b1a2917c3eb31296},
doi = {10.1109/ISMAR-Adjunct64951.2024.00069},
isbn = {979-833150691-9 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. - IEEE Int. Symp. Mixed Augment. Real. Adjunct, ISMAR-Adjunct},
pages = {304–308},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Shopping has long since been a method of distraction and relieving stress. Virtual Reality (VR) effectively simulates immersive experiences, including shopping through head-mounted displays (HMD), which create an environment through realistic renderings and sounds. Current studies in VR have shown that assistants can support users by reducing stress, indicating their ability to improve mental health within VR. Customization and personalization have also been used to enhance the user experience with users preferring the tailored experience and leading to a greater sense of immersion. There is a gap in knowledge on the effects of customization on a VR assistant's ability to reduce stress within the VR retailing space. This research aims to identify relationships between customization and shopping assistants within VR to better understand its effects on the user experience. Understanding this will help the development of VR assistants for mental health and consumer-ready VR shopping experiences. © 2024 IEEE.},
keywords = {Customisation, Customizable, generative artificial intelligence, Head-mounted-displays, Helmet mounted displays, Immersive, Mental health, mHealth, Realistic rendering, stress, Stress relief, Users' experiences, Virtual environments, Virtual Reality, Virtual shopping, Virtual shopping assistant},
pubstate = {published},
tppubtype = {inproceedings}
}
Shopping has long since been a method of distraction and relieving stress. Virtual Reality (VR) effectively simulates immersive experiences, including shopping through head-mounted displays (HMD), which create an environment through realistic renderings and sounds. Current studies in VR have shown that assistants can support users by reducing stress, indicating their ability to improve mental health within VR. Customization and personalization have also been used to enhance the user experience with users preferring the tailored experience and leading to a greater sense of immersion. There is a gap in knowledge on the effects of customization on a VR assistant's ability to reduce stress within the VR retailing space. This research aims to identify relationships between customization and shopping assistants within VR to better understand its effects on the user experience. Understanding this will help the development of VR assistants for mental health and consumer-ready VR shopping experiences. © 2024 IEEE.
2023
Marín-Morales, J.; Llanes-Jurado, J.; Minissi, M. E.; Gómez-Zaragozá, L.; Altozano, A.; Alcaniz, M.
Gaze and Head Movement Patterns of Depressive Symptoms During Conversations with Emotional Virtual Humans Proceedings Article
In: Int. Conf. Affect. Comput. Intell. Interact., ACII, Institute of Electrical and Electronics Engineers Inc., 2023, ISBN: 979-835032743-4 (ISBN).
Abstract | Links | BibTeX | Tags: Biomarkers, Clustering, Clusterings, Computational Linguistics, Depressive disorder, Depressive symptom, E-Learning, Emotion elicitation, Eye movements, Gaze movements, K-means clustering, Language Model, Large language model, large language models, Learning systems, Mental health, Multivariant analysis, Signal processing, Statistical learning, virtual human, Virtual humans, Virtual Reality
@inproceedings{marin-morales_gaze_2023,
title = {Gaze and Head Movement Patterns of Depressive Symptoms During Conversations with Emotional Virtual Humans},
author = {J. Marín-Morales and J. Llanes-Jurado and M. E. Minissi and L. Gómez-Zaragozá and A. Altozano and M. Alcaniz},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184656388&doi=10.1109%2fACII59096.2023.10388134&partnerID=40&md5=143cdd8530e17a7b64bdf88f3a0496ab},
doi = {10.1109/ACII59096.2023.10388134},
isbn = {979-835032743-4 (ISBN)},
year = {2023},
date = {2023-01-01},
booktitle = {Int. Conf. Affect. Comput. Intell. Interact., ACII},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Depressive symptoms involve dysfunctional social attitudes and heightened negative emotional states. Identifying biomarkers requires data collection in realistic environments that activate depression-specific phenomena. However, no previous research analysed biomarkers in combination with AI-powered conversational virtual humans (VH) for mental health assessment. This study aims to explore gaze and head movements patterns related to depressive symptoms during conversations with emotional VH. A total of 105 participants were evenly divided into a control group and a group of subjects with depressive symptoms (SDS). They completed six semi-guided conversations designed to evoke basic emotions. The VHs were developed using a cognitive-inspired framework, enabling real-time voice-based conversational interactions powered by a Large Language Model, and including emotional facial expressions and lip synchronization. They have embedded life-history, context, attitudes, emotions and motivations. Signal processing techniques were applied to obtain gaze and head movements features, and heatmaps were generated. Then, parametric and non-parametric statistical tests were applied to evaluate differences between groups. Additionally, a two-dimensional t-SNE embedding was created and combined with k-means clustering. Results indicate that SDS exhibited shorter blinks and longer saccades. The control group showed affiliative lateral head gyros and accelerations, while the SDS demonstrated stress-related back-and-forth movements. SDS also displayed the avoidance of eye contact. The exploratory multivariate statistical unsupervised learning achieved 72.3% accuracy. The present study analyse biomarkers in affective processes with multiple social contextual factors and information modalities in ecological environments, and enhances our understanding of gaze and head movements patterns in individuals with depressive symptoms, ultimately contributing to the development of more effective assessments and intervention strategies. © 2023 IEEE.},
keywords = {Biomarkers, Clustering, Clusterings, Computational Linguistics, Depressive disorder, Depressive symptom, E-Learning, Emotion elicitation, Eye movements, Gaze movements, K-means clustering, Language Model, Large language model, large language models, Learning systems, Mental health, Multivariant analysis, Signal processing, Statistical learning, virtual human, Virtual humans, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
Depressive symptoms involve dysfunctional social attitudes and heightened negative emotional states. Identifying biomarkers requires data collection in realistic environments that activate depression-specific phenomena. However, no previous research analysed biomarkers in combination with AI-powered conversational virtual humans (VH) for mental health assessment. This study aims to explore gaze and head movements patterns related to depressive symptoms during conversations with emotional VH. A total of 105 participants were evenly divided into a control group and a group of subjects with depressive symptoms (SDS). They completed six semi-guided conversations designed to evoke basic emotions. The VHs were developed using a cognitive-inspired framework, enabling real-time voice-based conversational interactions powered by a Large Language Model, and including emotional facial expressions and lip synchronization. They have embedded life-history, context, attitudes, emotions and motivations. Signal processing techniques were applied to obtain gaze and head movements features, and heatmaps were generated. Then, parametric and non-parametric statistical tests were applied to evaluate differences between groups. Additionally, a two-dimensional t-SNE embedding was created and combined with k-means clustering. Results indicate that SDS exhibited shorter blinks and longer saccades. The control group showed affiliative lateral head gyros and accelerations, while the SDS demonstrated stress-related back-and-forth movements. SDS also displayed the avoidance of eye contact. The exploratory multivariate statistical unsupervised learning achieved 72.3% accuracy. The present study analyse biomarkers in affective processes with multiple social contextual factors and information modalities in ecological environments, and enhances our understanding of gaze and head movements patterns in individuals with depressive symptoms, ultimately contributing to the development of more effective assessments and intervention strategies. © 2023 IEEE.