2nd International Workshop on Architecture-Based IoT and Artificial Intelligence to Support Public Health

Mission and objectives

---

IoT architectures play a crucial role in monitoring and supporting public health by enabling innovative solutions to enhance the population’s quality of life. Effective monitoring requires a diverse range of sensors to capture various environmental conditions, including inertial sensor readings, image signals, acoustic signals, and radio frequency signals. However, raw sensor reading requires pre-processing stage and artificial intelligence algorithms to output insights about public health.

These insights can then be communicated to authorities, enabling proactive measures to safeguard public health and improve overall well-being. Furthermore, combining artificial intelligence algorithms with sensor data fusion can significantly enhance system performance and support better decision-making in digital public health. However, machine learning algorithms typically require large datasets to train complex artificial neural network architectures and may produce unexpected inferences, potentially leading to poor decisions. While machine learning algorithms require large datasets and can generate unexpected inferences, explainable artificial intelligence is essential to ensure transparency and trust in these algorithms.

As an intersectional field, biomedical engineering exemplifies the intersection between the Internet of Things architectures and artificial intelligence algorithms applied to the development of biomedical devices that support public health. Examples include wearable devices for monitoring patients’ daily activities and multiparameter health systems that track vital signs while aligning technical performance with clinical safety. Artificial intelligence also contributes to data fusion to forecast trends of diseases.

Considering the numerous challenges associated with the communication systems, Internet of Things, artificial intelligence, explainable artificial intelligence algorithm, strategies based on data fusion to support digital public health, This workshop will promote critical discussion and allow audience to engage in discussions and ask questions to researches and industry experts about scientific contributions and developing new technologies on a global scale to support public health.

Intended audience

---

The workshop is designed for a broad audience, including but not limited to:

• Students and researchers from academia;
• Researchers from research institute;
• Healthcare policymakers;
• Biomedical engineers and clinical research professionals;
• Industry and start-up of technologies and health;
• National and international public health agencies;
• Public health experts, epidemiologists, and clinicians.

Expected outcomes

---

The expected outcome includes, but not limited to:

• Identifying innovations and challenges in technologies that support public health;
• Presenting new reports and scientific contributions on recent technologies and artificial intelligence algorithms for support public health;
• Understanding emerging technologies and artificial intelligence applications in IoT systems;
• Promoting discussion between specialists and audience on challenges and technological advancements.

Format and schedule

---

Duration	Session
11:00 – 11:05	Opening: Dr. Juliane Oliveira (Eldorado Research Institute, Brasil) &Dr. Bruno Dutra (Eldorado Research Institute, Brasil)
11:05 – 11:20	Keynote – Brief Talk: Internet of Things for Healthcare and Accessibility   Prof. Dr. Şefki Kolozali (University of Essex, United Kingdom)
11:20 – 12:08	Presentations: Moderator: Dr. Bruno Dutra  Full papers: 11:20 – 11:32* Safety-Prioritised Multimodal Navigation for Edge Smart Glasses with Constrained On-Device Language Models Şefki Kolozali (University of Essex, United Kingdom) et al. 11:32 – 11:44*  A Survey on Privacy-Preserving Emotion and Attention Recognition for Children with ASD Hudson Bruno (Eldorado Research Institute, Brasil) et al. 11:44 – 11:56* Fine-Tuning YOLO26 for Outer and Inner Cochlear Hair Cell Detection Steven Fernandes (Creighton University United States) et al. Extended Abstract: 11:56 – 12:08*  Architecture-Ready, Explainable ML for Predicting Treatment Adherence Across Subjective Health Experience Segments Rodrigo Ortega-Izquierdo (PredictBy Research and Consulting, Spain) & Valeria Perez-Muto (ibd.) et al.
12:08 – 12:30	Panel discussion Moderator: Dr. Juliane Oliveira  Speakers: – Prof. Dr. Şefki Kolozali – Internet of Things and healthcare (University of Essex, United Kingdom) – Prof. Dr. Jailson Correia – Public Health – Infectious diseases and pediatrics (University of Pernambuco) – Rodrigo Ortega-Izquierdo – Data science and digital society (PredictBy Research and Consulting, Spain) – Valeria Perez-Muto – Health data (PredictBy Research and Consulting, Spain)

*Authors of full papers or extended abstracts have 10 minutes to present their work and 2 minutes to answer questions from the audience. 

Previous Iterations of the Session

---

The first edition of the International Workshop on Architecture-Based IoT and Artificial Intelligence to Support Public Health was held during the 10th International Digital Public Health Conference 2025. The workshop provided an excellent opportunity for networking, engaging in discussions, and gaining insights into digital public services, artificial intelligence, the Internet of Things, and, most importantly, their societal impacts. The paper and abstract session featured three extended abstract presentations, followed by a mini panel discussion with five specialists. More details can be found here.

For this year’s edition, the 11th International Digital Public Health Conference 2026, the organizers
propose the second edition of the International Workshop on Architecture-Based IoT and Artificial
Intelligence to Support Public Health, aiming to foster deeper discussions and insights into emerging technologies and digital public services and show important aspect of development technologies to support public health with full paper and abstract session and mini panel session.

Speakers

---

---

---